Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification.

PubMed

Li, Jinyan; Fong, Simon; Sung, Yunsick; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2016-01-01

An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms

NASA Astrophysics Data System (ADS)

Zhong, Shuya; Pantelous, Athanasios A.; Beer, Michael; Zhou, Jian

2018-05-01

Offshore wind farm is an emerging source of renewable energy, which has been shown to have tremendous potential in recent years. In this blooming area, a key challenge is that the preventive maintenance of offshore turbines should be scheduled reasonably to satisfy the power supply without failure. In this direction, two significant goals should be considered simultaneously as a trade-off. One is to maximise the system reliability and the other is to minimise the maintenance related cost. Thus, a non-linear multi-objective programming model is proposed including two newly defined objectives with thirteen families of constraints suitable for the preventive maintenance of offshore wind farms. In order to solve our model effectively, the nondominated sorting genetic algorithm II, especially for the multi-objective optimisation is utilised and Pareto-optimal solutions of schedules can be obtained to offer adequate support to decision-makers. Finally, an example is given to illustrate the performances of the devised model and algorithm, and explore the relationships of the two targets with the help of a contrast model.

Multi-Objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using Hierarchical Asynchronous Parallel Evolutionary Algorithms

DTIC Science & Technology

2007-09-17

been proposed; these include a combination of variable fidelity models, parallelisation strategies and hybridisation techniques (Coello, Veldhuizen et...Coello et al (Coello, Veldhuizen et al. 2002). 4.4.2 HIERARCHICAL POPULATION TOPOLOGY A hierarchical population topology, when integrated into...to hybrid parallel Multi-Objective Evolutionary Algorithms (pMOEA) (Cantu-Paz 2000; Veldhuizen , Zydallis et al. 2003); it uses a master slave

Treatment planning optimisation in proton therapy

PubMed Central

McGowan, S E; Burnet, N G; Lomax, A J

2013-01-01

ABSTRACT. The goal of radiotherapy is to achieve uniform target coverage while sparing normal tissue. In proton therapy, the same sources of geometric uncertainty are present as in conventional radiotherapy. However, an important and fundamental difference in proton therapy is that protons have a finite range, highly dependent on the electron density of the material they are traversing, resulting in a steep dose gradient at the distal edge of the Bragg peak. Therefore, an accurate knowledge of the sources and magnitudes of the uncertainties affecting the proton range is essential for producing plans which are robust to these uncertainties. This review describes the current knowledge of the geometric uncertainties and discusses their impact on proton dose plans. The need for patient-specific validation is essential and in cases of complex intensity-modulated proton therapy plans the use of a planning target volume (PTV) may fail to ensure coverage of the target. In cases where a PTV cannot be used, other methods of quantifying plan quality have been investigated. A promising option is to incorporate uncertainties directly into the optimisation algorithm. A further development is the inclusion of robustness into a multicriteria optimisation framework, allowing a multi-objective Pareto optimisation function to balance robustness and conformity. The question remains as to whether adaptive therapy can become an integral part of a proton therapy, to allow re-optimisation during the course of a patient's treatment. The challenge of ensuring that plans are robust to range uncertainties in proton therapy remains, although these methods can provide practical solutions. PMID:23255545

Optimisation of logistics processes of energy grass collection

NASA Astrophysics Data System (ADS)

Bányai, Tamás.

2010-05-01

The collection of energy grass is a logistics-intensive process [1]. The optimal design and control of transportation and collection subprocesses is a critical point of the supply chain. To avoid irresponsible decisions by right of experience and intuition, the optimisation and analysis of collection processes based on mathematical models and methods is the scientific suggestible way. Within the frame of this work, the author focuses on the optimisation possibilities of the collection processes, especially from the point of view transportation and related warehousing operations. However the developed optimisation methods in the literature [2] take into account the harvesting processes, county-specific yields, transportation distances, erosion constraints, machinery specifications, and other key variables, but the possibility of more collection points and the multi-level collection were not taken into consideration. The possible areas of using energy grass is very wide (energetically use, biogas and bio alcohol production, paper and textile industry, industrial fibre material, foddering purposes, biological soil protection [3], etc.), so not only a single level but also a multi-level collection system with more collection and production facilities has to be taken into consideration. The input parameters of the optimisation problem are the followings: total amount of energy grass to be harvested in each region; specific facility costs of collection, warehousing and production units; specific costs of transportation resources; pre-scheduling of harvesting process; specific transportation and warehousing costs; pre-scheduling of processing of energy grass at each facility (exclusive warehousing). The model take into consideration the following assumptions: (1) cooperative relation among processing and production facilties, (2) capacity constraints are not ignored, (3) the cost function of transportation is non-linear, (4) the drivers conditions are ignored. The objective function of the optimisation is the maximisation of the profit which means the maximization of the difference between revenue and cost. The objective function trades off the income of the assigned transportation demands against the logistic costs. The constraints are the followings: (1) the free capacity of the assigned transportation resource is more than the re-quested capacity of the transportation demand; the calculated arrival time of the transportation resource to the harvesting place is not later than the requested arrival time of them; (3) the calculated arrival time of the transportation demand to the processing and production facility is not later than the requested arrival time; (4) one transportation demand is assigned to one transportation resource and one resource is assigned to one transportation resource. The decision variable of the optimisation problem is the set of scheduling variables and the assignment of resources to transportation demands. The evaluation parameters of the optimised system are the followings: total costs of the collection process; utilisation of transportation resources and warehouses; efficiency of production and/or processing facilities. However the multidimensional heuristic optimisation method is based on genetic algorithm, but the routing sequence of the optimisation works on the base of an ant colony algorithm. The optimal routes are calculated by the aid of the ant colony algorithm as a subroutine of the global optimisation method and the optimal assignment is given by the genetic algorithm. One important part of the mathematical method is the sensibility analysis of the objective function, which shows the influence rate of the different input parameters. Acknowledgements This research was implemented within the frame of the project entitled "Development and operation of the Technology and Knowledge Transfer Centre of the University of Miskolc". with support by the European Union and co-funding of the European Social Fund. References [1] P. R. Daniel: The Economics of Harvesting and Transporting Corn Stover for Conversion to Fuel Ethanol: A Case Study for Minnesota. University of Minnesota, Department of Applied Economics. 2006. http://ideas.repec.org/p/ags/umaesp/14213.html [2] T. G. Douglas, J. Brendan, D. Erin & V.-D. Becca: Energy and Chemicals from Native Grasses: Production, Transportation and Processing Technologies Considered in the Northern Great Plains. University of Minnesota, Department of Applied Economics. 2006. http://ideas.repec.org/p/ags/umaesp/13838.html [3] Homepage of energygrass. www.energiafu.hu

Multi-objective optimization of radiotherapy: distributed Q-learning and agent-based simulation

NASA Astrophysics Data System (ADS)

Jalalimanesh, Ammar; Haghighi, Hamidreza Shahabi; Ahmadi, Abbas; Hejazian, Hossein; Soltani, Madjid

2017-09-01

Radiotherapy (RT) is among the regular techniques for the treatment of cancerous tumours. Many of cancer patients are treated by this manner. Treatment planning is the most important phase in RT and it plays a key role in therapy quality achievement. As the goal of RT is to irradiate the tumour with adequately high levels of radiation while sparing neighbouring healthy tissues as much as possible, it is a multi-objective problem naturally. In this study, we propose an agent-based model of vascular tumour growth and also effects of RT. Next, we use multi-objective distributed Q-learning algorithm to find Pareto-optimal solutions for calculating RT dynamic dose. We consider multiple objectives and each group of optimizer agents attempt to optimise one of them, iteratively. At the end of each iteration, agents compromise the solutions to shape the Pareto-front of multi-objective problem. We propose a new approach by defining three schemes of treatment planning created based on different combinations of our objectives namely invasive, conservative and moderate. In invasive scheme, we enforce killing cancer cells and pay less attention about irradiation effects on normal cells. In conservative scheme, we take more care of normal cells and try to destroy cancer cells in a less stressed manner. The moderate scheme stands in between. For implementation, each of these schemes is handled by one agent in MDQ-learning algorithm and the Pareto optimal solutions are discovered by the collaboration of agents. By applying this methodology, we could reach Pareto treatment plans through building different scenarios of tumour growth and RT. The proposed multi-objective optimisation algorithm generates robust solutions and finds the best treatment plan for different conditions.

Analysis of optimisation method for a two-stroke piston ring using the Finite Element Method and the Simulated Annealing Method

NASA Astrophysics Data System (ADS)

Kaliszewski, M.; Mazuro, P.

2016-09-01

Simulated Annealing Method of optimisation for the sealing piston ring geometry is tested. The aim of optimisation is to develop ring geometry which would exert demanded pressure on a cylinder just while being bended to fit the cylinder. Method of FEM analysis of an arbitrary piston ring geometry is applied in an ANSYS software. The demanded pressure function (basing on formulae presented by A. Iskra) as well as objective function are introduced. Geometry definition constructed by polynomials in radial coordinate system is delivered and discussed. Possible application of Simulated Annealing Method in a piston ring optimisation task is proposed and visualised. Difficulties leading to possible lack of convergence of optimisation are presented. An example of an unsuccessful optimisation performed in APDL is discussed. Possible line of further optimisation improvement is proposed.

Optimal coordinated voltage control in active distribution networks using backtracking search algorithm

PubMed Central

Tengku Hashim, Tengku Juhana; Mohamed, Azah

2017-01-01

The growing interest in distributed generation (DG) in recent years has led to a number of generators connected to a distribution system. The integration of DGs in a distribution system has resulted in a network known as active distribution network due to the existence of bidirectional power flow in the system. Voltage rise issue is one of the predominantly important technical issues to be addressed when DGs exist in an active distribution network. This paper presents the application of the backtracking search algorithm (BSA), which is relatively new optimisation technique to determine the optimal settings of coordinated voltage control in a distribution system. The coordinated voltage control considers power factor, on-load tap-changer and generation curtailment control to manage voltage rise issue. A multi-objective function is formulated to minimise total losses and voltage deviation in a distribution system. The proposed BSA is compared with that of particle swarm optimisation (PSO) so as to evaluate its effectiveness in determining the optimal settings of power factor, tap-changer and percentage active power generation to be curtailed. The load flow algorithm from MATPOWER is integrated in the MATLAB environment to solve the multi-objective optimisation problem. Both the BSA and PSO optimisation techniques have been tested on a radial 13-bus distribution system and the results show that the BSA performs better than PSO by providing better fitness value and convergence rate. PMID:28991919

Optimal coordinated voltage control in active distribution networks using backtracking search algorithm.

PubMed

Tengku Hashim, Tengku Juhana; Mohamed, Azah

2017-01-01

The growing interest in distributed generation (DG) in recent years has led to a number of generators connected to a distribution system. The integration of DGs in a distribution system has resulted in a network known as active distribution network due to the existence of bidirectional power flow in the system. Voltage rise issue is one of the predominantly important technical issues to be addressed when DGs exist in an active distribution network. This paper presents the application of the backtracking search algorithm (BSA), which is relatively new optimisation technique to determine the optimal settings of coordinated voltage control in a distribution system. The coordinated voltage control considers power factor, on-load tap-changer and generation curtailment control to manage voltage rise issue. A multi-objective function is formulated to minimise total losses and voltage deviation in a distribution system. The proposed BSA is compared with that of particle swarm optimisation (PSO) so as to evaluate its effectiveness in determining the optimal settings of power factor, tap-changer and percentage active power generation to be curtailed. The load flow algorithm from MATPOWER is integrated in the MATLAB environment to solve the multi-objective optimisation problem. Both the BSA and PSO optimisation techniques have been tested on a radial 13-bus distribution system and the results show that the BSA performs better than PSO by providing better fitness value and convergence rate.

SLA-based optimisation of virtualised resource for multi-tier web applications in cloud data centres

NASA Astrophysics Data System (ADS)

Bi, Jing; Yuan, Haitao; Tie, Ming; Tan, Wei

2015-10-01

Dynamic virtualised resource allocation is the key to quality of service assurance for multi-tier web application services in cloud data centre. In this paper, we develop a self-management architecture of cloud data centres with virtualisation mechanism for multi-tier web application services. Based on this architecture, we establish a flexible hybrid queueing model to determine the amount of virtual machines for each tier of virtualised application service environments. Besides, we propose a non-linear constrained optimisation problem with restrictions defined in service level agreement. Furthermore, we develop a heuristic mixed optimisation algorithm to maximise the profit of cloud infrastructure providers, and to meet performance requirements from different clients as well. Finally, we compare the effectiveness of our dynamic allocation strategy with two other allocation strategies. The simulation results show that the proposed resource allocation method is efficient in improving the overall performance and reducing the resource energy cost.

A radiobiology-based inverse treatment planning method for optimisation of permanent l-125 prostate implants in focal brachytherapy

NASA Astrophysics Data System (ADS)

Haworth, Annette; Mears, Christopher; Betts, John M.; Reynolds, Hayley M.; Tack, Guido; Leo, Kevin; Williams, Scott; Ebert, Martin A.

2016-01-01

Treatment plans for ten patients, initially treated with a conventional approach to low dose-rate brachytherapy (LDR, 145 Gy to entire prostate), were compared with plans for the same patients created with an inverse-optimisation planning process utilising a biologically-based objective. The ‘biological optimisation’ considered a non-uniform distribution of tumour cell density through the prostate based on known and expected locations of the tumour. Using dose planning-objectives derived from our previous biological-model validation study, the volume of the urethra receiving 125% of the conventional prescription (145 Gy) was reduced from a median value of 64% to less than 8% whilst maintaining high values of TCP. On average, the number of planned seeds was reduced from 85 to less than 75. The robustness of plans to random seed displacements needs to be carefully considered when using contemporary seed placement techniques. We conclude that an inverse planning approach to LDR treatments, based on a biological objective, has the potential to maintain high rates of tumour control whilst minimising dose to healthy tissue. In future, the radiobiological model will be informed using multi-parametric MRI to provide a personalised medicine approach.

Vertical transportation systems embedded on shuffled frog leaping algorithm for manufacturing optimisation problems in industries.

PubMed

Aungkulanon, Pasura; Luangpaiboon, Pongchanun

2016-01-01

Response surface methods via the first or second order models are important in manufacturing processes. This study, however, proposes different structured mechanisms of the vertical transportation systems or VTS embedded on a shuffled frog leaping-based approach. There are three VTS scenarios, a motion reaching a normal operating velocity, and both reaching and not reaching transitional motion. These variants were performed to simultaneously inspect multiple responses affected by machining parameters in multi-pass turning processes. The numerical results of two machining optimisation problems demonstrated the high performance measures of the proposed methods, when compared to other optimisation algorithms for an actual deep cut design.

A supportive architecture for CFD-based design optimisation

NASA Astrophysics Data System (ADS)

Li, Ni; Su, Zeya; Bi, Zhuming; Tian, Chao; Ren, Zhiming; Gong, Guanghong

2014-03-01

Multi-disciplinary design optimisation (MDO) is one of critical methodologies to the implementation of enterprise systems (ES). MDO requiring the analysis of fluid dynamics raises a special challenge due to its extremely intensive computation. The rapid development of computational fluid dynamic (CFD) technique has caused a rise of its applications in various fields. Especially for the exterior designs of vehicles, CFD has become one of the three main design tools comparable to analytical approaches and wind tunnel experiments. CFD-based design optimisation is an effective way to achieve the desired performance under the given constraints. However, due to the complexity of CFD, integrating with CFD analysis in an intelligent optimisation algorithm is not straightforward. It is a challenge to solve a CFD-based design problem, which is usually with high dimensions, and multiple objectives and constraints. It is desirable to have an integrated architecture for CFD-based design optimisation. However, our review on existing works has found that very few researchers have studied on the assistive tools to facilitate CFD-based design optimisation. In the paper, a multi-layer architecture and a general procedure are proposed to integrate different CFD toolsets with intelligent optimisation algorithms, parallel computing technique and other techniques for efficient computation. In the proposed architecture, the integration is performed either at the code level or data level to fully utilise the capabilities of different assistive tools. Two intelligent algorithms are developed and embedded with parallel computing. These algorithms, together with the supportive architecture, lay a solid foundation for various applications of CFD-based design optimisation. To illustrate the effectiveness of the proposed architecture and algorithms, the case studies on aerodynamic shape design of a hypersonic cruising vehicle are provided, and the result has shown that the proposed architecture and developed algorithms have performed successfully and efficiently in dealing with the design optimisation with over 200 design variables.

Prediction of multi performance characteristics of wire EDM process using grey ANFIS

NASA Astrophysics Data System (ADS)

Kumanan, Somasundaram; Nair, Anish

2017-09-01

Super alloys are used to fabricate components in ultra-supercritical power plants. These hard to machine materials are processed using non-traditional machining methods like Wire cut electrical discharge machining and needs attention. This paper details about multi performance optimization of wire EDM process using Grey ANFIS. Experiments are designed to establish the performance characteristics of wire EDM such as surface roughness, material removal rate, wire wear rate and geometric tolerances. The control parameters are pulse on time, pulse off time, current, voltage, flushing pressure, wire tension, table feed and wire speed. Grey relational analysis is employed to optimise the multi objectives. Analysis of variance of the grey grades is used to identify the critical parameters. A regression model is developed and used to generate datasets for the training of proposed adaptive neuro fuzzy inference system. The developed prediction model is tested for its prediction ability.

Optimal integrated management of groundwater resources and irrigated agriculture in arid coastal regions

NASA Astrophysics Data System (ADS)

Grundmann, J.; Schütze, N.; Heck, V.

2014-09-01

Groundwater systems in arid coastal regions are particularly at risk due to limited potential for groundwater replenishment and increasing water demand, caused by a continuously growing population. For ensuring a sustainable management of those regions, we developed a new simulation-based integrated water management system. The management system unites process modelling with artificial intelligence tools and evolutionary optimisation techniques for managing both water quality and water quantity of a strongly coupled groundwater-agriculture system. Due to the large number of decision variables, a decomposition approach is applied to separate the original large optimisation problem into smaller, independent optimisation problems which finally allow for faster and more reliable solutions. It consists of an analytical inner optimisation loop to achieve a most profitable agricultural production for a given amount of water and an outer simulation-based optimisation loop to find the optimal groundwater abstraction pattern. Thereby, the behaviour of farms is described by crop-water-production functions and the aquifer response, including the seawater interface, is simulated by an artificial neural network. The methodology is applied exemplarily for the south Batinah re-gion/Oman, which is affected by saltwater intrusion into a coastal aquifer system due to excessive groundwater withdrawal for irrigated agriculture. Due to contradicting objectives like profit-oriented agriculture vs aquifer sustainability, a multi-objective optimisation is performed which can provide sustainable solutions for water and agricultural management over long-term periods at farm and regional scales in respect of water resources, environment, and socio-economic development.

Optimisation of Ferrochrome Addition Using Multi-Objective Evolutionary and Genetic Algorithms for Stainless Steel Making via AOD Converter

NASA Astrophysics Data System (ADS)

Behera, Kishore Kumar; Pal, Snehanshu

2018-03-01

This paper describes a new approach towards optimum utilisation of ferrochrome added during stainless steel making in AOD converter. The objective of optimisation is to enhance end blow chromium content of steel and reduce the ferrochrome addition during refining. By developing a thermodynamic based mathematical model, a study has been conducted to compute the optimum trade-off between ferrochrome addition and end blow chromium content of stainless steel using a predator prey genetic algorithm through training of 100 dataset considering different input and output variables such as oxygen, argon, nitrogen blowing rate, duration of blowing, initial bath temperature, chromium and carbon content, weight of ferrochrome added during refining. Optimisation is performed within constrained imposed on the input parameters whose values fall within certain ranges. The analysis of pareto fronts is observed to generate a set of feasible optimal solution between the two conflicting objectives that provides an effective guideline for better ferrochrome utilisation. It is found out that after a certain critical range, further addition of ferrochrome does not affect the chromium percentage of steel. Single variable response analysis is performed to study the variation and interaction of all individual input parameters on output variables.

The faint intergalactic-medium red-shifted emission balloon: future UV observations with EMCCDs

NASA Astrophysics Data System (ADS)

Kyne, Gillian; Hamden, Erika T.; Lingner, Nicole; Morrissey, Patrick; Nikzad, Shouleh; Martin, D. Christopher

2016-08-01

We present the latest developments in our joint NASA/CNES suborbital project. This project is a balloon-borne UV multi-object spectrograph, which has been designed to detect faint emission from the circumgalactic medium (CGM) around low redshift galaxies. One major change from FIREBall-1 has been the use of a delta-doped Electron Multiplying CCD (EMCCD). EMCCDs can be used in photon-counting (PC) mode to achieve extremely low readout noise (¡ 1e-). Our testing initially focused on reducing clock-induced-charge (CIC) through wave shaping and well depth optimisation with the CCD Controller for Counting Photons (CCCP) from Nüvü. This optimisation also includes methods for reducing dark current, via cooling and substrate voltage adjustment. We present result of laboratory noise measurements including dark current. Furthermore, we will briefly present some initial results from our first set of on-sky observations using a delta-doped EMCCD on the 200 inch telescope at Palomar using the Palomar Cosmic Web Imager (PCWI).

Optimisation of driver actions in RWD race car including tyre thermodynamics

NASA Astrophysics Data System (ADS)

Maniowski, Michal

2016-04-01

The paper presents an innovative method for a lap time minimisation by using genetic algorithms for a multi objective optimisation of a race driver-vehicle model. The decision variables consist of 16 parameters responsible for actions of a professional driver (e.g. time traces for brake, accelerator and steering wheel) on a race track part with RH corner. Purpose-built, high fidelity, multibody vehicle model (called 'miMa') is described by 30 generalised coordinates and 440 parameters, crucial in motorsport. Focus is put on modelling of the tyre tread thermodynamics and its influence on race vehicle dynamics. Numerical example considers a Rear Wheel Drive BMW E36 prepared for track day events. In order to improve the section lap time (by 5%) and corner exit velocity (by 4%) a few different driving strategies are found depending on thermal conditions of semi-slick tyres. The process of the race driver adaptation to initially cold or hot tyres is explained.

Multi-tasking arbitration and behaviour design for human-interactive robots

NASA Astrophysics Data System (ADS)

Kobayashi, Yuichi; Onishi, Masaki; Hosoe, Shigeyuki; Luo, Zhiwei

2013-05-01

Robots that interact with humans in household environments are required to handle multiple real-time tasks simultaneously, such as carrying objects, collision avoidance and conversation with human. This article presents a design framework for the control and recognition processes to meet these requirements taking into account stochastic human behaviour. The proposed design method first introduces a Petri net for synchronisation of multiple tasks. The Petri net formulation is converted to Markov decision processes and processed in an optimal control framework. Three tasks (safety confirmation, object conveyance and conversation) interact and are expressed by the Petri net. Using the proposed framework, tasks that normally tend to be designed by integrating many if-then rules can be designed in a systematic manner in a state estimation and optimisation framework from the viewpoint of the shortest time optimal control. The proposed arbitration method was verified by simulations and experiments using RI-MAN, which was developed for interactive tasks with humans.

Extended behavioural modelling of FET and lattice-mismatched HEMT devices

NASA Astrophysics Data System (ADS)

Khawam, Yahya; Albasha, Lutfi

2017-07-01

This study presents an improved large signal model that can be used for high electron mobility transistors (HEMTs) and field effect transistors using measurement-based behavioural modelling techniques. The steps for accurate large and small signal modelling for transistor are also discussed. The proposed DC model is based on the Fager model since it compensates between the number of model's parameters and accuracy. The objective is to increase the accuracy of the drain-source current model with respect to any change in gate or drain voltages. Also, the objective is to extend the improved DC model to account for soft breakdown and kink effect found in some variants of HEMT devices. A hybrid Newton's-Genetic algorithm is used in order to determine the unknown parameters in the developed model. In addition to accurate modelling of a transistor's DC characteristics, the complete large signal model is modelled using multi-bias s-parameter measurements. The way that the complete model is performed is by using a hybrid multi-objective optimisation technique (Non-dominated Sorting Genetic Algorithm II) and local minimum search (multivariable Newton's method) for parasitic elements extraction. Finally, the results of DC modelling and multi-bias s-parameters modelling are presented, and three-device modelling recommendations are discussed.

An integrated and dynamic optimisation model for the multi-level emergency logistics network in anti-bioterrorism system

NASA Astrophysics Data System (ADS)

Liu, Ming; Zhao, Lindu

2012-08-01

Demand for emergency resources is usually uncertain and varies quickly in anti-bioterrorism system. Besides, emergency resources which had been allocated to the epidemic areas in the early rescue cycle will affect the demand later. In this article, an integrated and dynamic optimisation model with time-varying demand based on the epidemic diffusion rule is constructed. The heuristic algorithm coupled with the MATLAB mathematical programming solver is adopted to solve the optimisation model. In what follows, the application of the optimisation model as well as a short sensitivity analysis of the key parameters in the time-varying demand forecast model is presented. The results show that both the model and the solution algorithm are useful in practice, and both objectives of inventory level and emergency rescue cost can be controlled effectively. Thus, it can provide some guidelines for decision makers when coping with emergency rescue problem with uncertain demand, and offers an excellent reference when issues pertain to bioterrorism.

The use of surrogates for an optimal management of coupled groundwater-agriculture hydrosystems

NASA Astrophysics Data System (ADS)

Grundmann, J.; Schütze, N.; Brettschneider, M.; Schmitz, G. H.; Lennartz, F.

2012-04-01

For ensuring an optimal sustainable water resources management in arid coastal environments, we develop a new simulation based integrated water management system. It aims at achieving best possible solutions for groundwater withdrawals for agricultural and municipal water use including saline water management together with a substantial increase of the water use efficiency in irrigated agriculture. To achieve a robust and fast operation of the management system regarding water quality and water quantity we develop appropriate surrogate models by combining physically based process modelling with methods of artificial intelligence. Thereby we use an artificial neural network for modelling the aquifer response, inclusive the seawater interface, which was trained on a scenario database generated by a numerical density depended groundwater flow model. For simulating the behaviour of high productive agricultural farms crop water production functions are generated by means of soil-vegetation-atmosphere-transport (SVAT)-models, adapted to the regional climate conditions, and a novel evolutionary optimisation algorithm for optimal irrigation scheduling and control. We apply both surrogates exemplarily within a simulation based optimisation environment using the characteristics of the south Batinah region in the Sultanate of Oman which is affected by saltwater intrusion into the coastal aquifer due to excessive groundwater withdrawal for irrigated agriculture. We demonstrate the effectiveness of our methodology for the evaluation and optimisation of different irrigation practices, cropping pattern and resulting abstraction scenarios. Due to contradicting objectives like profit-oriented agriculture vs. aquifer sustainability a multi-criterial optimisation is performed.

Optimisation of insect cell growth in deep-well blocks: development of a high-throughput insect cell expression screen.

PubMed

Bahia, Daljit; Cheung, Robert; Buchs, Mirjam; Geisse, Sabine; Hunt, Ian

2005-01-01

This report describes a method to culture insects cells in 24 deep-well blocks for the routine small-scale optimisation of baculovirus-mediated protein expression experiments. Miniaturisation of this process provides the necessary reduction in terms of resource allocation, reagents, and labour to allow extensive and rapid optimisation of expression conditions, with the concomitant reduction in lead-time before commencement of large-scale bioreactor experiments. This therefore greatly simplifies the optimisation process and allows the use of liquid handling robotics in much of the initial optimisation stages of the process, thereby greatly increasing the throughput of the laboratory. We present several examples of the use of deep-well block expression studies in the optimisation of therapeutically relevant protein targets. We also discuss how the enhanced throughput offered by this approach can be adapted to robotic handling systems and the implications this has on the capacity to conduct multi-parallel protein expression studies.

An Optimised System for Generating Multi-Resolution Dtms Using NASA Mro Datasets

NASA Astrophysics Data System (ADS)

Tao, Y.; Muller, J.-P.; Sidiropoulos, P.; Veitch-Michaelis, J.; Yershov, V.

2016-06-01

Within the EU FP-7 iMars project, a fully automated multi-resolution DTM processing chain, called Co-registration ASP-Gotcha Optimised (CASP-GO) has been developed, based on the open source NASA Ames Stereo Pipeline (ASP). CASP-GO includes tiepoint based multi-resolution image co-registration and an adaptive least squares correlation-based sub-pixel refinement method called Gotcha. The implemented system guarantees global geo-referencing compliance with respect to HRSC (and thence to MOLA), provides refined stereo matching completeness and accuracy based on the ASP normalised cross-correlation. We summarise issues discovered from experimenting with the use of the open-source ASP DTM processing chain and introduce our new working solutions. These issues include global co-registration accuracy, de-noising, dealing with failure in matching, matching confidence estimation, outlier definition and rejection scheme, various DTM artefacts, uncertainty estimation, and quality-efficiency trade-offs.

Prior knowledge guided active modules identification: an integrated multi-objective approach.

PubMed

Chen, Weiqi; Liu, Jing; He, Shan

2017-03-14

Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.

AlphaMate: a program for optimising selection, maintenance of diversity, and mate allocation in breeding programs.

PubMed

Gorjanc, Gregor; Hickey, John M

2018-05-02

AlphaMate is a flexible program that optimises selection, maintenance of genetic diversity, and mate allocation in breeding programs. It can be used in animal and cross- and self-pollinating plant populations. These populations can be subject to selective breeding or conservation management. The problem is formulated as a multi-objective optimisation of a valid mating plan that is solved with an evolutionary algorithm. A valid mating plan is defined by a combination of mating constraints (the number of matings, the maximal number of parents, the minimal/equal/maximal number of contributions per parent, or allowance for selfing) that are gender specific or generic. The optimisation can maximize genetic gain, minimize group coancestry, minimize inbreeding of individual matings, or maximize genetic gain for a given increase in group coancestry or inbreeding. Users provide a list of candidate individuals with associated gender and selection criteria information (if applicable) and coancestry matrix. Selection criteria and coancestry matrix can be based on pedigree or genome-wide markers. Additional individual or mating specific information can be included to enrich optimisation objectives. An example of rapid recurrent genomic selection in wheat demonstrates how AlphaMate can double the efficiency of converting genetic diversity into genetic gain compared to truncation selection. Another example demonstrates the use of genome editing to expand the gain-diversity frontier. Executable versions of AlphaMate for Windows, Mac, and Linux platforms are available at http://www.AlphaGenes.roslin.ed.ac.uk/AlphaMate. gregor.gorjanc@roslin.ed.ack.uk.

A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

NASA Astrophysics Data System (ADS)

Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun

2014-11-01

In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms

NASA Astrophysics Data System (ADS)

Wang, Rui; Mansor, Maszatul M.; Purshouse, Robin C.; Fleming, Peter J.

2015-10-01

Many-objective optimisation problems remain challenging for many state-of-the-art multi-objective evolutionary algorithms. Preference-inspired co-evolutionary algorithms (PICEAs) which co-evolve the usual population of candidate solutions with a family of decision-maker preferences during the search have been demonstrated to be effective on such problems. However, it is unknown whether PICEAs are robust with respect to the parameter settings. This study aims to address this question. First, a global sensitivity analysis method - the Sobol' variance decomposition method - is employed to determine the relative importance of the parameters controlling the performance of PICEAs. Experimental results show that the performance of PICEAs is controlled for the most part by the number of function evaluations. Next, we investigate the effect of key parameters identified from the Sobol' test and the genetic operators employed in PICEAs. Experimental results show improved performance of the PICEAs as more preferences are co-evolved. Additionally, some suggestions for genetic operator settings are provided for non-expert users.

Man vs. Machine: An interactive poll to evaluate hydrological model performance of a manual and an automatic calibration

NASA Astrophysics Data System (ADS)

Wesemann, Johannes; Burgholzer, Reinhard; Herrnegger, Mathew; Schulz, Karsten

2017-04-01

In recent years, a lot of research in hydrological modelling has been invested to improve the automatic calibration of rainfall-runoff models. This includes for example (1) the implementation of new optimisation methods, (2) the incorporation of new and different objective criteria and signatures in the optimisation and (3) the usage of auxiliary data sets apart from runoff. Nevertheless, in many applications manual calibration is still justifiable and frequently applied. The hydrologist performing the manual calibration, with his expert knowledge, is able to judge the hydrographs simultaneously concerning details but also in a holistic view. This integrated eye-ball verification procedure available to man can be difficult to formulate in objective criteria, even when using a multi-criteria approach. Comparing the results of automatic and manual calibration is not straightforward. Automatic calibration often solely involves objective criteria such as Nash-Sutcliffe Efficiency Coefficient or the Kling-Gupta-Efficiency as a benchmark during the calibration. Consequently, a comparison based on such measures is intrinsically biased towards automatic calibration. Additionally, objective criteria do not cover all aspects of a hydrograph leaving questions concerning the quality of a simulation open. This contribution therefore seeks to examine the quality of manually and automatically calibrated hydrographs by interactively involving expert knowledge in the evaluation. Simulations have been performed for the Mur catchment in Austria with the rainfall-runoff model COSERO using two parameter sets evolved from a manual and an automatic calibration. A subset of resulting hydrographs for observation and simulation, representing the typical flow conditions and events, will be evaluated in this study. In an interactive crowdsourcing approach experts attending the session can vote for their preferred simulated hydrograph without having information on the calibration method that produced the respective hydrograph. Therefore, the result of the poll can be seen as an additional quality criterion for the comparison of the two different approaches and help in the evaluation of the automatic calibration method.

On the Performance of Alternate Conceptual Ecohydrological Models for Streamflow Prediction

NASA Astrophysics Data System (ADS)

Naseem, Bushra; Ajami, Hoori; Cordery, Ian; Sharma, Ashish

2016-04-01

A merging of a lumped conceptual hydrological model with two conceptual dynamic vegetation models is presented to assess the performance of these models for simultaneous simulations of streamflow and leaf area index (LAI). Two conceptual dynamic vegetation models with differing representation of ecological processes are merged with a lumped conceptual hydrological model (HYMOD) to predict catchment scale streamflow and LAI. The merged RR-LAI-I model computes relative leaf biomass based on transpiration rates while the RR-LAI-II model computes above ground green and dead biomass based on net primary productivity and water use efficiency in response to soil moisture dynamics. To assess the performance of these models, daily discharge and 8-day MODIS LAI product for 27 catchments of 90 - 1600km2 in size located in the Murray - Darling Basin in Australia are used. Our results illustrate that when single-objective optimisation was focussed on maximizing the objective function for streamflow or LAI, the other un-calibrated predicted outcome (LAI if streamflow is the focus) was consistently compromised. Thus, single-objective optimization cannot take into account the essence of all processes in the conceptual ecohydrological models. However, multi-objective optimisation showed great strength for streamflow and LAI predictions. Both response outputs were better simulated by RR-LAI-II than RR-LAI-I due to better representation of physical processes such as net primary productivity (NPP) in RR-LAI-II. Our results highlight that simultaneous calibration of streamflow and LAI using a multi-objective algorithm proves to be an attractive tool for improved streamflow predictions.

Experimental research and numerical optimisation of multi-point sheet metal forming implementation using a solid elastic cushion system

NASA Astrophysics Data System (ADS)

Tolipov, A. A.; Elghawail, A.; Shushing, S.; Pham, D.; Essa, K.

2017-09-01

There is a growing demand for flexible manufacturing techniques that meet the rapid changes in customer needs. A finite element analysis numerical optimisation technique was used to optimise the multi-point sheet forming process. Multi-point forming (MPF) is a flexible sheet metal forming technique where the same tool can be readily changed to produce different parts. The process suffers from some geometrical defects such as wrinkling and dimpling, which have been found to be the cause of the major surface quality problems. This study investigated the influence of parameters such as the elastic cushion hardness, blank holder force, coefficient of friction, cushion thickness and radius of curvature, on the quality of parts formed in a flexible multi-point stamping die. For those reasons, in this investigation, a multipoint forming stamping process using a blank holder was carried out in order to study the effects of the wrinkling, dimpling, thickness variation and forming force. The aim was to determine the optimum values of these parameters. Finite element modelling (FEM) was employed to simulate the multi-point forming of hemispherical shapes. Using the response surface method, the effects of process parameters on wrinkling, maximum deviation from the target shape and thickness variation were investigated. The results show that elastic cushion with proper thickness and polyurethane with the hardness of Shore A90. It has also been found that the application of lubrication cans improve the shape accuracy of the formed workpiece. These final results were compared with the numerical simulation results of the multi-point forming for hemispherical shapes using a blank-holder and it was found that using cushion hardness realistic to reduce wrinkling and maximum deviation.

Multi-response optimisation of ultrasound-assisted extraction for recovery of flavonoids from red grape skins using response surface methodology.

PubMed

Tomaz, Ivana; Maslov, Luna; Stupić, Domagoj; Preiner, Darko; Ašperger, Danijela; Karoglan Kontić, Jasminka

2016-01-01

For the characterisation of grape cultivars, the profile and content of flavonoids are important because these compounds impact grape and wine quality. To determine the correct profile and content of flavonoids, the use of robust, sensitive and reliable methods is necessary. The object of this research is to develop a new ultrasound-assisted extraction (UAE) method for the recovery of flavonoids from grape skins using response surface methodology. Optimisation of UAE was performed using a complementary study combining a Box-Behnken experimental design with qualitative analysis by high-performance liquid chromatography. Optimal extraction conditions were obtained using the extraction solvent composed of acetonitrile:water:formic acid (26:73:1, v/v/v) at an extraction temperature of 50 °C, an extraction time of 15 min in a single-extraction step and with a solid-to-solvent ratio of 1:80 g/mL. The calculated relative standard deviations for the optimal extraction method were very low, measuring less than 5%. This study demonstrates that numerous factors have strong effects on the extraction efficiency, including the type of organic modifier and its percentage in the extraction solvent, the number of extraction steps, the solid-to-solvent ratio, the extraction time and temperature and, finally, the particular nature of analyte and their position within the grape skin cell. Copyright © 2015 John Wiley & Sons, Ltd.

Is an objective refraction optimised using the visual Strehl ratio better than a subjective refraction?

PubMed Central

Hastings, Gareth D.; Marsack, Jason D.; Nguyen, Lan Chi; Cheng, Han; Applegate, Raymond A.

2017-01-01

Purpose To prospectively examine whether using the visual image quality metric, visual Strehl (VSX), to optimise objective refraction from wavefront error measurements can provide equivalent or better visual performance than subjective refraction and which refraction is preferred in free viewing. Methods Subjective refractions and wavefront aberrations were measured on 40 visually-normal eyes of 20 subjects, through natural and dilated pupils. For each eye a sphere, cylinder, and axis prescription was also objectively determined that optimised visual image quality (VSX) for the measured wavefront error. High contrast (HC) and low contrast (LC) logMAR visual acuity (VA) and short-term monocular distance vision preference were recorded and compared between the VSX-objective and subjective prescriptions both undilated and dilated. Results For 36 myopic eyes, clinically equivalent (and not statistically different) HC VA was provided with both the objective and subjective refractions (undilated mean ±SD was −0.06 ±0.04 with both refractions; dilated was −0.05 ±0.04 with the objective, and −0.05 ±0.05 with the subjective refraction). LC logMAR VA provided by the objective refraction was also clinically equivalent and not statistically different to that provided by the subjective refraction through both natural and dilated pupils for myopic eyes. In free viewing the objective prescription was preferred over the subjective by 72% of myopic eyes when not dilated. For four habitually undercorrected high hyperopic eyes, the VSX-objective refraction was more positive in spherical power and VA poorer than with the subjective refraction. Conclusions A method of simultaneously optimising sphere, cylinder, and axis from wavefront error measurements, using the visual image quality metric VSX, is described. In myopic subjects, visual performance, as measured by HC and LC VA, with this VSX-objective refraction was found equivalent to that provided by subjective refraction, and was typically preferred over subjective refraction. Subjective refraction was preferred by habitually undercorrected hyperopic eyes. PMID:28370389

A reliability-based maintenance technicians' workloads optimisation model with stochastic consideration

NASA Astrophysics Data System (ADS)

Ighravwe, D. E.; Oke, S. A.; Adebiyi, K. A.

2016-06-01

The growing interest in technicians' workloads research is probably associated with the recent surge in competition. This was prompted by unprecedented technological development that triggers changes in customer tastes and preferences for industrial goods. In a quest for business improvement, this worldwide intense competition in industries has stimulated theories and practical frameworks that seek to optimise performance in workplaces. In line with this drive, the present paper proposes an optimisation model which considers technicians' reliability that complements factory information obtained. The information used emerged from technicians' productivity and earned-values using the concept of multi-objective modelling approach. Since technicians are expected to carry out routine and stochastic maintenance work, we consider these workloads as constraints. The influence of training, fatigue and experiential knowledge of technicians on workload management was considered. These workloads were combined with maintenance policy in optimising reliability, productivity and earned-values using the goal programming approach. Practical datasets were utilised in studying the applicability of the proposed model in practice. It was observed that our model was able to generate information that practicing maintenance engineers can apply in making more informed decisions on technicians' management.

Green supplier selection: a new genetic/immune strategy with industrial application

NASA Astrophysics Data System (ADS)

Kumar, Amit; Jain, Vipul; Kumar, Sameer; Chandra, Charu

2016-10-01

With the onset of the 'climate change movement', organisations are striving to include environmental criteria into the supplier selection process. This article hybridises a Green Data Envelopment Analysis (GDEA)-based approach with a new Genetic/Immune Strategy for Data Envelopment Analysis (GIS-DEA). A GIS-DEA approach provides a different view to solving multi-criteria decision making problems using data envelopment analysis (DEA) by considering DEA as a multi-objective optimisation problem with efficiency as one objective and proximity of solution to decision makers' preferences as the other objective. The hybrid approach called GIS-GDEA is applied here to a well-known automobile spare parts manufacturer in India and the results presented. User validation developed based on specific set of criteria suggests that the supplier selection process with GIS-GDEA is more practical than other approaches in a current industrial scenario with multiple decision makers.

Implementation of the multi-channel monolith reactor in an optimisation procedure for heterogeneous oxidation catalysts based on genetic algorithms.

PubMed

Breuer, Christian; Lucas, Martin; Schütze, Frank-Walter; Claus, Peter

2007-01-01

A multi-criteria optimisation procedure based on genetic algorithms is carried out in search of advanced heterogeneous catalysts for total oxidation. Simple but flexible software routines have been created to be applied within a search space of more then 150,000 individuals. The general catalyst design includes mono-, bi- and trimetallic compositions assembled out of 49 different metals and depleted on an Al2O3 support in up to nine amount levels. As an efficient tool for high-throughput screening and perfectly matched to the requirements of heterogeneous gas phase catalysis - especially for applications technically run in honeycomb structures - the multi-channel monolith reactor is implemented to evaluate the catalyst performances. Out of a multi-component feed-gas, the conversion rates of carbon monoxide (CO) and a model hydrocarbon (HC) are monitored in parallel. In combination with further restrictions to preparation and pre-treatment a primary screening can be conducted, promising to provide results close to technically applied catalysts. Presented are the resulting performances of the optimisation process for the first catalyst generations and the prospect of its auto-adaptation to specified optimisation goals.

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Montazeri, A.; West, C.; Monk, S. D.; Taylor, C. J.

2017-04-01

This paper concerns the problem of dynamic modelling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model-orientated research using the same machine, the paper develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimise the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivisation of an output error single-performance index. The developed algorithm utilises a multi-objective genetic algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivisation has improved convergence of the estimated parameters compared to the single-objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

An analytical fuzzy-based approach to ?-gain optimal control of input-affine nonlinear systems using Newton-type algorithm

NASA Astrophysics Data System (ADS)

Milic, Vladimir; Kasac, Josip; Novakovic, Branko

2015-10-01

This paper is concerned with ?-gain optimisation of input-affine nonlinear systems controlled by analytic fuzzy logic system. Unlike the conventional fuzzy-based strategies, the non-conventional analytic fuzzy control method does not require an explicit fuzzy rule base. As the first contribution of this paper, we prove, by using the Stone-Weierstrass theorem, that the proposed fuzzy system without rule base is universal approximator. The second contribution of this paper is an algorithm for solving a finite-horizon minimax problem for ?-gain optimisation. The proposed algorithm consists of recursive chain rule for first- and second-order derivatives, Newton's method, multi-step Adams method and automatic differentiation. Finally, the results of this paper are evaluated on a second-order nonlinear system.

Investigating the Trade-Off Between Power Generation and Environmental Impact of Tidal-Turbine Arrays Using Array Layout Optimisation and Habitat Sustainability Modelling.

NASA Astrophysics Data System (ADS)

du Feu, R. J.; Funke, S. W.; Kramer, S. C.; Hill, J.; Piggott, M. D.

2016-12-01

The installation of tidal turbines into the ocean will inevitably affect the environment around them. However, due to the relative infancy of this sector the extent and severity of such effects is unknown. The layout of an array of turbines is an important factor in determining not only the array's final yield but also how it will influence regional hydrodynamics. This in turn could affect, for example, sediment transportation or habitat suitability. The two potentially competing objectives of extracting energy from the tidal current, and of limiting any environmental impact consequent to influencing that current, are investigated here. This relationship is posed as a multi-objective optimisation problem. OpenTidalFarm, an array layout optimisation tool, and MaxEnt, habitat sustainability modelling software, are used to evaluate scenarios off the coast of the UK. MaxEnt is used to estimate the likelihood of finding a species in a given location based upon environmental input data and presence data of the species. Environmental features which are known to impact habitat, specifically those affected by the presence of an array, such as bed shear stress, are chosen as inputs. MaxEnt then uses a maximum-entropy modelling approach to estimate population distribution across the modelled area. OpenTidalFarm is used to maximise the power generated by an array, or multiple arrays, through adjusting the position and number of turbines within them. It uses a 2D shallow water model with turbine arrays represented as adjustable friction fields. It has the capability to also optimise for user created functionals that can be expressed mathematically. This work uses two functionals; power extracted by the array, and the suitability of habitat as predicted by MaxEnt. A gradient-based local optimisation is used to adjust the array layout at each iteration. This work presents arrays that are optimised for both yield and the viability of habitat for chosen species. In each scenario studied, a range of array formations is found expressing varying preferences for either functional. Further analyses then allow for the identification of trade-offs between the two key societal objectives of energy production and conservation. This in turn produces information valuable to stakeholders and policymakers when making decisions on array design.

Discrete bacteria foraging optimization algorithm for graph based problems - a transition from continuous to discrete

NASA Astrophysics Data System (ADS)

Sur, Chiranjib; Shukla, Anupam

2018-03-01

Bacteria Foraging Optimisation Algorithm is a collective behaviour-based meta-heuristics searching depending on the social influence of the bacteria co-agents in the search space of the problem. The algorithm faces tremendous hindrance in terms of its application for discrete problems and graph-based problems due to biased mathematical modelling and dynamic structure of the algorithm. This had been the key factor to revive and introduce the discrete form called Discrete Bacteria Foraging Optimisation (DBFO) Algorithm for discrete problems which exceeds the number of continuous domain problems represented by mathematical and numerical equations in real life. In this work, we have mainly simulated a graph-based road multi-objective optimisation problem and have discussed the prospect of its utilisation in other similar optimisation problems and graph-based problems. The various solution representations that can be handled by this DBFO has also been discussed. The implications and dynamics of the various parameters used in the DBFO are illustrated from the point view of the problems and has been a combination of both exploration and exploitation. The result of DBFO has been compared with Ant Colony Optimisation and Intelligent Water Drops Algorithms. Important features of DBFO are that the bacteria agents do not depend on the local heuristic information but estimates new exploration schemes depending upon the previous experience and covered path analysis. This makes the algorithm better in combination generation for graph-based problems and combination generation for NP hard problems.

Optimisation of Healthcare Contracts: Tensions Between Standardisation and Innovation

PubMed Central

Mikkers, Misja; Ryan, Padhraig

2016-01-01

An important determinant of health system performance is contracting. Providers often respond to financial incentives, despite the ethical underpinnings of medicine, and payers can craft contracts to influence performance. Yet contracting is highly imperfect in both single-payer and multi-payer health systems. Arguably, in a competitive, multi-payer environment, contractual innovation may occur more rapidly than in a single-payer system. This innovation in contract design could enhance performance. However, contractual innovation often fails to improve performance as payer incentives are misaligned with public policy objectives. Numerous countries seek to improve healthcare contracts, but thus far no health system has demonstrably crafted the necessary blend of incentives to stimulate optimal contracting. PMID:26927400

Markovian queue optimisation analysis with an unreliable server subject to working breakdowns and impatient customers

NASA Astrophysics Data System (ADS)

Liou, Cheng-Dar

2015-09-01

This study investigates an infinite capacity Markovian queue with a single unreliable service station, in which the customers may balk (do not enter) and renege (leave the queue after entering). The unreliable service station can be working breakdowns even if no customers are in the system. The matrix-analytic method is used to compute the steady-state probabilities for the number of customers, rate matrix and stability condition in the system. The single-objective model for cost and bi-objective model for cost and expected waiting time are derived in the system to fit in with practical applications. The particle swarm optimisation algorithm is implemented to find the optimal combinations of parameters in the pursuit of minimum cost. Two different approaches are used to identify the Pareto optimal set and compared: the epsilon-constraint method and non-dominate sorting genetic algorithm. Compared results allow using the traditional optimisation approach epsilon-constraint method, which is computationally faster and permits a direct sensitivity analysis of the solution under constraint or parameter perturbation. The Pareto front and non-dominated solutions set are obtained and illustrated. The decision makers can use these to improve their decision-making quality.

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

PubMed Central

Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

2013-01-01

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm. PMID:23737718

Improving Vector Evaluated Particle Swarm Optimisation by incorporating nondominated solutions.

PubMed

Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

2013-01-01

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

Visualising Pareto-optimal trade-offs helps move beyond monetary-only criteria for water management decisions

NASA Astrophysics Data System (ADS)

Hurford, Anthony; Harou, Julien

2014-05-01

Water related eco-system services are important to the livelihoods of the poorest sectors of society in developing countries. Degradation or loss of these services can increase the vulnerability of people decreasing their capacity to support themselves. New approaches to help guide water resources management decisions are needed which account for the non-market value of ecosystem goods and services. In case studies from Brazil and Kenya we demonstrate the capability of many objective Pareto-optimal trade-off analysis to help decision makers balance economic and non-market benefits from the management of existing multi-reservoir systems. A multi-criteria search algorithm is coupled to a water resources management simulator of each basin to generate a set of Pareto-approximate trade-offs representing the best case management decisions. In both cases, volume dependent reservoir release rules are the management decisions being optimised. In the Kenyan case we further assess the impacts of proposed irrigation investments, and how the possibility of new investments impacts the system's trade-offs. During the multi-criteria search (optimisation), performance of different sets of management decisions (policies) is assessed against case-specific objective functions representing provision of water supply and irrigation, hydropower generation and maintenance of ecosystem services. Results are visualised as trade-off surfaces to help decision makers understand the impacts of different policies on a broad range of stakeholders and to assist in decision-making. These case studies show how the approach can reveal unexpected opportunities for win-win solutions, and quantify the trade-offs between investing to increase agricultural revenue and negative impacts on protected ecosystems which support rural livelihoods.

Multi-terminal pipe routing by Steiner minimal tree and particle swarm optimisation

NASA Astrophysics Data System (ADS)

Liu, Qiang; Wang, Chengen

2012-08-01

Computer-aided design of pipe routing is of fundamental importance for complex equipments' developments. In this article, non-rectilinear branch pipe routing with multiple terminals that can be formulated as a Euclidean Steiner Minimal Tree with Obstacles (ESMTO) problem is studied in the context of an aeroengine-integrated design engineering. Unlike the traditional methods that connect pipe terminals sequentially, this article presents a new branch pipe routing algorithm based on the Steiner tree theory. The article begins with a new algorithm for solving the ESMTO problem by using particle swarm optimisation (PSO), and then extends the method to the surface cases by using geodesics to meet the requirements of routing non-rectilinear pipes on the surfaces of aeroengines. Subsequently, the adaptive region strategy and the basic visibility graph method are adopted to increase the computation efficiency. Numeral computations show that the proposed routing algorithm can find satisfactory routing layouts while running in polynomial time.

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

2017-02-01

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

Reactive power planning under high penetration of wind energy using Benders decomposition

DOE PAGES

Xu, Yan; Wei, Yanli; Fang, Xin; ...

2015-11-05

This study addresses the optimal allocation of reactive power volt-ampere reactive (VAR) sources under the paradigm of high penetration of wind energy. Reactive power planning (RPP) in this particular condition involves a high level of uncertainty because of wind power characteristic. To properly model wind generation uncertainty, a multi-scenario framework optimal power flow that considers the voltage stability constraint under the worst wind scenario and transmission N 1 contingency is developed. The objective of RPP in this study is to minimise the total cost including the VAR investment cost and the expected generation cost. Therefore RPP under this condition ismore » modelled as a two-stage stochastic programming problem to optimise the VAR location and size in one stage, then to minimise the fuel cost in the other stage, and eventually, to find the global optimal RPP results iteratively. Benders decomposition is used to solve this model with an upper level problem (master problem) for VAR allocation optimisation and a lower problem (sub-problem) for generation cost minimisation. Impact of the potential reactive power support from doubly-fed induction generator (DFIG) is also analysed. Lastly, case studies on the IEEE 14-bus and 118-bus systems are provided to verify the proposed method.« less

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

PubMed

Plewczynski, Dariusz; Basu, Subhadip; Saha, Indrajit

2012-08-01

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The source code and precompiled binaries of brainstorming tool are available at http://code.google.com/p/automotifserver/ under Apache 2.0 licensing.

Development of the hard and soft constraints based optimisation model for unit sizing of the hybrid renewable energy system designed for microgrid applications

NASA Astrophysics Data System (ADS)

Sundaramoorthy, Kumaravel

2017-02-01

The hybrid energy systems (HESs) based electricity generation system has become a more attractive solution for rural electrification nowadays. Economically feasible and technically reliable HESs are solidly based on an optimisation stage. This article discusses about the optimal unit sizing model with the objective function to minimise the total cost of the HES. Three typical rural sites from southern part of India have been selected for the application of the developed optimisation methodology. Feasibility studies and sensitivity analysis on the optimal HES are discussed elaborately in this article. A comparison has been carried out with the Hybrid Optimization Model for Electric Renewable optimisation model for three sites. The optimal HES is found with less total net present rate and rate of energy compared with the existing method

Consideration effect of wind farms on the network reconfiguration in the distribution systems in an uncertain environment

NASA Astrophysics Data System (ADS)

Rahmani, Kianoosh; Kavousifard, Farzaneh; Abbasi, Alireza

2017-09-01

This article proposes a novel probabilistic Distribution Feeder Reconfiguration (DFR) based method to consider the uncertainty impacts into account with high accuracy. In order to achieve the set aim, different scenarios are generated to demonstrate the degree of uncertainty in the investigated elements which are known as the active and reactive load consumption and the active power generation of the wind power units. Notably, a normal Probability Density Function (PDF) based on the desired accuracy is divided into several class intervals for each uncertain parameter. Besides, the Weiball PDF is utilised for modelling wind generators and taking the variation impacts of the power production in wind generators. The proposed problem is solved based on Fuzzy Adaptive Modified Particle Swarm Optimisation to find the most optimal switching scheme during the Multi-objective DFR. Moreover, this paper holds two suggestions known as new mutation methods to adjust the inertia weight of PSO by the fuzzy rules to enhance its ability in global searching within the entire search space.

Data-driven train set crash dynamics simulation

NASA Astrophysics Data System (ADS)

Tang, Zhao; Zhu, Yunrui; Nie, Yinyu; Guo, Shihui; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2017-02-01

Traditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force-displacement curves and predicts a force-displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency.

A data-driven, knowledge-based approach to biomarker discovery: application to circulating microRNA markers of colorectal cancer prognosis.

PubMed

Vafaee, Fatemeh; Diakos, Connie; Kirschner, Michaela B; Reid, Glen; Michael, Michael Z; Horvath, Lisa G; Alinejad-Rokny, Hamid; Cheng, Zhangkai Jason; Kuncic, Zdenka; Clarke, Stephen

2018-01-01

Recent advances in high-throughput technologies have provided an unprecedented opportunity to identify molecular markers of disease processes. This plethora of complex-omics data has simultaneously complicated the problem of extracting meaningful molecular signatures and opened up new opportunities for more sophisticated integrative and holistic approaches. In this era, effective integration of data-driven and knowledge-based approaches for biomarker identification has been recognised as key to improving the identification of high-performance biomarkers, and necessary for translational applications. Here, we have evaluated the role of circulating microRNA as a means of predicting the prognosis of patients with colorectal cancer, which is the second leading cause of cancer-related death worldwide. We have developed a multi-objective optimisation method that effectively integrates a data-driven approach with the knowledge obtained from the microRNA-mediated regulatory network to identify robust plasma microRNA signatures which are reliable in terms of predictive power as well as functional relevance. The proposed multi-objective framework has the capacity to adjust for conflicting biomarker objectives and to incorporate heterogeneous information facilitating systems approaches to biomarker discovery. We have found a prognostic signature of colorectal cancer comprising 11 circulating microRNAs. The identified signature predicts the patients' survival outcome and targets pathways underlying colorectal cancer progression. The altered expression of the identified microRNAs was confirmed in an independent public data set of plasma samples of patients in early stage vs advanced colorectal cancer. Furthermore, the generality of the proposed method was demonstrated across three publicly available miRNA data sets associated with biomarker studies in other diseases.

Optimisation of chromatographic resolution using objective functions including both time and spectral information.

PubMed

Torres-Lapasió, J R; Pous-Torres, S; Ortiz-Bolsico, C; García-Alvarez-Coque, M C

2015-01-16

The optimisation of the resolution in high-performance liquid chromatography is traditionally performed attending only to the time information. However, even in the optimal conditions, some peak pairs may remain unresolved. Such incomplete resolution can be still accomplished by deconvolution, which can be carried out with more guarantees of success by including spectral information. In this work, two-way chromatographic objective functions (COFs) that incorporate both time and spectral information were tested, based on the peak purity (analyte peak fraction free of overlapping) and the multivariate selectivity (figure of merit derived from the net analyte signal) concepts. These COFs are sensitive to situations where the components that coelute in a mixture show some spectral differences. Therefore, they are useful to find out experimental conditions where the spectrochromatograms can be recovered by deconvolution. Two-way multivariate selectivity yielded the best performance and was applied to the separation using diode-array detection of a mixture of 25 phenolic compounds, which remained unresolved in the chromatographic order using linear and multi-linear gradients of acetonitrile-water. Peak deconvolution was carried out using the combination of orthogonal projection approach and alternating least squares. Copyright © 2014 Elsevier B.V. All rights reserved.

Learning Content and Software Evaluation and Personalisation Problems

ERIC Educational Resources Information Center

Kurilovas, Eugenijus; Serikoviene, Silvija

2010-01-01

The paper aims to analyse several scientific approaches how to evaluate, implement or choose learning content and software suitable for personalised users/learners needs. Learning objects metadata customisation method as well as the Method of multiple criteria evaluation and optimisation of learning software represented by the experts' additive…

Systemic solutions for multi-benefit water and environmental management.

PubMed

Everard, Mark; McInnes, Robert

2013-09-01

The environmental and financial costs of inputs to, and unintended consequences arising from narrow consideration of outputs from, water and environmental management technologies highlight the need for low-input solutions that optimise outcomes across multiple ecosystem services. Case studies examining the inputs and outputs associated with several ecosystem-based water and environmental management technologies reveal a range from those that differ little from conventional electro-mechanical engineering techniques through methods, such as integrated constructed wetlands (ICWs), designed explicitly as low-input systems optimising ecosystem service outcomes. All techniques present opportunities for further optimisation of outputs, and hence for greater cumulative public value. We define 'systemic solutions' as "…low-input technologies using natural processes to optimise benefits across the spectrum of ecosystem services and their beneficiaries". They contribute to sustainable development by averting unintended negative impacts and optimising benefits to all ecosystem service beneficiaries, increasing net economic value. Legacy legislation addressing issues in a fragmented way, associated 'ring-fenced' budgets and established management assumptions represent obstacles to implementing 'systemic solutions'. However, flexible implementation of legacy regulations recognising their primary purpose, rather than slavish adherence to detailed sub-clauses, may achieve greater overall public benefit through optimisation of outcomes across ecosystem services. Systemic solutions are not a panacea if applied merely as 'downstream' fixes, but are part of, and a means to accelerate, broader culture change towards more sustainable practice. This necessarily entails connecting a wider network of interests in the formulation and design of mutually-beneficial systemic solutions, including for example spatial planners, engineers, regulators, managers, farming and other businesses, and researchers working on ways to quantify and optimise delivery of ecosystem services. Copyright © 2013 Elsevier B.V. All rights reserved.

Scoring functions for protein-protein interactions.

PubMed

Moal, Iain H; Moretti, Rocco; Baker, David; Fernández-Recio, Juan

2013-12-01

The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Optimisation study of a vehicle bumper subsystem with fuzzy parameters

NASA Astrophysics Data System (ADS)

Farkas, L.; Moens, D.; Donders, S.; Vandepitte, D.

2012-10-01

This paper deals with the design and optimisation for crashworthiness of a vehicle bumper subsystem, which is a key scenario for vehicle component design. The automotive manufacturers and suppliers have to find optimal design solutions for such subsystems that comply with the conflicting requirements of the regulatory bodies regarding functional performance (safety and repairability) and regarding the environmental impact (mass). For the bumper design challenge, an integrated methodology for multi-attribute design engineering of mechanical structures is set up. The integrated process captures the various tasks that are usually performed manually, this way facilitating the automated design iterations for optimisation. Subsequently, an optimisation process is applied that takes the effect of parametric uncertainties into account, such that the system level of failure possibility is acceptable. This optimisation process is referred to as possibility-based design optimisation and integrates the fuzzy FE analysis applied for the uncertainty treatment in crash simulations. This process is the counterpart of the reliability-based design optimisation used in a probabilistic context with statistically defined parameters (variabilities).

Is an objective refraction optimised using the visual Strehl ratio better than a subjective refraction?

PubMed

Hastings, Gareth D; Marsack, Jason D; Nguyen, Lan Chi; Cheng, Han; Applegate, Raymond A

2017-05-01

To prospectively examine whether using the visual image quality metric, visual Strehl (VSX), to optimise objective refraction from wavefront error measurements can provide equivalent or better visual performance than subjective refraction and which refraction is preferred in free viewing. Subjective refractions and wavefront aberrations were measured on 40 visually-normal eyes of 20 subjects, through natural and dilated pupils. For each eye a sphere, cylinder, and axis prescription was also objectively determined that optimised visual image quality (VSX) for the measured wavefront error. High contrast (HC) and low contrast (LC) logMAR visual acuity (VA) and short-term monocular distance vision preference were recorded and compared between the VSX-objective and subjective prescriptions both undilated and dilated. For 36 myopic eyes, clinically equivalent (and not statistically different) HC VA was provided with both the objective and subjective refractions (undilated mean ± S.D. was -0.06 ± 0.04 with both refractions; dilated was -0.05 ± 0.04 with the objective, and -0.05 ± 0.05 with the subjective refraction). LC logMAR VA provided by the objective refraction was also clinically equivalent and not statistically different to that provided by the subjective refraction through both natural and dilated pupils for myopic eyes. In free viewing the objective prescription was preferred over the subjective by 72% of myopic eyes when not dilated. For four habitually undercorrected high hyperopic eyes, the VSX-objective refraction was more positive in spherical power and VA poorer than with the subjective refraction. A method of simultaneously optimising sphere, cylinder, and axis from wavefront error measurements, using the visual image quality metric VSX, is described. In myopic subjects, visual performance, as measured by HC and LC VA, with this VSX-objective refraction was found equivalent to that provided by subjective refraction, and was typically preferred over subjective refraction. Subjective refraction was preferred by habitually undercorrected hyperopic eyes. © 2017 The Authors Ophthalmic & Physiological Optics © 2017 The College of Optometrists.

PyEvolve: a toolkit for statistical modelling of molecular evolution.

PubMed

Butterfield, Andrew; Vedagiri, Vivek; Lang, Edward; Lawrence, Cath; Wakefield, Matthew J; Isaev, Alexander; Huttley, Gavin A

2004-01-05

Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences - ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation. Here we describe the implementation of PyEvolve, a toolkit for the application of existing, and development of new, statistical methods for molecular evolution. We present the object architecture and design schema of PyEvolve, which includes an adaptable multi-level parallelisation schema. The approach for defining new methods is illustrated by implementing a novel dinucleotide model of substitution that includes a parameter for mutation of methylated CpG's, which required 8 lines of standard Python code to define. Benchmarking was performed using either a dinucleotide or codon substitution model applied to an alignment of BRCA1 sequences from 20 mammals, or a 10 species subset. Up to five-fold parallel performance gains over serial were recorded. Compared to leading alternative software, PyEvolve exhibited significantly better real world performance for parameter rich models with a large data set, reducing the time required for optimisation from approximately 10 days to approximately 6 hours. PyEvolve provides flexible functionality that can be used either for statistical modelling of molecular evolution, or the development of new methods in the field. The toolkit can be used interactively or by writing and executing scripts. The toolkit uses efficient processes for specifying the parameterisation of statistical models, and implements numerous optimisations that make highly parameter rich likelihood functions solvable within hours on multi-cpu hardware. PyEvolve can be readily adapted in response to changing computational demands and hardware configurations to maximise performance. PyEvolve is released under the GPL and can be downloaded from http://cbis.anu.edu.au/software.

Topology optimisation of micro fluidic mixers considering fluid-structure interactions with a coupled Lattice Boltzmann algorithm

NASA Astrophysics Data System (ADS)

Munk, David J.; Kipouros, Timoleon; Vio, Gareth A.; Steven, Grant P.; Parks, Geoffrey T.

2017-11-01

Recently, the study of micro fluidic devices has gained much interest in various fields from biology to engineering. In the constant development cycle, the need to optimise the topology of the interior of these devices, where there are two or more optimality criteria, is always present. In this work, twin physical situations, whereby optimal fluid mixing in the form of vorticity maximisation is accompanied by the requirement that the casing in which the mixing takes place has the best structural performance in terms of the greatest specific stiffness, are considered. In the steady state of mixing this also means that the stresses in the casing are as uniform as possible, thus giving a desired operating life with minimum weight. The ultimate aim of this research is to couple two key disciplines, fluids and structures, into a topology optimisation framework, which shows fast convergence for multidisciplinary optimisation problems. This is achieved by developing a bi-directional evolutionary structural optimisation algorithm that is directly coupled to the Lattice Boltzmann method, used for simulating the flow in the micro fluidic device, for the objectives of minimum compliance and maximum vorticity. The needs for the exploration of larger design spaces and to produce innovative designs make meta-heuristic algorithms, such as genetic algorithms, particle swarms and Tabu Searches, less efficient for this task. The multidisciplinary topology optimisation framework presented in this article is shown to increase the stiffness of the structure from the datum case and produce physically acceptable designs. Furthermore, the topology optimisation method outperforms a Tabu Search algorithm in designing the baffle to maximise the mixing of the two fluids.

Efficient and portable acceleration of quantum chemical many-body methods in mixed floating point precision using OpenACC compiler directives

NASA Astrophysics Data System (ADS)

Eriksen, Janus J.

2017-09-01

It is demonstrated how the non-proprietary OpenACC standard of compiler directives may be used to compactly and efficiently accelerate the rate-determining steps of two of the most routinely applied many-body methods of electronic structure theory, namely the second-order Møller-Plesset (MP2) model in its resolution-of-the-identity approximated form and the (T) triples correction to the coupled cluster singles and doubles model (CCSD(T)). By means of compute directives as well as the use of optimised device math libraries, the operations involved in the energy kernels have been ported to graphics processing unit (GPU) accelerators, and the associated data transfers correspondingly optimised to such a degree that the final implementations (using either double and/or single precision arithmetics) are capable of scaling to as large systems as allowed for by the capacity of the host central processing unit (CPU) main memory. The performance of the hybrid CPU/GPU implementations is assessed through calculations on test systems of alanine amino acid chains using one-electron basis sets of increasing size (ranging from double- to pentuple-ζ quality). For all but the smallest problem sizes of the present study, the optimised accelerated codes (using a single multi-core CPU host node in conjunction with six GPUs) are found to be capable of reducing the total time-to-solution by at least an order of magnitude over optimised, OpenMP-threaded CPU-only reference implementations.

Combined use of a priori data for fast system self-calibration of a non-rigid multi-camera fringe projection system

NASA Astrophysics Data System (ADS)

Stavroulakis, Petros I.; Chen, Shuxiao; Sims-Waterhouse, Danny; Piano, Samanta; Southon, Nicholas; Bointon, Patrick; Leach, Richard

2017-06-01

In non-rigid fringe projection 3D measurement systems, where either the camera or projector setup can change significantly between measurements or the object needs to be tracked, self-calibration has to be carried out frequently to keep the measurements accurate1. In fringe projection systems, it is common to use methods developed initially for photogrammetry for the calibration of the camera(s) in the system in terms of extrinsic and intrinsic parameters. To calibrate the projector(s) an extra correspondence between a pre-calibrated camera and an image created by the projector is performed. These recalibration steps are usually time consuming and involve the measurement of calibrated patterns on planes, before the actual object can continue to be measured after a motion of a camera or projector has been introduced in the setup and hence do not facilitate fast 3D measurement of objects when frequent experimental setup changes are necessary. By employing and combining a priori information via inverse rendering, on-board sensors, deep learning and leveraging a graphics processor unit (GPU), we assess a fine camera pose estimation method which is based on optimising the rendering of a model of a scene and the object to match the view from the camera. We find that the success of this calibration pipeline can be greatly improved by using adequate a priori information from the aforementioned sources.

Four Scenarios for Determining the Size and Reusability of Learning Objects

ERIC Educational Resources Information Center

Schoonenboom, Judith

2012-01-01

The best method for determining the size of learning objects (LOs) so as to optimise their reusability has been a topic of debate for years now. Although there appears to be agreement on basic assumptions, developed guidelines and principles are often in conflict. This study shows that this confusion stems from the fact that in the literature,…

Employing multi-GPU power for molecular dynamics simulation: an extension of GALAMOST

NASA Astrophysics Data System (ADS)

Zhu, You-Liang; Pan, Deng; Li, Zhan-Wei; Liu, Hong; Qian, Hu-Jun; Zhao, Yang; Lu, Zhong-Yuan; Sun, Zhao-Yan

2018-04-01

We describe the algorithm of employing multi-GPU power on the basis of Message Passing Interface (MPI) domain decomposition in a molecular dynamics code, GALAMOST, which is designed for the coarse-grained simulation of soft matters. The code of multi-GPU version is developed based on our previous single-GPU version. In multi-GPU runs, one GPU takes charge of one domain and runs single-GPU code path. The communication between neighbouring domains takes a similar algorithm of CPU-based code of LAMMPS, but is optimised specifically for GPUs. We employ a memory-saving design which can enlarge maximum system size at the same device condition. An optimisation algorithm is employed to prolong the update period of neighbour list. We demonstrate good performance of multi-GPU runs on the simulation of Lennard-Jones liquid, dissipative particle dynamics liquid, polymer and nanoparticle composite, and two-patch particles on workstation. A good scaling of many nodes on cluster for two-patch particles is presented.

Radiation exposure in X-ray-based imaging techniques used in osteoporosis

PubMed Central

Adams, Judith E.; Guglielmi, Giuseppe; Link, Thomas M.

2010-01-01

Recent advances in medical X-ray imaging have enabled the development of new techniques capable of assessing not only bone quantity but also structure. This article provides (a) a brief review of the current X-ray methods used for quantitative assessment of the skeleton, (b) data on the levels of radiation exposure associated with these methods and (c) information about radiation safety issues. Radiation doses associated with dual-energy X-ray absorptiometry are very low. However, as with any X-ray imaging technique, each particular examination must always be clinically justified. When an examination is justified, the emphasis must be on dose optimisation of imaging protocols. Dose optimisation is more important for paediatric examinations because children are more vulnerable to radiation than adults. Methods based on multi-detector CT (MDCT) are associated with higher radiation doses. New 3D volumetric hip and spine quantitative computed tomography (QCT) techniques and high-resolution MDCT for evaluation of bone structure deliver doses to patients from 1 to 3 mSv. Low-dose protocols are needed to reduce radiation exposure from these methods and minimise associated health risks. PMID:20559834

Optimisation of substrate blends in anaerobic co-digestion using adaptive linear programming.

PubMed

García-Gen, Santiago; Rodríguez, Jorge; Lema, Juan M

2014-12-01

Anaerobic co-digestion of multiple substrates has the potential to enhance biogas productivity by making use of the complementary characteristics of different substrates. A blending strategy based on a linear programming optimisation method is proposed aiming at maximising COD conversion into methane, but simultaneously maintaining a digestate and biogas quality. The method incorporates experimental and heuristic information to define the objective function and the linear restrictions. The active constraints are continuously adapted (by relaxing the restriction boundaries) such that further optimisations in terms of methane productivity can be achieved. The feasibility of the blends calculated with this methodology was previously tested and accurately predicted with an ADM1-based co-digestion model. This was validated in a continuously operated pilot plant, treating for several months different mixtures of glycerine, gelatine and pig manure at organic loading rates from 1.50 to 4.93 gCOD/Ld and hydraulic retention times between 32 and 40 days at mesophilic conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multi-spectral antireflection coating on zinc sulphide simultaneously effective in visible, eye safe laser wave length and MWIR region

NASA Astrophysics Data System (ADS)

Awasthi, Suman; Nautiyal, B. B.; Kumar, Rajiv; Bandyopadhyay, P. K.

2012-09-01

In recent years multi-spectral device is steadily growing popularity. Multi-spectral antireflection coating effective in visible region for sighting system, laser wavelength for ranging and MWIR region for thermal system can use common objective/receiver optics highly useful for state of art thermal instrumentation. In this paper, design and fabrication of antireflection coating simultaneously effective in visible region (450-650 nm), Eye safe laser wave length (1540 nm) and MWIR region (3.6-4.9 μm) has been reported. Comprehensive search method of design was used and the number of layers in the design was optimised with lowest evaluated merit function studied with respect to various layers. Finally eight-layer design stack was established using hafnium oxide as high index layer and silicon-di-oxide as low index coating material combination. The multilayer stack had been fabricated by using electron beam gun evaporation system in Symphony 9 vacuum coating unit. During layer deposition the substrate was irradiated with End-Hall ion gun. The evaporation was carried out in presence of oxygen and layer thicknesses were measured with crystal monitor. The result achieved for the antireflection coating was 85% average transmission from 450 to 650 nm in visible region, 95% transmission at 1540 nm and 96% average transmission from 3.6 to 4.9 μm in MWIR region.

Determine the optimal carrier selection for a logistics network based on multi-commodity reliability criterion

NASA Astrophysics Data System (ADS)

Lin, Yi-Kuei; Yeh, Cheng-Ta

2013-05-01

From the perspective of supply chain management, the selected carrier plays an important role in freight delivery. This article proposes a new criterion of multi-commodity reliability and optimises the carrier selection based on such a criterion for logistics networks with routes and nodes, over which multiple commodities are delivered. Carrier selection concerns the selection of exactly one carrier to deliver freight on each route. The capacity of each carrier has several available values associated with a probability distribution, since some of a carrier's capacity may be reserved for various orders. Therefore, the logistics network, given any carrier selection, is a multi-commodity multi-state logistics network. Multi-commodity reliability is defined as a probability that the logistics network can satisfy a customer's demand for various commodities, and is a performance indicator for freight delivery. To solve this problem, this study proposes an optimisation algorithm that integrates genetic algorithm, minimal paths and Recursive Sum of Disjoint Products. A practical example in which multi-sized LCD monitors are delivered from China to Germany is considered to illustrate the solution procedure.

Simultaneous determination of water-soluble vitamins in beverages and dietary supplements by LC-MS/MS.

PubMed

Kakitani, Ayano; Inoue, Tomonori; Matsumoto, Keiko; Watanabe, Jun; Nagatomi, Yasushi; Mochizuki, Naoki

2014-01-01

An LC-MS/MS method was developed for the simultaneous determination of 15 water-soluble vitamins that are widely used as additives in beverages and dietary supplements. This combined method involves the following simple pre-treatment procedures: dietary supplement samples were prepared by centrifugation and filtration after an extraction step, whereas beverage samples were diluted prior to injection. Chromatographic analysis in this method utilised a multi-mode ODS column, which provided reverse-phase, anion- and cation-exchange capacities, and therefore improved the retention of highly polar analytes such as water-soluble vitamins. Additionally, the multi-mode ODS column did not require adding ion pair reagents to the mobile phase. We optimised the chromatographic separation of 15 water-soluble vitamins by adjusting the mobile phase pH and the organic solvent. We also conducted an analysis of a NIST Standard Reference Material (SRM 3280 Multi-vitamin/Multi-element tablets) using this method to verify its accuracy. In addition, the method was applied to identify the vitamins in commercial beverages and dietary supplements. By comparing results with the label values and results obtained by official methods, it was concluded that the method could be used for quality control and to compose nutrition labels for vitamin-enriched products.

Comparing approaches for using climate projections in assessing water resources investments for systems with multiple stakeholder groups

NASA Astrophysics Data System (ADS)

Hurford, Anthony; Harou, Julien

2015-04-01

Climate change has challenged conventional methods of planning water resources infrastructure investment, relying on stationarity of time-series data. It is not clear how to best use projections of future climatic conditions. Many-objective simulation-optimisation and trade-off analysis using evolutionary algorithms has been proposed as an approach to addressing complex planning problems with multiple conflicting objectives. The search for promising assets and policies can be carried out across a range of climate projections, to identify the configurations of infrastructure investment shown by model simulation to be robust under diverse future conditions. Climate projections can be used in different ways within a simulation model to represent the range of possible future conditions and understand how optimal investments vary according to the different hydrological conditions. We compare two approaches, optimising over an ensemble of different 20-year flow and PET timeseries projections, and separately for individual future scenarios built synthetically from the original ensemble. Comparing trade-off curves and surfaces generated by the two approaches helps understand the limits and benefits of optimising under different sets of conditions. The comparison is made for the Tana Basin in Kenya, where climate change combined with multiple conflicting objectives of water management and infrastructure investment mean decision-making is particularly challenging.

Multiobjective optimisation of bogie suspension to boost speed on curves

NASA Astrophysics Data System (ADS)

Milad Mousavi-Bideleh, Seyed; Berbyuk, Viktor

2016-01-01

To improve safety and maximum admissible speed on different operational scenarios, multiobjective optimisation of bogie suspension components of a one-car railway vehicle model is considered. The vehicle model has 50 degrees of freedom and is developed in multibody dynamics software SIMPACK. Track shift force, running stability, and risk of derailment are selected as safety objective functions. The improved maximum admissible speeds of the vehicle on curves are determined based on the track plane accelerations up to 1.5 m/s2. To attenuate the number of design parameters for optimisation and improve the computational efficiency, a global sensitivity analysis is accomplished using the multiplicative dimensional reduction method (M-DRM). A multistep optimisation routine based on genetic algorithm (GA) and MATLAB/SIMPACK co-simulation is executed at three levels. The bogie conventional secondary and primary suspension components are chosen as the design parameters in the first two steps, respectively. In the last step semi-active suspension is in focus. The input electrical current to magnetorheological yaw dampers is optimised to guarantee an appropriate safety level. Semi-active controllers are also applied and the respective effects on bogie dynamics are explored. The safety Pareto optimised results are compared with those associated with in-service values. The global sensitivity analysis and multistep approach significantly reduced the number of design parameters and improved the computational efficiency of the optimisation. Furthermore, using the optimised values of design parameters give the possibility to run the vehicle up to 13% faster on curves while a satisfactory safety level is guaranteed. The results obtained can be used in Pareto optimisation and active bogie suspension design problems.

Radiation dose optimisation for conventional imaging in infants and newborns using automatic dose management software: an application of the new 2013/59 EURATOM directive.

PubMed

Alejo, L; Corredoira, E; Sánchez-Muñoz, F; Huerga, C; Aza, Z; Plaza-Núñez, R; Serrada, A; Bret-Zurita, M; Parrón, M; Prieto-Areyano, C; Garzón-Moll, G; Madero, R; Guibelalde, E

2018-04-09

Objective: The new 2013/59 EURATOM Directive (ED) demands dosimetric optimisation procedures without undue delay. The aim of this study was to optimise paediatric conventional radiology examinations applying the ED without compromising the clinical diagnosis. Automatic dose management software (ADMS) was used to analyse 2678 studies of children from birth to 5 years of age, obtaining local diagnostic reference levels (DRLs) in terms of entrance surface air kerma. Given local DRL for infants and chest examinations exceeded the European Commission (EC) DRL, an optimisation was performed decreasing the kVp and applying the automatic control exposure. To assess the image quality, an analysis of high-contrast resolution (HCSR), signal-to-noise ratio (SNR) and figure of merit (FOM) was performed, as well as a blind test based on the generalised estimating equations method. For newborns and chest examinations, the local DRL exceeded the EC DRL by 113%. After the optimisation, a reduction of 54% was obtained. No significant differences were found in the image quality blind test. A decrease in SNR (-37%) and HCSR (-68%), and an increase in FOM (42%), was observed. ADMS allows the fast calculation of local DRLs and the performance of optimisation procedures in babies without delay. However, physical and clinical analyses of image quality remain to be needed to ensure the diagnostic integrity after the optimisation process. Advances in knowledge: ADMS are useful to detect radiation protection problems and to perform optimisation procedures in paediatric conventional imaging without undue delay, as ED requires.

Energy Decision Science and Informatics | Integrated Energy Solutions |

Science.gov Websites

Science Advanced decision science methods include multi-objective and multi-criteria decision support. Our decision science methods, including multi-objective and multi-criteria decision support. For example, we

Distributed optimisation problem with communication delay and external disturbance

NASA Astrophysics Data System (ADS)

Tran, Ngoc-Tu; Xiao, Jiang-Wen; Wang, Yan-Wu; Yang, Wu

2017-12-01

This paper investigates the distributed optimisation problem for the multi-agent systems (MASs) with the simultaneous presence of external disturbance and the communication delay. To solve this problem, a two-step design scheme is introduced. In the first step, based on the internal model principle, the internal model term is constructed to compensate the disturbance asymptotically. In the second step, a distributed optimisation algorithm is designed to solve the distributed optimisation problem based on the MASs with the simultaneous presence of disturbance and communication delay. Moreover, in the proposed algorithm, each agent interacts with its neighbours through the connected topology and the delay occurs during the information exchange. By utilising Lyapunov-Krasovskii functional, the delay-dependent conditions are derived for both slowly and fast time-varying delay, respectively, to ensure the convergence of the algorithm to the optimal solution of the optimisation problem. Several numerical simulation examples are provided to illustrate the effectiveness of the theoretical results.

Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion

PubMed Central

Ma, Da; Cardoso, Manuel J.; Modat, Marc; Powell, Nick; Wells, Jack; Holmes, Holly; Wiseman, Frances; Tybulewicz, Victor; Fisher, Elizabeth; Lythgoe, Mark F.; Ourselin, Sébastien

2014-01-01

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework. PMID:24475148

Stochastic optimisation of water allocation on a global scale

NASA Astrophysics Data System (ADS)

Schmitz, Oliver; Straatsma, Menno; Karssenberg, Derek; Bierkens, Marc F. P.

2014-05-01

Climate change, increasing population and further economic developments are expected to increase water scarcity for many regions of the world. Optimal water management strategies are required to minimise the water gap between water supply and domestic, industrial and agricultural water demand. A crucial aspect of water allocation is the spatial scale of optimisation. Blue water supply peaks at the upstream parts of large catchments, whereas demands are often largest at the industrialised downstream parts. Two extremes exist in water allocation: (i) 'First come, first serve,' which allows the upstream water demands to be fulfilled without considerations of downstream demands, and (ii) 'All for one, one for all' that satisfies water allocation over the whole catchment. In practice, water treaties govern intermediate solutions. The objective of this study is to determine the effect of these two end members on water allocation optimisation with respect to water scarcity. We conduct this study on a global scale with the year 2100 as temporal horizon. Water supply is calculated using the hydrological model PCR-GLOBWB, operating at a 5 arcminutes resolution and a daily time step. PCR-GLOBWB is forced with temperature and precipitation fields from the Hadgem2-ES global circulation model that participated in the latest coupled model intercomparison project (CMIP5). Water demands are calculated for representative concentration pathway 6.0 (RCP 6.0) and shared socio-economic pathway scenario 2 (SSP2). To enable the fast computation of the optimisation, we developed a hydrologically correct network of 1800 basin segments with an average size of 100 000 square kilometres. The maximum number of nodes in a network was 140 for the Amazon Basin. Water demands and supplies are aggregated to cubic kilometres per month per segment. A new open source implementation of the water allocation is developed for the stochastic optimisation of the water allocation. We apply a Genetic Algorithm for each segment to estimate the set of parameters that distribute the water supply for each node. We use the Python programming language and a flexible software architecture allowing to straightforwardly 1) exchange the process description for the nodes such that different water allocation schemes can be tested 2) exchange the objective function 3) apply the optimisation either to the whole catchment or to different sub-levels and 4) use multi-core CPUs concurrently and therefore reducing computation time. We demonstrate the application of the scientific workflow to the model outputs of PCR-GLOBWB and present first results on how water scarcity depends on the choice between the two extremes in water allocation.

Reservoir optimisation using El Niño information. Case study of Daule Peripa (Ecuador)

NASA Astrophysics Data System (ADS)

Gelati, Emiliano; Madsen, Henrik; Rosbjerg, Dan

2010-05-01

The optimisation of water resources systems requires the ability to produce runoff scenarios that are consistent with available climatic information. We approach stochastic runoff modelling with a Markov-modulated autoregressive model with exogenous input, which belongs to the class of Markov-switching models. The model assumes runoff parameterisation to be conditioned on a hidden climatic state following a Markov chain, whose state transition probabilities depend on climatic information. This approach allows stochastic modeling of non-stationary runoff, as runoff anomalies are described by a mixture of autoregressive models with exogenous input, each one corresponding to a climate state. We calibrate the model on the inflows of the Daule Peripa reservoir located in western Ecuador, where the occurrence of El Niño leads to anomalously heavy rainfall caused by positive sea surface temperature anomalies along the coast. El Niño - Southern Oscillation (ENSO) information is used to condition the runoff parameterisation. Inflow predictions are realistic, especially at the occurrence of El Niño events. The Daule Peripa reservoir serves a hydropower plant and a downstream water supply facility. Using historical ENSO records, synthetic monthly inflow scenarios are generated for the period 1950-2007. These scenarios are used as input to perform stochastic optimisation of the reservoir rule curves with a multi-objective Genetic Algorithm (MOGA). The optimised rule curves are assumed to be the reservoir base policy. ENSO standard indices are currently forecasted at monthly time scale with nine-month lead time. These forecasts are used to perform stochastic optimisation of reservoir releases at each monthly time step according to the following procedure: (i) nine-month inflow forecast scenarios are generated using ENSO forecasts; (ii) a MOGA is set up to optimise the upcoming nine monthly releases; (iii) the optimisation is carried out by simulating the releases on the inflow forecasts, and by applying the base policy on a subsequent synthetic inflow scenario in order to account for long-term costs; (iv) the optimised release for the first month is implemented; (v) the state of the system is updated and (i), (ii), (iii), and (iv) are iterated for the following time step. The results highlight the advantages of using a climate-driven stochastic model to produce inflow scenarios and forecasts for reservoir optimisation, showing potential improvements with respect to the current management. Dynamic programming was used to find the best possible release time series given the inflow observations, in order to benchmark any possible operational improvement.

Designing synthetic networks in silico: a generalised evolutionary algorithm approach.

PubMed

Smith, Robert W; van Sluijs, Bob; Fleck, Christian

2017-12-02

Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

A pragmatic multi-centre randomised controlled trial of fluid loading and level of dependency in high-risk surgical patients undergoing major elective surgery: trial protocol

PubMed Central

2010-01-01

Background Patients undergoing major elective or urgent surgery are at high risk of death or significant morbidity. Measures to reduce this morbidity and mortality include pre-operative optimisation and use of higher levels of dependency care after surgery. We propose a pragmatic multi-centre randomised controlled trial of level of dependency and pre-operative fluid therapy in high-risk surgical patients undergoing major elective surgery. Methods/Design A multi-centre randomised controlled trial with a 2 * 2 factorial design. The first randomisation is to pre-operative fluid therapy or standard regimen and the second randomisation is to routine intensive care versus high dependency care during the early post-operative period. We intend to recruit 204 patients undergoing major elective and urgent abdominal and thoraco-abdominal surgery who fulfil high-risk surgical criteria. The primary outcome for the comparison of level of care is cost-effectiveness at six months and for the comparison of fluid optimisation is the number of hospital days after surgery. Discussion We believe that the results of this study will be invaluable in determining the future care and clinical resource utilisation for this group of patients and thus will have a major impact on clinical practice. Trial Registration Trial registration number - ISRCTN32188676 PMID:20398378

3D Reconstruction of human bones based on dictionary learning.

PubMed

Zhang, Binkai; Wang, Xiang; Liang, Xiao; Zheng, Jinjin

2017-11-01

An effective method for reconstructing a 3D model of human bones from computed tomography (CT) image data based on dictionary learning is proposed. In this study, the dictionary comprises the vertices of triangular meshes, and the sparse coefficient matrix indicates the connectivity information. For better reconstruction performance, we proposed a balance coefficient between the approximation and regularisation terms and a method for optimisation. Moreover, we applied a local updating strategy and a mesh-optimisation method to update the dictionary and the sparse matrix, respectively. The two updating steps are iterated alternately until the objective function converges. Thus, a reconstructed mesh could be obtained with high accuracy and regularisation. The experimental results show that the proposed method has the potential to obtain high precision and high-quality triangular meshes for rapid prototyping, medical diagnosis, and tissue engineering. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

NASA Astrophysics Data System (ADS)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Minimising back reflections from the common path objective in a fundus camera

NASA Astrophysics Data System (ADS)

Swat, A.

2016-11-01

Eliminating back reflections is critical in the design of a fundus camera with internal illuminating system. As there is very little light reflected from the retina, even excellent antireflective coatings are not sufficient suppression of ghost reflections, therefore the number of surfaces in the common optics in illuminating and imaging paths shall be minimised. Typically a single aspheric objective is used. In the paper an alternative approach, an objective with all spherical surfaces, is presented. As more surfaces are required, more sophisticated method is needed to get rid of back reflections. Typically back reflections analysis, comprise treating subsequent objective surfaces as mirrors, and reflections from the objective surfaces are traced back through the imaging path. This approach can be applied in both sequential and nonsequential ray tracing. It is good enough for system check but not very suitable for early optimisation process in the optical system design phase. There are also available standard ghost control merit function operands in the sequential ray-trace, for example in Zemax system, but these don't allow back ray-trace in an alternative optical path, illumination vs. imaging. What is proposed in the paper, is a complete method to incorporate ghost reflected energy into the raytracing system merit function for sequential mode which is more efficient in optimisation process. Although developed for the purpose of specific case of fundus camera, the method might be utilised in a wider range of applications where ghost control is critical.

Integration of vehicle yaw stabilisation and rollover prevention through nonlinear hierarchical control allocation

NASA Astrophysics Data System (ADS)

Alberding, Matthäus B.; Tjønnås, Johannes; Johansen, Tor A.

2014-12-01

This work presents an approach to rollover prevention that takes advantage of the modular structure and optimisation properties of the control allocation paradigm. It eliminates the need for a stabilising roll controller by introducing rollover prevention as a constraint on the control allocation problem. The major advantage of this approach is the control authority margin that remains with a high-level controller even during interventions for rollover prevention. In this work, the high-level control is assigned to a yaw stabilising controller. It could be replaced by any other controller. The constraint for rollover prevention could be replaced by or extended to different control objectives. This work uses differential braking for actuation. The use of additional or different actuators is possible. The developed control algorithm is computationally efficient and suitable for low-cost automotive electronic control units. The predictive design of the rollover prevention constraint does not require any sensor equipment in addition to the yaw controller. The method is validated using an industrial multi-body vehicle simulation environment.

Study on optimal configuration of the grid-connected wind-solar-battery hybrid power system

NASA Astrophysics Data System (ADS)

Ma, Gang; Xu, Guchao; Ju, Rong; Wu, Tiantian

2017-08-01

The capacity allocation of each energy unit in the grid-connected wind-solar-battery hybrid power system is a significant segment in system design. In this paper, taking power grid dispatching into account, the research priorities are as follows: (1) We establish the mathematic models of each energy unit in the hybrid power system. (2) Based on dispatching of the power grid, energy surplus rate, system energy volatility and total cost, we establish the evaluation system for the wind-solar-battery power system and use a number of different devices as the constraint condition. (3) Based on an improved Genetic algorithm, we put forward a multi-objective optimisation algorithm to solve the optimal configuration problem in the hybrid power system, so we can achieve the high efficiency and economy of the grid-connected hybrid power system. The simulation result shows that the grid-connected wind-solar-battery hybrid power system has a higher comprehensive performance; the method of optimal configuration in this paper is useful and reasonable.

In vivo optimisation study for multi-baseline MR-based thermometry in the context of hyperthermia using MR-guided high intensity focused ultrasound for head and neck applications.

PubMed

Pichardo, Samuel; Köhler, Max; Lee, Justin; Hynnyen, Kullervo

2014-12-01

In this in vivo study, the feasibility to perform hyperthermia treatments in the head and neck using magnetic resonance image-guided high intensity focused ultrasound (MRgHIFU) was established using a porcine acute model. Porcine specimens with a weight between 17 and 18 kg were treated in the omohyoid muscle in the neck. Hyperthermia was applied with a target temperature of 41 °C for 30 min using a Sonalleve MRgHIFU system. MR-based thermometry was calculated using water-proton resonance frequency shift and multi-baseline look-up tables indexed by peak-to-peak displacement (Dpp) measurements using a pencil-beam navigator. Three hyperthermia experiments were conducted at different Dpp values of 0.2, 1.0 and 3.0 mm. An optimisation study was carried out to establish the optimal parameters controlling the multi-baseline method that ensured a minimisation of spatial-average peak-to-peak temperature (TSA-pp) and temperature direct current bias (TSA-DC). The multi-baseline technique reduced considerably the noise on both TSA-pp and TSA-DC. The reduction of noise was more important when Dpp was higher. For Dpp = 3 mm the average (±standard deviation (SD)) of TSA-pp and TSA-DC was reduced from 4.5 (± 2.5) and 2.5 (±0.6) °C, respectively, to 0.8 (± 0.7) and 0.09 (± 0.2) °C. This in vivo study showed the level of noise in PRFS-based thermometry introduced by respiratory motion in the context of MRgHIFU hyperthermia treatment for head and neck and the feasibility of reducing this noise using a multi-baseline technique.

Adaptive surrogate model based multi-objective transfer trajectory optimization between different libration points

NASA Astrophysics Data System (ADS)

Peng, Haijun; Wang, Wei

2016-10-01

An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.

Solving multi-objective optimization problems in conservation with the reference point method

PubMed Central

Dujardin, Yann; Chadès, Iadine

2018-01-01

Managing the biodiversity extinction crisis requires wise decision-making processes able to account for the limited resources available. In most decision problems in conservation biology, several conflicting objectives have to be taken into account. Most methods used in conservation either provide suboptimal solutions or use strong assumptions about the decision-maker’s preferences. Our paper reviews some of the existing approaches to solve multi-objective decision problems and presents new multi-objective linear programming formulations of two multi-objective optimization problems in conservation, allowing the use of a reference point approach. Reference point approaches solve multi-objective optimization problems by interactively representing the preferences of the decision-maker with a point in the criteria (objectives) space, called the reference point. We modelled and solved the following two problems in conservation: a dynamic multi-species management problem under uncertainty and a spatial allocation resource management problem. Results show that the reference point method outperforms classic methods while illustrating the use of an interactive methodology for solving combinatorial problems with multiple objectives. The method is general and can be adapted to a wide range of ecological combinatorial problems. PMID:29293650

The AOLI Non-Linear Curvature Wavefront Sensor: High sensitivity reconstruction for low-order AO

NASA Astrophysics Data System (ADS)

Crass, Jonathan; King, David; Mackay, Craig

2013-12-01

Many adaptive optics (AO) systems in use today require bright reference objects to determine the effects of atmospheric distortions on incoming wavefronts. This requirement is because Shack Hartmann wavefront sensors (SHWFS) distribute incoming light from reference objects into a large number of sub-apertures. Bright natural reference objects occur infrequently across the sky leading to the use of laser guide stars which add complexity to wavefront measurement systems. The non-linear curvature wavefront sensor as described by Guyon et al. has been shown to offer a significant increase in sensitivity when compared to a SHWFS. This facilitates much greater sky coverage using natural guide stars alone. This paper describes the current status of the non-linear curvature wavefront sensor being developed as part of an adaptive optics system for the Adaptive Optics Lucky Imager (AOLI) project. The sensor comprises two photon-counting EMCCD detectors from E2V Technologies, recording intensity at four near-pupil planes. These images are used with a reconstruction algorithm to determine the phase correction to be applied by an ALPAO 241-element deformable mirror. The overall system is intended to provide low-order correction for a Lucky Imaging based multi CCD imaging camera. We present the current optical design of the instrument including methods to minimise inherent optical effects, principally chromaticity. Wavefront reconstruction methods are discussed and strategies for their optimisation to run at the required real-time speeds are introduced. Finally, we discuss laboratory work with a demonstrator setup of the system.

Multiple Criteria Evaluation of Quality and Optimisation of e-Learning System Components

ERIC Educational Resources Information Center

Kurilovas, Eugenijus; Dagiene, Valentina

2010-01-01

The main research object of the paper is investigation and proposal of the comprehensive Learning Object Repositories (LORs) quality evaluation tool suitable for their multiple criteria decision analysis, evaluation and optimisation. Both LORs "internal quality" and "quality in use" evaluation (decision making) criteria are analysed in the paper.…

Image charge multi-role and function detectors

NASA Astrophysics Data System (ADS)

Milnes, James; Lapington, Jon S.; Jagutzki, Ottmar; Howorth, Jon

2009-06-01

The image charge technique used with microchannel plate imaging tubes provides several operational and practical benefits by serving to isolate the electronic image readout from the detector. The simple dielectric interface between detector and readout provides vacuum isolation and no vacuum electrical feed-throughs are required. Since the readout is mechanically separate from the detector, an image tube of generic design can be simply optimised for various applications by attaching it to different readout devices and electronics. We present imaging performance results using a single image tube with a variety of readout devices suited to differing applications: (a) A four electrode charge division tetra wedge anode, optimised for best spatial resolution in photon counting mode. (b) A cross delay line anode, enabling higher count rate, and the possibility of discriminating near co-incident events, and an event timing resolution of better than 1 ns. (c) A multi-anode readout connected, either to a multi-channel oscilloscope for analogue measurements of fast optical pulses, or alternately, to a multi-channel time correlated single photon counting (TCSPC) card.

Selecting a climate model subset to optimise key ensemble properties

NASA Astrophysics Data System (ADS)

Herger, Nadja; Abramowitz, Gab; Knutti, Reto; Angélil, Oliver; Lehmann, Karsten; Sanderson, Benjamin M.

2018-02-01

End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

Using Optimisation Techniques to Granulise Rough Set Partitions

NASA Astrophysics Data System (ADS)

Crossingham, Bodie; Marwala, Tshilidzi

2007-11-01

This paper presents an approach to optimise rough set partition sizes using various optimisation techniques. Three optimisation techniques are implemented to perform the granularisation process, namely, genetic algorithm (GA), hill climbing (HC) and simulated annealing (SA). These optimisation methods maximise the classification accuracy of the rough sets. The proposed rough set partition method is tested on a set of demographic properties of individuals obtained from the South African antenatal survey. The three techniques are compared in terms of their computational time, accuracy and number of rules produced when applied to the Human Immunodeficiency Virus (HIV) data set. The optimised methods results are compared to a well known non-optimised discretisation method, equal-width-bin partitioning (EWB). The accuracies achieved after optimising the partitions using GA, HC and SA are 66.89%, 65.84% and 65.48% respectively, compared to the accuracy of EWB of 59.86%. In addition to rough sets providing the plausabilities of the estimated HIV status, they also provide the linguistic rules describing how the demographic parameters drive the risk of HIV.

Optimal maintenance policy incorporating system level and unit level for mechanical systems

NASA Astrophysics Data System (ADS)

Duan, Chaoqun; Deng, Chao; Wang, Bingran

2018-04-01

The study works on a multi-level maintenance policy combining system level and unit level under soft and hard failure modes. The system experiences system-level preventive maintenance (SLPM) when the conditional reliability of entire system exceeds SLPM threshold, and also undergoes a two-level maintenance for each single unit, which is initiated when a single unit exceeds its preventive maintenance (PM) threshold, and the other is performed simultaneously the moment when any unit is going for maintenance. The units experience both periodic inspections and aperiodic inspections provided by failures of hard-type units. To model the practical situations, two types of economic dependence have been taken into account, which are set-up cost dependence and maintenance expertise dependence due to the same technology and tool/equipment can be utilised. The optimisation problem is formulated and solved in a semi-Markov decision process framework. The objective is to find the optimal system-level threshold and unit-level thresholds by minimising the long-run expected average cost per unit time. A formula for the mean residual life is derived for the proposed multi-level maintenance policy. The method is illustrated by a real case study of feed subsystem from a boring machine, and a comparison with other policies demonstrates the effectiveness of our approach.

Minimum Colour Differences Required To Recognise Small Objects On A Colour CRT

NASA Astrophysics Data System (ADS)

Phillips, Peter L.

1985-05-01

Data is required to assist in the assessment, evaluation and optimisation of colour and other displays for both military and general use. A general aim is to develop a mathematical technique to aid optimisation and reduce the amount of expensive hardware development and trials necessary when introducing new displays. The present standards and methods available for evaluating colour differences are known not to apply to the perception of typical objects on a display. Data is required for irregular objects viewed at small angular subtense ((1°) and relating the recognition of form rather than colour matching. Therefore laboratory experiments have been carried out using a computer controlled CRT to measure the threshold colour difference that an observer requires between object and background so that he can discriminate a variety of similar objects. Measurements are included for a variety of background and object colourings. The results are presented in the CIE colorimetric system similar to current standards used by the display engineer. Apart from the characteristic small field tritanopia, the results show that larger colour differences are required for object recognition than those assumed from conventional colour discrimination data. A simple relationship to account for object size and background colour is suggested to aid visual performance assessments and modelling.

Cross-Domain Multi-View Object Retrieval via Multi-Scale Topic Models.

PubMed

Hong, Richang; Hu, Zhenzhen; Wang, Ruxin; Wang, Meng; Tao, Dacheng

2016-09-27

The increasing number of 3D objects in various applications has increased the requirement for effective and efficient 3D object retrieval methods, which attracted extensive research efforts in recent years. Existing works mainly focus on how to extract features and conduct object matching. With the increasing applications, 3D objects come from different areas. In such circumstances, how to conduct object retrieval becomes more important. To address this issue, we propose a multi-view object retrieval method using multi-scale topic models in this paper. In our method, multiple views are first extracted from each object, and then the dense visual features are extracted to represent each view. To represent the 3D object, multi-scale topic models are employed to extract the hidden relationship among these features with respected to varied topic numbers in the topic model. In this way, each object can be represented by a set of bag of topics. To compare the objects, we first conduct topic clustering for the basic topics from two datasets, and then generate the common topic dictionary for new representation. Then, the two objects can be aligned to the same common feature space for comparison. To evaluate the performance of the proposed method, experiments are conducted on two datasets. The 3D object retrieval experimental results and comparison with existing methods demonstrate the effectiveness of the proposed method.

Multi-objective optimization of a continuous bio-dissimilation process of glycerol to 1, 3-propanediol.

PubMed

Xu, Gongxian; Liu, Ying; Gao, Qunwang

2016-02-10

This paper deals with multi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. In order to maximize the production rate of 1, 3-propanediol, maximize the conversion rate of glycerol to 1, 3-propanediol, maximize the conversion rate of glycerol, and minimize the concentration of by-product ethanol, we first propose six new multi-objective optimization models that can simultaneously optimize any two of the four objectives above. Then these multi-objective optimization problems are solved by using the weighted-sum and normal-boundary intersection methods respectively. Both the Pareto filter algorithm and removal criteria are used to remove those non-Pareto optimal points obtained by the normal-boundary intersection method. The results show that the normal-boundary intersection method can successfully obtain the approximate Pareto optimal sets of all the proposed multi-objective optimization problems, while the weighted-sum approach cannot achieve the overall Pareto optimal solutions of some multi-objective problems. Copyright © 2015 Elsevier B.V. All rights reserved.

Ultra-trace determination of plutonium in marine samples using multi-collector inductively coupled plasma mass spectrometry.

PubMed

Lindahl, Patric; Keith-Roach, Miranda; Worsfold, Paul; Choi, Min-Seok; Shin, Hyung-Seon; Lee, Sang-Hoon

2010-06-25

Sources of plutonium isotopes to the marine environment are well defined, both spatially and temporally, which makes Pu a potential tracer for oceanic processes. This paper presents the selection, optimisation and validation of a sample preparation method for the ultra-trace determination of Pu isotopes ((240)Pu and (239)Pu) in marine samples by multi-collector (MC) ICP-MS. The method was optimised for the removal of the interference from (238)U and the chemical recovery of Pu. Comparison of various separation strategies using AG1-X8, TEVA, TRU, and UTEVA resins to determine Pu in marine calcium carbonate samples is reported. A combination of anion-exchange (AG1-X8) and extraction chromatography (UTEVA/TRU) was the most suitable, with a radiochemical Pu yield of 87+/-5% and a U decontamination factor of 1.2 x 10(4). Validation of the method was accomplished by determining Pu in various IAEA certified marine reference materials. The estimated MC-ICP-MS instrumental limit of detection for (239)Pu and (240)Pu was 0.02 fg mL(-1), with an absolute limit of quantification of 0.11 fg. The proposed method allows the determination of ultra-trace Pu, at femtogram levels, in small size marine samples (e.g., 0.6-2.0 g coral or 15-20 L seawater). Finally, the analytical method was applied to determining historical records of the Pu signature in coral samples from the tropical Northwest Pacific and (239+240)Pu concentrations and (240)Pu/(239)Pu atom ratios in seawater samples as part of the 2008 GEOTRACES intercalibration exercise. Copyright 2010 Elsevier B.V. All rights reserved.

Automatic trajectory planning for low-thrust active removal mission in low-earth orbit

NASA Astrophysics Data System (ADS)

Di Carlo, Marilena; Romero Martin, Juan Manuel; Vasile, Massimiliano

2017-03-01

In this paper two strategies are proposed to de-orbit up to 10 non-cooperative objects per year from the region within 800 and 1400 km altitude in Low Earth Orbit (LEO). The underlying idea is to use a single servicing spacecraft to de-orbit several objects applying two different approaches. The first strategy is analogous to the Traveling Salesman Problem: the servicing spacecraft rendezvous with multiple objects in order to physically attach a de-orbiting kit that reduces the perigee of the orbit. The second strategy is analogous to the Vehicle Routing Problem: the servicing spacecraft rendezvous and docks with an object, spirals it down to a lower altitude orbit, undocks, and then spirals up to the next target. In order to maximise the number of de-orbited objects with minimum propellant consumption, an optimal sequence of targets is identified using a bio-inspired incremental automatic planning and scheduling discrete optimisation algorithm. The optimisation of the resulting sequence is realised using a direct transcription method based on an asymptotic analytical solution of the perturbed Keplerian motion. The analytical model takes into account the perturbations deriving from the J2 gravitational effect and the atmospheric drag.

Multi-agent modelling framework for water, energy and other resource networks

NASA Astrophysics Data System (ADS)

Knox, S.; Selby, P. D.; Meier, P.; Harou, J. J.; Yoon, J.; Lachaut, T.; Klassert, C. J. A.; Avisse, N.; Mohamed, K.; Tomlinson, J.; Khadem, M.; Tilmant, A.; Gorelick, S.

2015-12-01

Bespoke modelling tools are often needed when planning future engineered interventions in the context of various climate, socio-economic and geopolitical futures. Such tools can help improve system operating policies or assess infrastructure upgrades and their risks. A frequently used approach is to simulate and/or optimise the impact of interventions in engineered systems. Modelling complex infrastructure systems can involve incorporating multiple aspects into a single model, for example physical, economic and political. This presents the challenge of combining research from diverse areas into a single system effectively. We present the Pynsim 'Python Network Simulator' framework, a library for building simulation models capable of representing, the physical, institutional and economic aspects of an engineered resources system. Pynsim is an open source, object oriented code aiming to promote integration of different modelling processes through a single code library. We present two case studies that demonstrate important features of Pynsim's design. The first is a large interdisciplinary project of a national water system in the Middle East with modellers from fields including water resources, economics, hydrology and geography each considering different facets of a multi agent system. It includes: modelling water supply and demand for households and farms; a water tanker market with transfer of water between farms and households, and policy decisions made by government institutions at district, national and international level. This study demonstrates that a well-structured library of code can provide a hub for development and act as a catalyst for integrating models. The second focuses on optimising the location of new run-of-river hydropower plants. Using a multi-objective evolutionary algorithm, this study analyses different network configurations to identify the optimal placement of new power plants within a river network. This demonstrates that Pynsim can be used to evaluate a multitude of topologies for identifying the optimal location of infrastructure investments. Pynsim is available on GitHub or via standard python installer packages such as pip. It comes with several examples and online documentation, making it attractive for those less experienced in software engineering.

Distributed learning and multi-objectivity in traffic light control

NASA Astrophysics Data System (ADS)

Brys, Tim; Pham, Tong T.; Taylor, Matthew E.

2014-01-01

Traffic jams and suboptimal traffic flows are ubiquitous in modern societies, and they create enormous economic losses each year. Delays at traffic lights alone account for roughly 10% of all delays in US traffic. As most traffic light scheduling systems currently in use are static, set up by human experts rather than being adaptive, the interest in machine learning approaches to this problem has increased in recent years. Reinforcement learning (RL) approaches are often used in these studies, as they require little pre-existing knowledge about traffic flows. Distributed constraint optimisation approaches (DCOP) have also been shown to be successful, but are limited to cases where the traffic flows are known. The distributed coordination of exploration and exploitation (DCEE) framework was recently proposed to introduce learning in the DCOP framework. In this paper, we present a study of DCEE and RL techniques in a complex simulator, illustrating the particular advantages of each, comparing them against standard isolated traffic actuated signals. We analyse how learning and coordination behave under different traffic conditions, and discuss the multi-objective nature of the problem. Finally we evaluate several alternative reward signals in the best performing approach, some of these taking advantage of the correlation between the problem-inherent objectives to improve performance.

An enhanced multi-view vertical line locus matching algorithm of object space ground primitives based on positioning consistency for aerial and space images

NASA Astrophysics Data System (ADS)

Zhang, Ka; Sheng, Yehua; Wang, Meizhen; Fu, Suxia

2018-05-01

The traditional multi-view vertical line locus (TMVLL) matching method is an object-space-based method that is commonly used to directly acquire spatial 3D coordinates of ground objects in photogrammetry. However, the TMVLL method can only obtain one elevation and lacks an accurate means of validating the matching results. In this paper, we propose an enhanced multi-view vertical line locus (EMVLL) matching algorithm based on positioning consistency for aerial or space images. The algorithm involves three components: confirming candidate pixels of the ground primitive in the base image, multi-view image matching based on the object space constraints for all candidate pixels, and validating the consistency of the object space coordinates with the multi-view matching result. The proposed algorithm was tested using actual aerial images and space images. Experimental results show that the EMVLL method successfully solves the problems associated with the TMVLL method, and has greater reliability, accuracy and computing efficiency.

Improved NSGA model for multi objective operation scheduling and its evaluation

NASA Astrophysics Data System (ADS)

Li, Weining; Wang, Fuyu

2017-09-01

Reasonable operation can increase the income of the hospital and improve the patient’s satisfactory level. In this paper, by using multi object operation scheduling method with improved NSGA algorithm, it shortens the operation time, reduces the operation costand lowers the operation risk, the multi-objective optimization model is established for flexible operation scheduling, through the MATLAB simulation method, the Pareto solution is obtained, the standardization of data processing. The optimal scheduling scheme is selected by using entropy weight -Topsis combination method. The results show that the algorithm is feasible to solve the multi-objective operation scheduling problem, and provide a reference for hospital operation scheduling.

Design optimisation of a TOF-based collimated camera prototype for online hadrontherapy monitoring

NASA Astrophysics Data System (ADS)

Pinto, M.; Dauvergne, D.; Freud, N.; Krimmer, J.; Letang, J. M.; Ray, C.; Roellinghoff, F.; Testa, E.

2014-12-01

Hadrontherapy is an innovative radiation therapy modality for which one of the main key advantages is the target conformality allowed by the physical properties of ion species. However, in order to maximise the exploitation of its potentialities, online monitoring is required in order to assert the treatment quality, namely monitoring devices relying on the detection of secondary radiations. Herein is presented a method based on Monte Carlo simulations to optimise a multi-slit collimated camera employing time-of-flight selection of prompt-gamma rays to be used in a clinical scenario. In addition, an analytical tool is developed based on the Monte Carlo data to predict the expected precision for a given geometrical configuration. Such a method follows the clinical workflow requirements to simultaneously have a solution that is relatively accurate and fast. Two different camera designs are proposed, considering different endpoints based on the trade-off between camera detection efficiency and spatial resolution to be used in a proton therapy treatment with active dose delivery and assuming a homogeneous target.

Can generic knee joint models improve the measurement of osteoarthritic knee kinematics during squatting activity?

PubMed

Clément, Julien; Dumas, Raphaël; Hagemeister, Nicola; de Guise, Jaques A

2017-01-01

Knee joint kinematics derived from multi-body optimisation (MBO) still requires evaluation. The objective of this study was to corroborate model-derived kinematics of osteoarthritic knees obtained using four generic knee joint models used in musculoskeletal modelling - spherical, hinge, degree-of-freedom coupling curves and parallel mechanism - against reference knee kinematics measured by stereo-radiography. Root mean square errors ranged from 0.7° to 23.4° for knee rotations and from 0.6 to 9.0 mm for knee displacements. Model-derived knee kinematics computed from generic knee joint models was inaccurate. Future developments and experiments should improve the reliability of osteoarthritic knee models in MBO and musculoskeletal modelling.

Optimization of a multi-channel parabolic guide for the material science diffractometer STRESS-SPEC at FRM II

NASA Astrophysics Data System (ADS)

Rebelo Kornmeier, Joana; Ostermann, Andreas; Hofmann, Michael; Gibmeier, Jens

2014-02-01

Neutron strain diffractometers usually use slits to define a gauge volume within engineering samples. In this study a multi-channel parabolic neutron guide was developed to be used instead of the primary slit to minimise the loss of intensity and vertical definition of the gauge volume when using slits placed far away from the measurement position in bulky components. The major advantage of a focusing guide is that the maximum flux is not at the exit of the guide as for a slit system but at the focal point relatively far away from the exit of the guide. Monte Carlo simulations were used to optimise the multi-channel parabolic guide with respect to the instrument characteristics of the diffractometer STRESS-SPEC at the FRM II neutron source. Also the simulations are in excellent agreement with experimental measurements using the optimised multi-channel parabolic guide at the neutron diffractometer. In addition the performance of the guide was compared to the standard slit setup at STRESS-SPEC using a single bead weld sample used in earlier round robin tests for residual strain measurements.

Strategic optimisation of microgrid by evolving a unitised regenerative fuel cell system operational criterion

NASA Astrophysics Data System (ADS)

Bhansali, Gaurav; Singh, Bhanu Pratap; Kumar, Rajesh

2016-09-01

In this paper, the problem of microgrid optimisation with storage has been addressed in an unaccounted way rather than confining it to loss minimisation. Unitised regenerative fuel cell (URFC) systems have been studied and employed in microgrids to store energy and feed it back into the system when required. A value function-dependent on line losses, URFC system operational cost and stored energy at the end of the day are defined here. The function is highly complex, nonlinear and multi dimensional in nature. Therefore, heuristic optimisation techniques in combination with load flow analysis are used here to resolve the network and time domain complexity related with the problem. Particle swarm optimisation with the forward/backward sweep algorithm ensures optimal operation of microgrid thereby minimising the operational cost of the microgrid. Results are shown and are found to be consistently improving with evolution of the solution strategy.

Multi-Objective Programming for Lot-Sizing with Quantity Discount

NASA Astrophysics Data System (ADS)

Kang, He-Yau; Lee, Amy H. I.; Lai, Chun-Mei; Kang, Mei-Sung

2011-11-01

Multi-objective programming (MOP) is one of the popular methods for decision making in a complex environment. In a MOP, decision makers try to optimize two or more objectives simultaneously under various constraints. A complete optimal solution seldom exists, and a Pareto-optimal solution is usually used. Some methods, such as the weighting method which assigns priorities to the objectives and sets aspiration levels for the objectives, are used to derive a compromise solution. The ɛ-constraint method is a modified weight method. One of the objective functions is optimized while the other objective functions are treated as constraints and are incorporated in the constraint part of the model. This research considers a stochastic lot-sizing problem with multi-suppliers and quantity discounts. The model is transformed into a mixed integer programming (MIP) model next based on the ɛ-constraint method. An illustrative example is used to illustrate the practicality of the proposed model. The results demonstrate that the model is an effective and accurate tool for determining the replenishment of a manufacturer from multiple suppliers for multi-periods.

An enhanced multi-channel bacterial foraging optimization algorithm for MIMO communication system

NASA Astrophysics Data System (ADS)

Palanimuthu, Senthilkumar Jayalakshmi; Muthial, Chandrasekaran

2017-04-01

Channel estimation and optimisation are the main challenging tasks in Multi Input Multi Output (MIMO) wireless communication systems. In this work, a Multi-Channel Bacterial Foraging Optimization Algorithm approach is proposed for the selection of antenna in a transmission area. The main advantage of this method is, it reduces the loss of bandwidth during data transmission effectively. Here, we considered the channel estimation and optimisation for improving the transmission speed and reducing the unused bandwidth. Initially, the message is given to the input of the communication system. Then, the symbol mapping process is performed for converting the message into signals. It will be encoded based on the space-time encoding technique. Here, the single signal is divided into multiple signals and it will be given to the input of space-time precoder. Hence, the multiplexing is applied to transmission channel estimation. In this paper, the Rayleigh channel is selected based on the bandwidth range. This is the Gaussian distribution type channel. Then, the demultiplexing is applied on the obtained signal that is the reverse function of multiplexing, which splits the combined signal arriving from a medium into the original information signal. Furthermore, the long-term evolution technique is used for scheduling the time to channels during transmission. Here, the hidden Markov model technique is employed to predict the status information of the channel. Finally, the signals are decoded and the reconstructed signal is obtained after performing the scheduling process. The experimental results evaluate the performance of the proposed MIMO communication system in terms of bit error rate, mean squared error, average throughput, outage capacity and signal to interference noise ratio.

Dual ant colony operational modal analysis parameter estimation method

NASA Astrophysics Data System (ADS)

Sitarz, Piotr; Powałka, Bartosz

2018-01-01

Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

NASA Astrophysics Data System (ADS)

Chu, J.; Zhang, C.; Fu, G.; Li, Y.; Zhou, H.

2015-08-01

This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

A multi-objective model for sustainable recycling of municipal solid waste.

PubMed

Mirdar Harijani, Ali; Mansour, Saeed; Karimi, Behrooz

2017-04-01

The efficient management of municipal solid waste is a major problem for large and populated cities. In many countries, the majority of municipal solid waste is landfilled or dumped owing to an inefficient waste management system. Therefore, an optimal and sustainable waste management strategy is needed. This study introduces a recycling and disposal network for sustainable utilisation of municipal solid waste. In order to optimise the network, we develop a multi-objective mixed integer linear programming model in which the economic, environmental and social dimensions of sustainability are concurrently balanced. The model is able to: select the best combination of waste treatment facilities; specify the type, location and capacity of waste treatment facilities; determine the allocation of waste to facilities; consider the transportation of waste and distribution of processed products; maximise the profit of the system; minimise the environmental footprint; maximise the social impacts of the system; and eventually generate an optimal and sustainable configuration for municipal solid waste management. The proposed methodology could be applied to any region around the world. Here, the city of Tehran, Iran, is presented as a real case study to show the applicability of the methodology.

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

PubMed

Bae, Seung-Hwan; Yoon, Kuk-Jin

2018-03-01

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

Multidisciplinary design optimisation of a recurve bow based on applications of the autogenetic design theory and distributed computing

NASA Astrophysics Data System (ADS)

Fritzsche, Matthias; Kittel, Konstantin; Blankenburg, Alexander; Vajna, Sándor

2012-08-01

The focus of this paper is to present a method of multidisciplinary design optimisation based on the autogenetic design theory (ADT) that provides methods, which are partially implemented in the optimisation software described here. The main thesis of the ADT is that biological evolution and the process of developing products are mainly similar, i.e. procedures from biological evolution can be transferred into product development. In order to fulfil requirements and boundary conditions of any kind (that may change at any time), both biological evolution and product development look for appropriate solution possibilities in a certain area, and try to optimise those that are actually promising by varying parameters and combinations of these solutions. As the time necessary for multidisciplinary design optimisations is a critical aspect in product development, ways to distribute the optimisation process with the effective use of unused calculating capacity, can reduce the optimisation time drastically. Finally, a practical example shows how ADT methods and distributed optimising are applied to improve a product.

Integration of Monte-Carlo ray tracing with a stochastic optimisation method: application to the design of solar receiver geometry.

PubMed

Asselineau, Charles-Alexis; Zapata, Jose; Pye, John

2015-06-01

A stochastic optimisation method adapted to illumination and radiative heat transfer problems involving Monte-Carlo ray-tracing is presented. A solar receiver shape optimisation case study illustrates the advantages of the method and its potential: efficient receivers are identified using a moderate computational cost.

A Novel Sky-Subtraction Method Based on Non-negative Matrix Factorisation with Sparsity for Multi-object Fibre Spectroscopy

NASA Astrophysics Data System (ADS)

Zhang, Bo; Zhang, Long; Ye, Zhongfu

2016-12-01

A novel sky-subtraction method based on non-negative matrix factorisation with sparsity is proposed in this paper. The proposed non-negative matrix factorisation with sparsity method is redesigned for sky-subtraction considering the characteristics of the skylights. It has two constraint terms, one for sparsity and the other for homogeneity. Different from the standard sky-subtraction techniques, such as the B-spline curve fitting methods and the Principal Components Analysis approaches, sky-subtraction based on non-negative matrix factorisation with sparsity method has higher accuracy and flexibility. The non-negative matrix factorisation with sparsity method has research value for the sky-subtraction on multi-object fibre spectroscopic telescope surveys. To demonstrate the effectiveness and superiority of the proposed algorithm, experiments are performed on Large Sky Area Multi-Object Fiber Spectroscopic Telescope data, as the mechanisms of the multi-object fibre spectroscopic telescopes are similar.

Tail mean and related robust solution concepts

NASA Astrophysics Data System (ADS)

Ogryczak, Włodzimierz

2014-01-01

Robust optimisation might be viewed as a multicriteria optimisation problem where objectives correspond to the scenarios although their probabilities are unknown or imprecise. The simplest robust solution concept represents a conservative approach focused on the worst-case scenario results optimisation. A softer concept allows one to optimise the tail mean thus combining performances under multiple worst scenarios. We show that while considering robust models allowing the probabilities to vary only within given intervals, the tail mean represents the robust solution for only upper bounded probabilities. For any arbitrary intervals of probabilities the corresponding robust solution may be expressed by the optimisation of appropriately combined mean and tail mean criteria thus remaining easily implementable with auxiliary linear inequalities. Moreover, we use the tail mean concept to develope linear programming implementable robust solution concepts related to risk averse optimisation criteria.

OPTHYLIC: An Optimised Tool for Hybrid Limits Computation

NASA Astrophysics Data System (ADS)

Busato, Emmanuel; Calvet, David; Theveneaux-Pelzer, Timothée

2018-05-01

A software tool, computing observed and expected upper limits on Poissonian process rates using a hybrid frequentist-Bayesian CLs method, is presented. This tool can be used for simple counting experiments where only signal, background and observed yields are provided or for multi-bin experiments where binned distributions of discriminating variables are provided. It allows the combination of several channels and takes into account statistical and systematic uncertainties, as well as correlations of systematic uncertainties between channels. It has been validated against other software tools and analytical calculations, for several realistic cases.

An improved PSO-SVM model for online recognition defects in eddy current testing

NASA Astrophysics Data System (ADS)

Liu, Baoling; Hou, Dibo; Huang, Pingjie; Liu, Banteng; Tang, Huayi; Zhang, Wubo; Chen, Peihua; Zhang, Guangxin

2013-12-01

Accurate and rapid recognition of defects is essential for structural integrity and health monitoring of in-service device using eddy current (EC) non-destructive testing. This paper introduces a novel model-free method that includes three main modules: a signal pre-processing module, a classifier module and an optimisation module. In the signal pre-processing module, a kind of two-stage differential structure is proposed to suppress the lift-off fluctuation that could contaminate the EC signal. In the classifier module, multi-class support vector machine (SVM) based on one-against-one strategy is utilised for its good accuracy. In the optimisation module, the optimal parameters of classifier are obtained by an improved particle swarm optimisation (IPSO) algorithm. The proposed IPSO technique can improve convergence performance of the primary PSO through the following strategies: nonlinear processing of inertia weight, introductions of the black hole and simulated annealing model with extremum disturbance. The good generalisation ability of the IPSO-SVM model has been validated through adding additional specimen into the testing set. Experiments show that the proposed algorithm can achieve higher recognition accuracy and efficiency than other well-known classifiers and the superiorities are more obvious with less training set, which contributes to online application.

Hydro-environmental management of groundwater resources: A fuzzy-based multi-objective compromise approach

NASA Astrophysics Data System (ADS)

Alizadeh, Mohammad Reza; Nikoo, Mohammad Reza; Rakhshandehroo, Gholam Reza

2017-08-01

Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

NASA Astrophysics Data System (ADS)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

Multi-Tasking Non-Destructive Laser Technology in Conservation Diagnostic Procedures

NASA Astrophysics Data System (ADS)

Tornari, V.; Tsiranidou, E.; Orphanos, Y.; Falldorf, C.; Klattenhof, R.; Esposito, E.; Agnani, A.; Dabu, R.; Stratan, A.; Anastassopoulos, A.; Schipper, D.; Hasperhoven, J.; Stefanaggi, M.; Bonnici, H.; Ursu, D.

Laser metrology provides techniques that have been successfully applied in industrial structural diagnostic fields but have not yet been refined and optimised for the special investigative requirements found in cultural heritage applications. A major impediment is the partial applicability of various optical coherent techniques, each one narrowing its use down to a specific application. This characteristic is not well suited for a field that encounters a great variety of diagnostic problems ranging from movable, multiple-composition museum objects, to immovable multi-layered wall paintings, statues and wood carvings, to monumental constructions and outdoor cultural heritage sites. Various diagnostic techniques have been suggested and are uniquely suited for each of the mentioned problems but it is this fragmented suitability that obstructs the technology transfer. Since optical coherent techniques for metrology are based on fundamental principles and take advantage of similar procedures for generation of informative signals for data collection, then the imposed limits elevate our aim to identify complementary capabilities to accomplish the needed functionality.

An effective pseudospectral method for constraint dynamic optimisation problems with characteristic times

NASA Astrophysics Data System (ADS)

Xiao, Long; Liu, Xinggao; Ma, Liang; Zhang, Zeyin

2018-03-01

Dynamic optimisation problem with characteristic times, widely existing in many areas, is one of the frontiers and hotspots of dynamic optimisation researches. This paper considers a class of dynamic optimisation problems with constraints that depend on the interior points either fixed or variable, where a novel direct pseudospectral method using Legendre-Gauss (LG) collocation points for solving these problems is presented. The formula for the state at the terminal time of each subdomain is derived, which results in a linear combination of the state at the LG points in the subdomains so as to avoid the complex nonlinear integral. The sensitivities of the state at the collocation points with respect to the variable characteristic times are derived to improve the efficiency of the method. Three well-known characteristic time dynamic optimisation problems are solved and compared in detail among the reported literature methods. The research results show the effectiveness of the proposed method.

Research a Novel Integrated and Dynamic Multi-object Trade-Off Mechanism in Software Project

NASA Astrophysics Data System (ADS)

Jiang, Weijin; Xu, Yuhui

Aiming at practical requirements of present software project management and control, the paper presented to construct integrated multi-object trade-off model based on software project process management, so as to actualize integrated and dynamic trade-oil of the multi-object system of project. Based on analyzing basic principle of dynamic controlling and integrated multi-object trade-off system process, the paper integrated method of cybernetics and network technology, through monitoring on some critical reference points according to the control objects, emphatically discussed the integrated and dynamic multi- object trade-off model and corresponding rules and mechanism in order to realize integration of process management and trade-off of multi-object system.

Joint kinematics estimation using a multi-body kinematics optimisation and an extended Kalman filter, and embedding a soft tissue artefact model.

PubMed

Bonnet, Vincent; Richard, Vincent; Camomilla, Valentina; Venture, Gentiane; Cappozzo, Aurelio; Dumas, Raphaël

2017-09-06

To reduce the impact of the soft tissue artefact (STA) on the estimate of skeletal movement using stereophotogrammetric and skin-marker data, multi-body kinematics optimisation (MKO) and extended Kalman filters (EKF) have been proposed. This paper assessed the feasibility and efficiency of these methods when they embed a mathematical model of the STA and simultaneously estimate the ankle, knee and hip joint kinematics and the model parameters. A STA model was used that provides an estimate of the STA affecting the marker-cluster located on a body segment as a function of the kinematics of the adjacent joints. The MKO and the EKF were implemented with and without the STA model. To assess these methods, intra-cortical pin and skin markers located on the thigh, shank, and foot of three subjects and tracked during the stance phase of running were used. Embedding the STA model in MKO and EKF reduced the average RMS of marker tracking from 12.6 to 1.6mm and from 4.3 to 1.9mm, respectively, showing that a STA model trial-specific calibration is feasible. Nevertheless, with the STA model embedded in MKO, the RMS difference between the estimated and the reference joint kinematics determined from the pin markers slightly increased (from 2.0 to 2.1deg) On the contrary, when the STA model was embedded in the EKF, this RMS difference was slightly reduced (from 2.0 to 1.7deg) thus showing a better potentiality of this method to attenuate STA effects and improve the accuracy of joint kinematics estimate. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets.

PubMed

Scharfe, Michael; Pielot, Rainer; Schreiber, Falk

2010-01-11

Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks. We evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from http://cbe.ipk-gatersleben.de. The results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics.

Fly ash-TiO2 nanocomposite material for multi-pollutants wastewater treatment.

PubMed

Visa, Maria; Andronic, Luminita; Duta, Anca

2015-03-01

This paper reports on the synthesis, characterization and adsorption properties of a novel nano-composite obtained using the hydrothermal method applied to a fly ash-TiO2 slurry and hexadecyltrimethyl-ammonium bromide, as surface controlling agent. The new adsorbent was investigated in terms of crystallinity (XRD), surface properties (AFM, SEM, and porosity and BET surface) and surface chemistry (EDX, FTIR). The nanocomposite's properties were sequentially tested in adsorption and photocatalysis processes applied to multi-pollutant synthetic wastewaters loaded with copper cations and two industrial dyes: the acid dye Bemacid Blau and the reactive dye Bemacid Rot; the nano-composite substrate allowed reaching high removal efficiencies, above 90%, both in adsorption and in photodegradation experiments, in optimised conditions. Copyright © 2014. Published by Elsevier Ltd.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

NASA Astrophysics Data System (ADS)

Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

Multi Robot Path Planning for Budgeted Active Perception with Self-Organising Maps

DTIC Science & Technology

2016-10-04

Multi- Robot Path Planning for Budgeted Active Perception with Self-Organising Maps Graeme Best1, Jan Faigl2 and Robert Fitch1 Abstract— We propose a...optimise paths for a multi- robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting...regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes

Improving multi-objective reservoir operation optimization with sensitivity-informed problem decomposition

NASA Astrophysics Data System (ADS)

Chu, J. G.; Zhang, C.; Fu, G. T.; Li, Y.; Zhou, H. C.

2015-04-01

This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce the computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed problem decomposition dramatically reduces the computational demands required for attaining high quality approximations of optimal tradeoff relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed problem decomposition and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform problem decomposition when solving the complex multi-objective reservoir operation problems.

Optimisation in radiotherapy. III: Stochastic optimisation algorithms and conclusions.

PubMed

Ebert, M

1997-12-01

This is the final article in a three part examination of optimisation in radiotherapy. Previous articles have established the bases and form of the radiotherapy optimisation problem, and examined certain types of optimisation algorithm, namely, those which perform some form of ordered search of the solution space (mathematical programming), and those which attempt to find the closest feasible solution to the inverse planning problem (deterministic inversion). The current paper examines algorithms which search the space of possible irradiation strategies by stochastic methods. The resulting iterative search methods move about the solution space by sampling random variates, which gradually become more constricted as the algorithm converges upon the optimal solution. This paper also discusses the implementation of optimisation in radiotherapy practice.

Improving target coverage and organ-at-risk sparing in intensity-modulated radiotherapy for cervical oesophageal cancer using a simple optimisation method.

PubMed

Lu, Jia-Yang; Cheung, Michael Lok-Man; Huang, Bao-Tian; Wu, Li-Li; Xie, Wen-Jia; Chen, Zhi-Jian; Li, De-Rui; Xie, Liang-Xi

2015-01-01

To assess the performance of a simple optimisation method for improving target coverage and organ-at-risk (OAR) sparing in intensity-modulated radiotherapy (IMRT) for cervical oesophageal cancer. For 20 selected patients, clinically acceptable original IMRT plans (Original plans) were created, and two optimisation methods were adopted to improve the plans: 1) a base dose function (BDF)-based method, in which the treatment plans were re-optimised based on the original plans, and 2) a dose-controlling structure (DCS)-based method, in which the original plans were re-optimised by assigning additional constraints for hot and cold spots. The Original, BDF-based and DCS-based plans were compared with regard to target dose homogeneity, conformity, OAR sparing, planning time and monitor units (MUs). Dosimetric verifications were performed and delivery times were recorded for the BDF-based and DCS-based plans. The BDF-based plans provided significantly superior dose homogeneity and conformity compared with both the DCS-based and Original plans. The BDF-based method further reduced the doses delivered to the OARs by approximately 1-3%. The re-optimisation time was reduced by approximately 28%, but the MUs and delivery time were slightly increased. All verification tests were passed and no significant differences were found. The BDF-based method for the optimisation of IMRT for cervical oesophageal cancer can achieve significantly better dose distributions with better planning efficiency at the expense of slightly more MUs.

Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

NASA Astrophysics Data System (ADS)

Omagari, Hiroki; Higashino, Shin-Ichiro

2018-04-01

In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

Integrative systems modeling and multi-objective optimization

EPA Science Inventory

This presentation presents a number of algorithms, tools, and methods for utilizing multi-objective optimization within integrated systems modeling frameworks. We first present innovative methods using a genetic algorithm to optimally calibrate the VELMA and SWAT ecohydrological ...

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Design of optimised backstepping controller for the synchronisation of chaotic Colpitts oscillator using shark smell algorithm

NASA Astrophysics Data System (ADS)

Fouladi, Ehsan; Mojallali, Hamed

2018-01-01

In this paper, an adaptive backstepping controller has been tuned to synchronise two chaotic Colpitts oscillators in a master-slave configuration. The parameters of the controller are determined using shark smell optimisation (SSO) algorithm. Numerical results are presented and compared with those of particle swarm optimisation (PSO) algorithm. Simulation results show better performance in terms of accuracy and convergence for the proposed optimised method compared to PSO optimised controller or any non-optimised backstepping controller.

Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

PubMed

Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

2017-03-21

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

Multi-object segmentation using coupled nonparametric shape and relative pose priors

NASA Astrophysics Data System (ADS)

Uzunbas, Mustafa Gökhan; Soldea, Octavian; Çetin, Müjdat; Ünal, Gözde; Erçil, Aytül; Unay, Devrim; Ekin, Ahmet; Firat, Zeynep

2009-02-01

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

NASA Astrophysics Data System (ADS)

Demirel, Mehmet C.; Mai, Juliane; Mendiguren, Gorka; Koch, Julian; Samaniego, Luis; Stisen, Simon

2018-02-01

Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

A novel global Harmony Search method based on Ant Colony Optimisation algorithm

NASA Astrophysics Data System (ADS)

Fouad, Allouani; Boukhetala, Djamel; Boudjema, Fares; Zenger, Kai; Gao, Xiao-Zhi

2016-03-01

The Global-best Harmony Search (GHS) is a stochastic optimisation algorithm recently developed, which hybridises the Harmony Search (HS) method with the concept of swarm intelligence in the particle swarm optimisation (PSO) to enhance its performance. In this article, a new optimisation algorithm called GHSACO is developed by incorporating the GHS with the Ant Colony Optimisation algorithm (ACO). Our method introduces a novel improvisation process, which is different from that of the GHS in the following aspects. (i) A modified harmony memory (HM) representation and conception. (ii) The use of a global random switching mechanism to monitor the choice between the ACO and GHS. (iii) An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The proposed GHSACO algorithm has been applied to various benchmark functions and constrained optimisation problems. Simulation results demonstrate that it can find significantly better solutions when compared with the original HS and some of its variants.

Optimisation of flight dynamic control based on many-objectives meta-heuristic: a comparative study

NASA Astrophysics Data System (ADS)

Bureerat, Sujin; Pholdee, Nantiwat; Radpukdee, Thana

2018-05-01

Development of many objective meta-heuristics (MnMHs) is a currently interesting topic as they are suitable to real applications of optimisation problems which usually require many ob-jectives. However, most of MnMHs have been mostly developed and tested based on stand-ard testing functions while the use of MnMHs to real applications is rare. Therefore, in this work, MnMHs are applied for optimisation design of flight dynamic control. The design prob-lem is posed to find control gains for minimising; the control effort, the spiral root, the damp-ing in roll root, sideslip angle deviation, and maximising; the damping ratio of the dutch-roll complex pair, the dutch-roll frequency, bank angle at pre-specified times 1 seconds and 2.8 second subjected to several constraints based on Military Specifications (1969) requirement. Several established many-objective meta-heuristics (MnMHs) are used to solve the problem while their performances are compared. With this research work, performance of several MnMHs for flight control is investigated. The results obtained will be the baseline for future development of flight dynamic and control.

EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

PubMed

Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos

2015-01-01

Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

Combinatorial Optimization in Project Selection Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Dewi, Sari; Sawaluddin

2018-01-01

This paper discusses the problem of project selection in the presence of two objective functions that maximize profit and minimize cost and the existence of some limitations is limited resources availability and time available so that there is need allocation of resources in each project. These resources are human resources, machine resources, raw material resources. This is treated as a consideration to not exceed the budget that has been determined. So that can be formulated mathematics for objective function (multi-objective) with boundaries that fulfilled. To assist the project selection process, a multi-objective combinatorial optimization approach is used to obtain an optimal solution for the selection of the right project. It then described a multi-objective method of genetic algorithm as one method of multi-objective combinatorial optimization approach to simplify the project selection process in a large scope.

A New Multiconstraint Method for Determining the Optimal Cable Stresses in Cable-Stayed Bridges

PubMed Central

Asgari, B.; Osman, S. A.; Adnan, A.

2014-01-01

Cable-stayed bridges are one of the most popular types of long-span bridges. The structural behaviour of cable-stayed bridges is sensitive to the load distribution between the girder, pylons, and cables. The determination of pretensioning cable stresses is critical in the cable-stayed bridge design procedure. By finding the optimum stresses in cables, the load and moment distribution of the bridge can be improved. In recent years, different research works have studied iterative and modern methods to find optimum stresses of cables. However, most of the proposed methods have limitations in optimising the structural performance of cable-stayed bridges. This paper presents a multiconstraint optimisation method to specify the optimum cable forces in cable-stayed bridges. The proposed optimisation method produces less bending moments and stresses in the bridge members and requires shorter simulation time than other proposed methods. The results of comparative study show that the proposed method is more successful in restricting the deck and pylon displacements and providing uniform deck moment distribution than unit load method (ULM). The final design of cable-stayed bridges can be optimised considerably through proposed multiconstraint optimisation method. PMID:25050400

A new multiconstraint method for determining the optimal cable stresses in cable-stayed bridges.

PubMed

Asgari, B; Osman, S A; Adnan, A

2014-01-01

Cable-stayed bridges are one of the most popular types of long-span bridges. The structural behaviour of cable-stayed bridges is sensitive to the load distribution between the girder, pylons, and cables. The determination of pretensioning cable stresses is critical in the cable-stayed bridge design procedure. By finding the optimum stresses in cables, the load and moment distribution of the bridge can be improved. In recent years, different research works have studied iterative and modern methods to find optimum stresses of cables. However, most of the proposed methods have limitations in optimising the structural performance of cable-stayed bridges. This paper presents a multiconstraint optimisation method to specify the optimum cable forces in cable-stayed bridges. The proposed optimisation method produces less bending moments and stresses in the bridge members and requires shorter simulation time than other proposed methods. The results of comparative study show that the proposed method is more successful in restricting the deck and pylon displacements and providing uniform deck moment distribution than unit load method (ULM). The final design of cable-stayed bridges can be optimised considerably through proposed multiconstraint optimisation method.

Development and optimisation of atorvastatin calcium loaded self-nanoemulsifying drug delivery system (SNEDDS) for enhancing oral bioavailability: in vitro and in vivo evaluation.

PubMed

Kassem, Abdulsalam M; Ibrahim, Hany M; Samy, Ahmed M

2017-05-01

The objective of this study was to develop and optimise self-nanoemulsifying drug delivery system (SNEDDS) of atorvastatin calcium (ATC) for improving dissolution rate and eventually oral bioavailability. Ternary phase diagrams were constructed on basis of solubility and emulsification studies. The composition of ATC-SNEDDS was optimised using the Box-Behnken optimisation design. Optimised ATC-SNEDDS was characterised for various physicochemical properties. Pharmacokinetic, pharmacodynamic and histological findings were performed in rats. Optimised ATC-SNEDDS resulted in droplets size of 5.66 nm, zeta potential of -19.52 mV, t 90 of 5.43 min and completely released ATC within 30 min irrespective of pH of the medium. Area under the curve of optimised ATC-SNEDDS in rats was 2.34-folds higher than ATC suspension. Pharmacodynamic studies revealed significant reduction in serum lipids of rats with fatty liver. Photomicrographs showed improvement in hepatocytes structure. In this study, we confirmed that ATC-SNEDDS would be a promising approach for improving oral bioavailability of ATC.

Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

2005-01-01

An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

An analysis of the optimal multiobjective inventory clustering decision with small quantity and great variety inventory by applying a DPSO.

PubMed

Wang, Shen-Tsu; Li, Meng-Hua

2014-01-01

When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions.

Development and evaluation of a home enteral nutrition team.

PubMed

Dinenage, Sarah; Gower, Morwenna; Van Wyk, Joanna; Blamey, Anne; Ashbolt, Karen; Sutcliffe, Michelle; Green, Sue M

2015-03-05

The organisation of services to support the increasing number of people receiving enteral tube feeding (ETF) at home varies across regions. There is evidence that multi-disciplinary primary care teams focussed on home enteral nutrition (HEN) can provide cost-effective care. This paper describes the development and evaluation of a HEN Team in one UK city. A HEN Team comprising dietetians, nurses and a speech and language therapist was developed with the aim of delivering a quality service for people with gastrostomy tubes living at home. Team objectives were set and an underpinning framework of organisation developed including a care pathway and a schedule of training. Impact on patient outcomes was assessed in a pre-post test evaluation design. Patients and carers reported improved support in managing their ETF. Cost savings were realised through: (1) prevention of hospital admission and related transport for ETF related issues; (2) effective management and reduction of waste of feed and thickener; (3) balloon gastrostomy tube replacement by the HEN Team in the patient's home, and optimisation of nutritional status. This service evaluation demonstrated that the establishment of a dedicated multi-professional HEN Team focussed on achievement of key objectives improved patient experience and, although calculation of cost savings were estimates, provided evidence of cost-effectiveness.

Thermal buckling optimisation of composite plates using firefly algorithm

NASA Astrophysics Data System (ADS)

Kamarian, S.; Shakeri, M.; Yas, M. H.

2017-07-01

Composite plates play a very important role in engineering applications, especially in aerospace industry. Thermal buckling of such components is of great importance and must be known to achieve an appropriate design. This paper deals with stacking sequence optimisation of laminated composite plates for maximising the critical buckling temperature using a powerful meta-heuristic algorithm called firefly algorithm (FA) which is based on the flashing behaviour of fireflies. The main objective of present work was to show the ability of FA in optimisation of composite structures. The performance of FA is compared with the results reported in the previous published works using other algorithms which shows the efficiency of FA in stacking sequence optimisation of laminated composite structures.

A class of multi-period semi-variance portfolio for petroleum exploration and development

NASA Astrophysics Data System (ADS)

Guo, Qiulin; Li, Jianzhong; Zou, Caineng; Guo, Yujuan; Yan, Wei

2012-10-01

Variance is substituted by semi-variance in Markowitz's portfolio selection model. For dynamic valuation on exploration and development projects, one period portfolio selection is extended to multi-period. In this article, a class of multi-period semi-variance exploration and development portfolio model is formulated originally. Besides, a hybrid genetic algorithm, which makes use of the position displacement strategy of the particle swarm optimiser as a mutation operation, is applied to solve the multi-period semi-variance model. For this class of portfolio model, numerical results show that the mode is effective and feasible.

Optimisation on processing parameters for minimising warpage on side arm using response surface methodology (RSM) and particle swarm optimisation (PSO)

NASA Astrophysics Data System (ADS)

Rayhana, N.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.; Sazli, M.; Yahya, Z. R.

2017-09-01

This study presents the application of optimisation method to reduce the warpage of side arm part. Autodesk Moldflow Insight software was integrated into this study to analyse the warpage. The design of Experiment (DOE) for Response Surface Methodology (RSM) was constructed and by using the equation from RSM, Particle Swarm Optimisation (PSO) was applied. The optimisation method will result in optimised processing parameters with minimum warpage. Mould temperature, melt temperature, packing pressure, packing time and cooling time was selected as the variable parameters. Parameters selection was based on most significant factor affecting warpage stated by previous researchers. The results show that warpage was improved by 28.16% for RSM and 28.17% for PSO. The warpage improvement in PSO from RSM is only by 0.01 %. Thus, the optimisation using RSM is already efficient to give the best combination parameters and optimum warpage value for side arm part. The most significant parameters affecting warpage are packing pressure.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

PubMed

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

PubMed Central

2010-01-01

Background Solving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks. Results We evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from http://cbe.ipk-gatersleben.de. Conclusions The results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics. PMID:20064262

Investigation of power and frequency for 3D conformal MRI-controlled transurethral ultrasound therapy with a dual frequency multi-element transducer.

PubMed

N'djin, William Apoutou; Burtnyk, Mathieu; Bronskill, Michael; Chopra, Rajiv

2012-01-01

Transurethral ultrasound therapy uses real-time magnetic resonance (MR) temperature feedback to enable the 3D control of thermal therapy accurately in a region within the prostate. Previous canine studies showed the feasibility of this method in vivo. The aim of this study was to reduce the procedure time, while maintaining targeting accuracy, by investigating new combinations of treatment parameters. Simulations and validation experiments in gel phantoms were used, with a collection of nine 3D realistic target prostate boundaries obtained from previous preclinical studies, where multi-slice MR images were acquired with the transurethral device in place. Acoustic power and rotation rate were varied based on temperature feedback at the prostate boundary. Maximum acoustic power and rotation rate were optimised interdependently, as a function of prostate radius and transducer operating frequency. The concept of dual frequency transducers was studied, using the fundamental frequency or the third harmonic component depending on the prostate radius. Numerical modelling enabled assessment of the effects of several acoustic parameters on treatment outcomes. The range of treatable prostate radii extended with increasing power, and tended to narrow with decreasing frequency. Reducing the frequency from 8 MHz to 4 MHz or increasing the surface acoustic power from 10 to 20 W/cm(2) led to treatment times shorter by up to 50% under appropriate conditions. A dual frequency configuration of 4/12 MHz with 20 W/cm(2) ultrasound intensity exposure can treat entire prostates up to 40 cm(3) in volume within 30 min. The interdependence between power and frequency may, however, require integrating multi-parametric functions in the controller for future optimisations.

Engaging Homeless Individuals in Discussion about Their Food Experiences to Optimise Wellbeing: A Pilot Study

ERIC Educational Resources Information Center

Pettinger, Clare; Parsons, Julie M.; Cunningham, Miranda; Withers, Lyndsey; D'Aprano, Gia; Letherby, Gayle; Sutton, Carole; Whiteford, Andrew; Ayres, Richard

2017-01-01

Objective: High levels of social and economic deprivation are apparent in many UK cities, where there is evidence of certain "marginalised" communities suffering disproportionately from poor nutrition, threatening health. Finding ways to engage with these communities is essential to identify strategies to optimise wellbeing and life…

Multi-objective based spectral unmixing for hyperspectral images

NASA Astrophysics Data System (ADS)

Xu, Xia; Shi, Zhenwei

2017-02-01

Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.

Optimisation and validation of a rapid and efficient microemulsion liquid chromatographic (MELC) method for the determination of paracetamol (acetaminophen) content in a suppository formulation.

PubMed

McEvoy, Eamon; Donegan, Sheila; Power, Joe; Altria, Kevin

2007-05-09

A rapid and efficient oil-in-water microemulsion liquid chromatographic method has been optimised and validated for the analysis of paracetamol in a suppository formulation. Excellent linearity, accuracy, precision and assay results were obtained. Lengthy sample pre-treatment/extraction procedures were eliminated due to the solubilising power of the microemulsion and rapid analysis times were achieved. The method was optimised to achieve rapid analysis time and relatively high peak efficiencies. A standard microemulsion composition of 33 g SDS, 66 g butan-1-ol, 8 g n-octane in 1l of 0.05% TFA modified with acetonitrile has been shown to be suitable for the rapid analysis of paracetamol in highly hydrophobic preparations under isocratic conditions. Validated assay results and overall analysis time of the optimised method was compared to British Pharmacopoeia reference methods. Sample preparation and analysis times for the MELC analysis of paracetamol in a suppository were extremely rapid compared to the reference method and similar assay results were achieved. A gradient MELC method using the same microemulsion has been optimised for the resolution of paracetamol and five of its related substances in approximately 7 min.

A soft computing-based approach to optimise queuing-inventory control problem

NASA Astrophysics Data System (ADS)

Alaghebandha, Mohammad; Hajipour, Vahid

2015-04-01

In this paper, a multi-product continuous review inventory control problem within batch arrival queuing approach (MQr/M/1) is developed to find the optimal quantities of maximum inventory. The objective function is to minimise summation of ordering, holding and shortage costs under warehouse space, service level and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Non-deterministic Polynomial-time hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, both ganetic algorithm and simulated annealing algorithm are utilised. In order to determine the best value of algorithm parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analysed using some numerical illustrations.

A Method for Decentralised Optimisation in Networks

NASA Astrophysics Data System (ADS)

Saramäki, Jari

2005-06-01

We outline a method for distributed Monte Carlo optimisation of computational problems in networks of agents, such as peer-to-peer networks of computers. The optimisation and messaging procedures are inspired by gossip protocols and epidemic data dissemination, and are decentralised, i.e. no central overseer is required. In the outlined method, each agent follows simple local rules and seeks for better solutions to the optimisation problem by Monte Carlo trials, as well as by querying other agents in its local neighbourhood. With proper network topology, good solutions spread rapidly through the network for further improvement. Furthermore, the system retains its functionality even in realistic settings where agents are randomly switched on and off.

Self-optimisation and model-based design of experiments for developing a C-H activation flow process.

PubMed

Echtermeyer, Alexander; Amar, Yehia; Zakrzewski, Jacek; Lapkin, Alexei

2017-01-01

A recently described C(sp 3 )-H activation reaction to synthesise aziridines was used as a model reaction to demonstrate the methodology of developing a process model using model-based design of experiments (MBDoE) and self-optimisation approaches in flow. The two approaches are compared in terms of experimental efficiency. The self-optimisation approach required the least number of experiments to reach the specified objectives of cost and product yield, whereas the MBDoE approach enabled a rapid generation of a process model.

Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision

NASA Astrophysics Data System (ADS)

Hendrawan, Y.; Hawa, L. C.; Damayanti, R.

2018-03-01

This study attempted to apply machine vision-based chips drying monitoring system which is able to optimise the drying process of cassava chips. The objective of this study is to propose fish swarm intelligent (FSI) optimization algorithms to find the most significant set of image features suitable for predicting water content of cassava chips during drying process using artificial neural network model (ANN). Feature selection entails choosing the feature subset that maximizes the prediction accuracy of ANN. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. The results showed that the best feature subset i.e. grey mean, L(Lab) Mean, a(Lab) energy, red entropy, hue contrast, and grey homogeneity. The best feature subset has been tested successfully in ANN model to describe the relationship between image features and water content of cassava chips during drying process with R2 of real and predicted data was equal to 0.9.

Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework.

PubMed

Yang, Lingjian; Ainali, Chrysanthi; Tsoka, Sophia; Papageorgiou, Lazaros G

2014-12-05

Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies. A supervised multiclass pathway activity inference method using optimisation techniques is reported. For each pathway expression dataset, patterns of its constituent genes are summarised into one composite feature, termed pathway activity, and a novel mathematical programming model is proposed to infer this feature as a weighted linear summation of expression of its constituent genes. Gene weights are determined by the optimisation model, in a way that the resulting pathway activity has the optimal discriminative power with regards to disease phenotypes. Classification is then performed on the resulting low-dimensional pathway activity profile. The model was evaluated through a variety of published gene expression profiles that cover different types of disease. We show that not only does it improve classification accuracy, but it can also perform well in multiclass disease datasets, a limitation of other approaches from the literature. Desirable features of the model include the ability to control the maximum number of genes that may participate in determining pathway activity, which may be pre-specified by the user. Overall, this work highlights the potential of building pathway-based multi-phenotype classifiers for accurate disease diagnosis and prognosis problems.

The development and optimisation of a primary care-based whole system complex intervention (CARE Plus) for patients with multimorbidity living in areas of high socioeconomic deprivation

PubMed Central

O'Brien, Rosaleen; Fitzpatrick, Bridie; Higgins, Maria; Guthrie, Bruce; Watt, Graham; Wyke, Sally

2016-01-01

Objectives To develop and optimise a primary care-based complex intervention (CARE Plus) to enhance the quality of life of patients with multimorbidity in the deprived areas. Methods Six co-design discussion groups involving 32 participants were held separately with multimorbid patients from the deprived areas, voluntary organisations, general practitioners and practice nurses working in the deprived areas. This was followed by piloting in two practices and further optimisation based on interviews with 11 general practitioners, 2 practice nurses and 6 participating multimorbid patients. Results Participants endorsed the need for longer consultations, relational continuity and a holistic approach. All felt that training and support of the health care staff was important. Most participants welcomed the idea of additional self-management support, though some practitioners were dubious about whether patients would use it. The pilot study led to changes including a revised care plan, the inclusion of mindfulness-based stress reduction techniques in the support of practitioners and patients, and the stream-lining of the written self-management support material for patients. Discussion We have co-designed and optimised an augmented primary care intervention involving a whole-system approach to enhance quality of life in multimorbid patients living in the deprived areas. CARE Plus will next be tested in a phase 2 cluster randomised controlled trial. PMID:27068113

Nonlinear bioheat transfer models and multi-objective numerical optimization of the cryosurgery operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kudryashov, Nikolay A.; Shilnikov, Kirill E.

Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumormore » tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.« less

Optimised analytical models of the dielectric properties of biological tissue.

PubMed

Salahuddin, Saqib; Porter, Emily; Krewer, Finn; O' Halloran, Martin

2017-05-01

The interaction of electromagnetic fields with the human body is quantified by the dielectric properties of biological tissues. These properties are incorporated into complex numerical simulations using parametric models such as Debye and Cole-Cole, for the computational investigation of electromagnetic wave propagation within the body. These parameters can be acquired through a variety of optimisation algorithms to achieve an accurate fit to measured data sets. A number of different optimisation techniques have been proposed, but these are often limited by the requirement for initial value estimations or by the large overall error (often up to several percentage points). In this work, a novel two-stage genetic algorithm proposed by the authors is applied to optimise the multi-pole Debye parameters for 54 types of human tissues. The performance of the two-stage genetic algorithm has been examined through a comparison with five other existing algorithms. The experimental results demonstrate that the two-stage genetic algorithm produces an accurate fit to a range of experimental data and efficiently out-performs all other optimisation algorithms under consideration. Accurate values of the three-pole Debye models for 54 types of human tissues, over 500 MHz to 20 GHz, are also presented for reference. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Comparison of the genetic algorithm and incremental optimisation routines for a Bayesian inverse modelling based network design

NASA Astrophysics Data System (ADS)

Nickless, A.; Rayner, P. J.; Erni, B.; Scholes, R. J.

2018-05-01

The design of an optimal network of atmospheric monitoring stations for the observation of carbon dioxide (CO2) concentrations can be obtained by applying an optimisation algorithm to a cost function based on minimising posterior uncertainty in the CO2 fluxes obtained from a Bayesian inverse modelling solution. Two candidate optimisation methods assessed were the evolutionary algorithm: the genetic algorithm (GA), and the deterministic algorithm: the incremental optimisation (IO) routine. This paper assessed the ability of the IO routine in comparison to the more computationally demanding GA routine to optimise the placement of a five-member network of CO2 monitoring sites located in South Africa. The comparison considered the reduction in uncertainty of the overall flux estimate, the spatial similarity of solutions, and computational requirements. Although the IO routine failed to find the solution with the global maximum uncertainty reduction, the resulting solution had only fractionally lower uncertainty reduction compared with the GA, and at only a quarter of the computational resources used by the lowest specified GA algorithm. The GA solution set showed more inconsistency if the number of iterations or population size was small, and more so for a complex prior flux covariance matrix. If the GA completed with a sub-optimal solution, these solutions were similar in fitness to the best available solution. Two additional scenarios were considered, with the objective of creating circumstances where the GA may outperform the IO. The first scenario considered an established network, where the optimisation was required to add an additional five stations to an existing five-member network. In the second scenario the optimisation was based only on the uncertainty reduction within a subregion of the domain. The GA was able to find a better solution than the IO under both scenarios, but with only a marginal improvement in the uncertainty reduction. These results suggest that the best use of resources for the network design problem would be spent in improvement of the prior estimates of the flux uncertainties rather than investing these resources in running a complex evolutionary optimisation algorithm. The authors recommend that, if time and computational resources allow, that multiple optimisation techniques should be used as a part of a comprehensive suite of sensitivity tests when performing such an optimisation exercise. This will provide a selection of best solutions which could be ranked based on their utility and practicality.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

A rapid, automated approach to optimisation of multiple reaction monitoring conditions for quantitative bioanalytical mass spectrometry.

PubMed

Higton, D M

2001-01-01

An improvement to the procedure for the rapid optimisation of mass spectrometry (PROMS), for the development of multiple reaction methods (MRM) for quantitative bioanalytical liquid chromatography/tandem mass spectrometry (LC/MS/MS), is presented. PROMS is an automated protocol that uses flow-injection analysis (FIA) and AppleScripts to create methods and acquire the data for optimisation. The protocol determines the optimum orifice potential, the MRM conditions for each compound, and finally creates the MRM methods needed for sample analysis. The sensitivities of the MRM methods created by PROMS approach those created manually. MRM method development using PROMS currently takes less than three minutes per compound compared to at least fifteen minutes manually. To further enhance throughput, approaches to MRM optimisation using one injection per compound, two injections per pool of five compounds and one injection per pool of five compounds have been investigated. No significant difference in the optimised instrumental parameters for MRM methods were found between the original PROMS approach and these new methods, which are up to ten times faster. The time taken for an AppleScript to determine the optimum conditions and build the MRM methods is the same with all approaches. Copyright 2001 John Wiley & Sons, Ltd.

Optimisation of Fabric Reinforced Polymer Composites Using a Variant of Genetic Algorithm

NASA Astrophysics Data System (ADS)

Axinte, Andrei; Taranu, Nicolae; Bejan, Liliana; Hudisteanu, Iuliana

2017-12-01

Fabric reinforced polymeric composites are high performance materials with a rather complex fabric geometry. Therefore, modelling this type of material is a cumbersome task, especially when an efficient use is targeted. One of the most important issue of its design process is the optimisation of the individual laminae and of the laminated structure as a whole. In order to do that, a parametric model of the material has been defined, emphasising the many geometric variables needed to be correlated in the complex process of optimisation. The input parameters involved in this work, include: widths or heights of the tows and the laminate stacking sequence, which are discrete variables, while the gaps between adjacent tows and the height of the neat matrix are continuous variables. This work is one of the first attempts of using a Genetic Algorithm ( GA) to optimise the geometrical parameters of satin reinforced multi-layer composites. Given the mixed type of the input parameters involved, an original software called SOMGA (Satin Optimisation with a Modified Genetic Algorithm) has been conceived and utilised in this work. The main goal is to find the best possible solution to the problem of designing a composite material which is able to withstand to a given set of external, in-plane, loads. The optimisation process has been performed using a fitness function which can analyse and compare mechanical behaviour of different fabric reinforced composites, the results being correlated with the ultimate strains, which demonstrate the efficiency of the composite structure.

Detailed systematic analysis of recruitment strategies in randomised controlled trials in patients with an unscheduled admission to hospital

PubMed Central

Rooshenas, Leila; Fairhurst, Katherine; Rees, Jonathan; Gamble, Carrol; Blazeby, Jane M

2018-01-01

Objectives To examine the design and findings of recruitment studies in randomised controlled trials (RCTs) involving patients with an unscheduled hospital admission (UHA), to consider how to optimise recruitment in future RCTs of this nature. Design Studies within the ORRCA database (Online Resource for Recruitment Research in Clinical TriAls; www.orrca.org.uk) that reported on recruitment to RCTs involving UHAs in patients >18 years were included. Extracted data included trial clinical details, and the rationale and main findings of the recruitment study. Results Of 3114 articles populating ORRCA, 39 recruitment studies were eligible, focusing on 68 real and 13 hypothetical host RCTs. Four studies were prospectively planned investigations of recruitment interventions, one of which was a nested RCT. Most recruitment papers were reports of recruitment experiences from one or more ‘real’ RCTs (n=24) or studies using hypothetical RCTs (n=11). Rationales for conducting recruitment studies included limited time for informed consent (IC) and patients being too unwell to provide IC. Methods to optimise recruitment included providing patients with trial information in the prehospital setting, technology to allow recruiters to cover multiple sites, screening logs to uncover recruitment barriers, and verbal rather than written information and consent. Conclusion There is a paucity of high-quality research into recruitment in RCTs involving UHAs with only one nested randomised study evaluating a recruitment intervention. Among the remaining studies, methods to optimise recruitment focused on how to improve information provision in the prehospital setting and use of screening logs. Future research in this setting should focus on the prospective evaluation of the well-developed interventions to optimise recruitment. PMID:29420230

Coronary Artery Diagnosis Aided by Neural Network

NASA Astrophysics Data System (ADS)

Stefko, Kamil

2007-01-01

Coronary artery disease is due to atheromatous narrowing and subsequent occlusion of the coronary vessel. Application of optimised feed forward multi-layer back propagation neural network (MLBP) for detection of narrowing in coronary artery vessels is presented in this paper. The research was performed using 580 data records from traditional ECG exercise test confirmed by coronary arteriography results. Each record of training database included description of the state of a patient providing input data for the neural network. Level and slope of ST segment of a 12 lead ECG signal recorded at rest and after effort (48 floating point values) was the main component of input data for neural network was. Coronary arteriography results (verified the existence or absence of more than 50% stenosis of the particular coronary vessels) were used as a correct neural network training output pattern. More than 96% of cases were correctly recognised by especially optimised and a thoroughly verified neural network. Leave one out method was used for neural network verification so 580 data records could be used for training as well as for verification of neural network.

Factors affecting weld root morphology in laser keyhole welding

NASA Astrophysics Data System (ADS)

Frostevarg, Jan

2018-02-01

Welding production efficiency is usually optimised if full penetration can be achieved in a single pass. Techniques such as electron and laser beam welding offer deep high speed keyhole welding, especially since multi-kilowatt lasers became available. However, there are limitations for these techniques when considering weld imperfections such as weld cap undercuts, interior porosity or humps at the root. The thickness of sheets during full penetration welding is practically limited by these root humps. The mechanisms behind root morphology formation are not yet satisfactory understood. In this paper root humping is studied by reviewing previous studies and findings and also by sample examination and process observation by high speed imaging. Different process regimes governing root quality are presented, categorized and explained. Even though this study mainly covers laser beam and laser arc hybrid welding, the presented findings can generally be applied full penetration welding in medium to thick sheets, especially the discussion of surface tension effects. As a final result of this analysis, a map of methods to optimise weld root topology is presented.

Numerical investigation on an array of Helmholtz resonators for the reduction of micro-pressure waves in modern and future high-speed rail tunnel systems

NASA Astrophysics Data System (ADS)

Tebbutt, J. A.; Vahdati, M.; Carolan, D.; Dear, J. P.

2017-07-01

Previous research has proposed that an array of Helmholtz resonators may be an effective method for suppressing the propagation of pressure and sound waves, generated by a high-speed train entering and moving in a tunnel. The array can be used to counteract environmental noise from tunnel portals and also the emergence of a shock wave in the tunnel. The implementation of an array of Helmholtz resonators in current and future high-speed train-tunnel systems is studied. Wave propagation in the tunnel is modelled using a quasi-one-dimensional formulation, accounting for non-linear effects, wall friction and the diffusivity of sound. A multi-objective genetic algorithm is then used to optimise the design of the array, subject to the geometric constraints of a demonstrative tunnel system and the incident wavefront in order to attenuate the propagation of pressure waves. It is shown that an array of Helmholtz resonators can be an effective countermeasure for various tunnel lengths. In addition, the array can be designed to function effectively over a wide operating envelope, ensuring it will still function effectively as train speeds increase into the future.

A support vector machine approach for classification of welding defects from ultrasonic signals

NASA Astrophysics Data System (ADS)

Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

2014-07-01

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

Using MOEA with Redistribution and Consensus Branches to Infer Phylogenies.

PubMed

Min, Xiaoping; Zhang, Mouzhao; Yuan, Sisi; Ge, Shengxiang; Liu, Xiangrong; Zeng, Xiangxiang; Xia, Ningshao

2017-12-26

In recent years, to infer phylogenies, which are NP-hard problems, more and more research has focused on using metaheuristics. Maximum Parsimony and Maximum Likelihood are two effective ways to conduct inference. Based on these methods, which can also be considered as the optimal criteria for phylogenies, various kinds of multi-objective metaheuristics have been used to reconstruct phylogenies. However, combining these two time-consuming methods results in those multi-objective metaheuristics being slower than a single objective. Therefore, we propose a novel, multi-objective optimization algorithm, MOEA-RC, to accelerate the processes of rebuilding phylogenies using structural information of elites in current populations. We compare MOEA-RC with two representative multi-objective algorithms, MOEA/D and NAGA-II, and a non-consensus version of MOEA-RC on three real-world datasets. The result is, within a given number of iterations, MOEA-RC achieves better solutions than the other algorithms.

Comparative assessment of knee joint models used in multi-body kinematics optimisation for soft tissue artefact compensation.

PubMed

Richard, Vincent; Cappozzo, Aurelio; Dumas, Raphaël

2017-09-06

Estimating joint kinematics from skin-marker trajectories recorded using stereophotogrammetry is complicated by soft tissue artefact (STA), an inexorable source of error. One solution is to use a bone pose estimator based on multi-body kinematics optimisation (MKO) embedding joint constraints to compensate for STA. However, there is some debate over the effectiveness of this method. The present study aimed to quantitatively assess the degree of agreement between reference (i.e., artefact-free) knee joint kinematics and the same kinematics estimated using MKO embedding six different knee joint models. The following motor tasks were assessed: level walking, hopping, cutting, running, sit-to-stand, and step-up. Reference knee kinematics was taken from pin-marker or biplane fluoroscopic data acquired concurrently with skin-marker data, made available by the respective authors. For each motor task, Bland-Altman analysis revealed that the performance of MKO varied according to the joint model used, with a wide discrepancy in results across degrees of freedom (DoFs), models and motor tasks (with a bias between -10.2° and 13.2° and between -10.2mm and 7.2mm, and with a confidence interval up to ±14.8° and ±11.1mm, for rotation and displacement, respectively). It can be concluded that, while MKO might occasionally improve kinematics estimation, as implemented to date it does not represent a reliable solution to the STA issue. Copyright © 2017 Elsevier Ltd. All rights reserved.

The development and optimisation of 3D black-blood R2* mapping of the carotid artery wall.

PubMed

Yuan, Jianmin; Graves, Martin J; Patterson, Andrew J; Priest, Andrew N; Ruetten, Pascal P R; Usman, Ammara; Gillard, Jonathan H

2017-12-01

To develop and optimise a 3D black-blood R 2 * mapping sequence for imaging the carotid artery wall, using optimal blood suppression and k-space view ordering. Two different blood suppression preparation methods were used; Delay Alternating with Nutation for Tailored Excitation (DANTE) and improved Motion Sensitive Driven Equilibrium (iMSDE) were each combined with a three-dimensional (3D) multi-echo Fast Spoiled GRadient echo (ME-FSPGR) readout. Three different k-space view-order designs: Radial Fan-beam Encoding Ordering (RFEO), Distance-Determined Encoding Ordering (DDEO) and Centric Phase Encoding Order (CPEO) were investigated. The sequences were evaluated through Bloch simulation and in a cohort of twenty volunteers. The vessel wall Signal-to-Noise Ratio (SNR), Contrast-to-Noise Ratio (CNR) and R 2 *, and the sternocleidomastoid muscle R 2 * were measured and compared. Different numbers of acquisitions-per-shot (APS) were evaluated to further optimise the effectiveness of blood suppression. All sequences resulted in comparable R 2 * measurements to a conventional, i.e. non-blood suppressed sequence in the sternocleidomastoid muscle of the volunteers. Both Bloch simulations and volunteer data showed that DANTE has a higher signal intensity and results in a higher image SNR than iMSDE. Blood suppression efficiency was not significantly different when using different k-space view orders. Smaller APS achieved better blood suppression. The use of blood-suppression preparation methods does not affect the measurement of R 2 *. DANTE prepared ME-FSPGR sequence with a small number of acquisitions-per-shot can provide high quality black-blood R 2 * measurements of the carotid vessel wall. Copyright © 2017 Elsevier Inc. All rights reserved.

Efficient processing of multiple nested event pattern queries over multi-dimensional event streams based on a triaxial hierarchical model.

PubMed

Xiao, Fuyuan; Aritsugi, Masayoshi; Wang, Qing; Zhang, Rong

2016-09-01

For efficient and sophisticated analysis of complex event patterns that appear in streams of big data from health care information systems and support for decision-making, a triaxial hierarchical model is proposed in this paper. Our triaxial hierarchical model is developed by focusing on hierarchies among nested event pattern queries with an event concept hierarchy, thereby allowing us to identify the relationships among the expressions and sub-expressions of the queries extensively. We devise a cost-based heuristic by means of the triaxial hierarchical model to find an optimised query execution plan in terms of the costs of both the operators and the communications between them. According to the triaxial hierarchical model, we can also calculate how to reuse the results of the common sub-expressions in multiple queries. By integrating the optimised query execution plan with the reuse schemes, a multi-query optimisation strategy is developed to accomplish efficient processing of multiple nested event pattern queries. We present empirical studies in which the performance of multi-query optimisation strategy was examined under various stream input rates and workloads. Specifically, the workloads of pattern queries can be used for supporting monitoring patients' conditions. On the other hand, experiments with varying input rates of streams can correspond to changes of the numbers of patients that a system should manage, whereas burst input rates can correspond to changes of rushes of patients to be taken care of. The experimental results have shown that, in Workload 1, our proposal can improve about 4 and 2 times throughput comparing with the relative works, respectively; in Workload 2, our proposal can improve about 3 and 2 times throughput comparing with the relative works, respectively; in Workload 3, our proposal can improve about 6 times throughput comparing with the relative work. The experimental results demonstrated that our proposal was able to process complex queries efficiently which can support health information systems and further decision-making. Copyright © 2016 Elsevier B.V. All rights reserved.

Pareto Tracer: a predictor-corrector method for multi-objective optimization problems

NASA Astrophysics Data System (ADS)

Martín, Adanay; Schütze, Oliver

2018-03-01

This article proposes a novel predictor-corrector (PC) method for the numerical treatment of multi-objective optimization problems (MOPs). The algorithm, Pareto Tracer (PT), is capable of performing a continuation along the set of (local) solutions of a given MOP with k objectives, and can cope with equality and box constraints. Additionally, the first steps towards a method that manages general inequality constraints are also introduced. The properties of PT are first discussed theoretically and later numerically on several examples.

Multi-phase SPH modelling of violent hydrodynamics on GPUs

NASA Astrophysics Data System (ADS)

Mokos, Athanasios; Rogers, Benedict D.; Stansby, Peter K.; Domínguez, José M.

2015-11-01

This paper presents the acceleration of multi-phase smoothed particle hydrodynamics (SPH) using a graphics processing unit (GPU) enabling large numbers of particles (10-20 million) to be simulated on just a single GPU card. With novel hardware architectures such as a GPU, the optimum approach to implement a multi-phase scheme presents some new challenges. Many more particles must be included in the calculation and there are very different speeds of sound in each phase with the largest speed of sound determining the time step. This requires efficient computation. To take full advantage of the hardware acceleration provided by a single GPU for a multi-phase simulation, four different algorithms are investigated: conditional statements, binary operators, separate particle lists and an intermediate global function. Runtime results show that the optimum approach needs to employ separate cell and neighbour lists for each phase. The profiler shows that this approach leads to a reduction in both memory transactions and arithmetic operations giving significant runtime gains. The four different algorithms are compared to the efficiency of the optimised single-phase GPU code, DualSPHysics, for 2-D and 3-D simulations which indicate that the multi-phase functionality has a significant computational overhead. A comparison with an optimised CPU code shows a speed up of an order of magnitude over an OpenMP simulation with 8 threads and two orders of magnitude over a single thread simulation. A demonstration of the multi-phase SPH GPU code is provided by a 3-D dam break case impacting an obstacle. This shows better agreement with experimental results than an equivalent single-phase code. The multi-phase GPU code enables a convergence study to be undertaken on a single GPU with a large number of particles that otherwise would have required large high performance computing resources.

Optimising operational amplifiers by evolutionary algorithms and gm/Id method

NASA Astrophysics Data System (ADS)

Tlelo-Cuautle, E.; Sanabria-Borbon, A. C.

2016-10-01

The evolutionary algorithm called non-dominated sorting genetic algorithm (NSGA-II) is applied herein in the optimisation of operational transconductance amplifiers. NSGA-II is accelerated by applying the gm/Id method to estimate reduced search spaces associated to widths (W) and lengths (L) of the metal-oxide-semiconductor field-effect-transistor (MOSFETs), and to guarantee their appropriate bias levels conditions. In addition, we introduce an integer encoding for the W/L sizes of the MOSFETs to avoid a post-processing step for rounding-off their values to be multiples of the integrated circuit fabrication technology. Finally, from the feasible solutions generated by NSGA-II, we introduce a second optimisation stage to guarantee that the final feasible W/L sizes solutions support process, voltage and temperature (PVT) variations. The optimisation results lead us to conclude that the gm/Id method and integer encoding are quite useful to accelerate the convergence of the evolutionary algorithm NSGA-II, while the second optimisation stage guarantees robustness of the feasible solutions to PVT variations.

Optimised collision avoidance for an ultra-close rendezvous with a failed satellite based on the Gauss pseudospectral method

NASA Astrophysics Data System (ADS)

Chu, Xiaoyu; Zhang, Jingrui; Lu, Shan; Zhang, Yao; Sun, Yue

2016-11-01

This paper presents a trajectory planning algorithm to optimise the collision avoidance of a chasing spacecraft operating in an ultra-close proximity to a failed satellite. The complex configuration and the tumbling motion of the failed satellite are considered. The two-spacecraft rendezvous dynamics are formulated based on the target body frame, and the collision avoidance constraints are detailed, particularly concerning the uncertainties. An optimisation solution of the approaching problem is generated using the Gauss pseudospectral method. A closed-loop control is used to track the optimised trajectory. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms.

A method for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

NASA Astrophysics Data System (ADS)

Ai, Xueshan; Dong, Zuo; Mo, Mingzhu

2017-04-01

The optimal reservoir operation is in generally a multi-objective problem. In real life, most of the reservoir operation optimization problems involve conflicting objectives, for which there is no single optimal solution which can simultaneously gain an optimal result of all the purposes, but rather a set of well distributed non-inferior solutions or Pareto frontier exists. On the other hand, most of the reservoirs operation rules is to gain greater social and economic benefits at the expense of ecological environment, resulting to the destruction of riverine ecology and reduction of aquatic biodiversity. To overcome these drawbacks, this study developed a multi-objective model for the reservoir operating with the conflicting functions of hydroelectric energy generation, irrigation and ecological protection. To solve the model with the objectives of maximize energy production, maximize the water demand satisfaction rate of irrigation and ecology, we proposed a multi-objective optimization method of variable penalty coefficient (VPC), which was based on integrate dynamic programming (DP) with discrete differential dynamic programming (DDDP), to generate a well distributed non-inferior along the Pareto front by changing the penalties coefficient of different objectives. This method was applied to an existing China reservoir named Donggu, through a course of a year, which is a multi-annual storage reservoir with multiple purposes. The case study results showed a good relationship between any two of the objectives and a good Pareto optimal solutions, which provide a reference for the reservoir decision makers.

Quadratic Optimisation with One Quadratic Equality Constraint

DTIC Science & Technology

2010-06-01

This report presents a theoretical framework for minimising a quadratic objective function subject to a quadratic equality constraint. The first part of the report gives a detailed algorithm which computes the global minimiser without calling special nonlinear optimisation solvers. The second part of the report shows how the developed theory can be applied to solve the time of arrival geolocation problem.

A new effective operator for the hybrid algorithm for solving global optimisation problems

NASA Astrophysics Data System (ADS)

Duc, Le Anh; Li, Kenli; Nguyen, Tien Trong; Yen, Vu Minh; Truong, Tung Khac

2018-04-01

Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.

Multi-scale image segmentation method with visual saliency constraints and its application

NASA Astrophysics Data System (ADS)

Chen, Yan; Yu, Jie; Sun, Kaimin

2018-03-01

Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. It is very important to get the image objects by multi-scale image segmentation in order to carry out object-based image analysis. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over-segmentation) results are difficult to avoid. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. To avoid the problem of over-segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. This weight acts as one of the merging constraints in the multi- scale image segmentation. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non- saliency background areas. Experiments show that this method works better for texture image segmentation than traditional multi-scale image segmentation methods, and can enable us to give priority control to the saliency objects of interest. This method has been used in image quality evaluation, scattered residential area extraction, sparse forest extraction and other applications to verify its validation. All applications showed good results.

Determination of linuron and related compounds in soil by microwave-assisted solvent extraction and reversed-phase liquid chromatography with UV detection.

PubMed

Molins, C; Hogendoorn, E A; Dijkman, E; Heusinkveld, H A; Baumann, R A

2000-02-11

The combination of microwave-assisted solvent extraction (MASE) and reversed-phase liquid chromatography (RPLC) with UV detection has been investigated for the efficient determination of phenylurea herbicides in soils involving the single-residue method (SRM) approach (linuron) and the multi-residue method (MRM) approach (monuron, monolinuron, isoproturon, metobromuron, diuron and linuron). Critical parameters of MASE, viz, extraction temperature, water content and extraction solvent were varied in order to optimise recoveries of the analytes while simultaneously minimising co-extraction of soil interferences. The optimised extraction procedure was applied to different types of soil with an organic carbon content of 0.4-16.7%. Besides freshly spiked soil samples, method validation included the analysis of samples with aged residues. A comparative study between the applicability of RPLC-UV without and with the use of column switching for the processing of uncleaned extracts, was carried out. For some of the tested analyte/matrix combinations the one-column approach (LC mode) is feasible. In comparison to LC, coupled-column LC (LC-LC mode) provides high selectivity in single-residue analysis (linuron) and, although less pronounced in multi-residue analysis (all six phenylurea herbicides), the clean-up performance of LC-LC improves both time of analysis and sample throughput. In the MRM approach the developed procedure involving MASE and LC-LC-UV provided acceptable recoveries (range, 80-120%) and RSDs (<12%) at levels of 10 microg/kg (n=9) and 50 microg/kg (n=7), respectively, for most analyte/matrix combinations. Recoveries from aged residue samples spiked at a level of 100 microg/kg (n=7) ranged, depending of the analyte/soil type combination, from 41-113% with RSDs ranging from 1-35%. In the SRM approach the developed LC-LC procedure was applied for the determination of linuron in 28 sandy soil samples collected in a field study. Linuron could be determined in soil with a limit of quantitation of 10 microg/kg.

An Analysis of the Optimal Multiobjective Inventory Clustering Decision with Small Quantity and Great Variety Inventory by Applying a DPSO

PubMed Central

Li, Meng-Hua

2014-01-01

When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions. PMID:25197713

Dual-mode nested search method for categorical uncertain multi-objective optimization

NASA Astrophysics Data System (ADS)

Tang, Long; Wang, Hu

2016-10-01

Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

UAV path planning using artificial potential field method updated by optimal control theory

NASA Astrophysics Data System (ADS)

Chen, Yong-bo; Luo, Guan-chen; Mei, Yue-song; Yu, Jian-qiao; Su, Xiao-long

2016-04-01

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.

A novel method for overlapping community detection using Multi-objective optimization

NASA Astrophysics Data System (ADS)

Ebrahimi, Morteza; Shahmoradi, Mohammad Reza; Heshmati, Zainabolhoda; Salehi, Mostafa

2018-09-01

The problem of community detection as one of the most important applications of network science can be addressed effectively by multi-objective optimization. In this paper, we aim to present a novel efficient method based on this approach. Also, in this study the idea of using all Pareto fronts to detect overlapping communities is introduced. The proposed method has two main advantages compared to other multi-objective optimization based approaches. The first advantage is scalability, and the second is the ability to find overlapping communities. Despite most of the works, the proposed method is able to find overlapping communities effectively. The new algorithm works by extracting appropriate communities from all the Pareto optimal solutions, instead of choosing the one optimal solution. Empirical experiments on different features of separated and overlapping communities, on both synthetic and real networks show that the proposed method performs better in comparison with other methods.

A Bayesian Approach for Sensor Optimisation in Impact Identification

PubMed Central

Mallardo, Vincenzo; Sharif Khodaei, Zahra; Aliabadi, Ferri M. H.

2016-01-01

This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating impacts on composite structures. A Bayesian-based objective function is adopted in the optimisation procedure as an indicator of the performance of meta-models developed for different sensor combinations to locate various impact events. To represent a real structure under operational load and to increase the reliability of the Structural Health Monitoring (SHM) system, the probability of malfunctioning sensors is included in the optimisation. The reliability and the robustness of the procedure is tested with experimental and numerical examples. Finally, the proposed optimisation algorithm is applied to a composite stiffened panel for both the uniform and non-uniform probability of impact occurrence. PMID:28774064

A simple method for low-contrast detectability, image quality and dose optimisation with CT iterative reconstruction algorithms and model observers.

PubMed

Bellesi, Luca; Wyttenbach, Rolf; Gaudino, Diego; Colleoni, Paolo; Pupillo, Francesco; Carrara, Mauro; Braghetti, Antonio; Puligheddu, Carla; Presilla, Stefano

2017-01-01

The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment. Images of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four-alternative forced-choice test) and model observers were performed across the various images. Compared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result. IR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low-contrast detection tasks and in dose optimisation.

Multi-objective sustainable river management: balancing flood control, bio-pysical restoration and socio-economic factors in a Scottish river

NASA Astrophysics Data System (ADS)

Moir, H.; Bowles, C.; Campbell, C.; Sawyer, A.; Comins, L.; Werritty, A.

2010-12-01

The sustainable management of river corridors requires an understanding of the linkages between geomorphic, hydrologic, ecologic and socio-economic factors across a hierarchy of spatial and temporal scales. Therefore, in order to be genuinely sustainable, management must ideally be set within a catchment/watershed context. However, in practice, this rarely occurs due to obstacles imposed by fragmented land ownership/governance and an incomplete understanding of bio-physical process linkages. We present our experience on a project with the goal of optimising physical objectives at the catchment scale within a framework influenced by environmental legislation and conflicting land-use pressures. The project was carried out on the Eddleston Water in the Scottish Borders and had the primary objective of providing sustainable flood risk management to settlements on the water course while also providing ecological benefit to the river corridor. These co-objectives had to be met while considering the constraints imposed by land-use (predominantly arable agriculture) and transport infrastructure on the floodplain. The Eddleston Water has been heavily impacted by many human activities for over 200 years although a modified upland drainage, markedly canalised main-stem channel and floodplain disconnection are most significant to present-day physical and ecological processes. Catchment-scale restoration plans aim to restore broad-scale hydrological processes in conjunction with re-naturalisation of the river corridor at the reach-scale (including floodbank set-back, floodplain reconnection, regeneration of riparian vegetation, large wood placement). In addition, these measures also had to accommodate the objective of sustainable flood risk management, through the combination of a re-naturalised run-off regime and the encouragement of floodplain water storage. We present the output from 1D and 2D hydraulic models of a 1km stretch of the Eddleston Water that jointly assesses the benefit to flood hydrograph attenuation and bio-physical processes of a suite of restoration designs within the floodplain. Although the models produced an optimised design based on these environmental objectives, the ‘real world’ situation of constraints imposed by ‘socio-economic’ factors (particularly agricultural and urban infrastructure pressures) subsequently modified this. In this way the project demonstrated the compromises that have to be made in implementing these type of idealised physical objectives.

Advances in ultrasonic testing of austenitic stainless steel welds. Towards a 3D description of the material including attenuation and optimisation by inversion

NASA Astrophysics Data System (ADS)

Moysan, J.; Gueudré, C.; Ploix, M.-A.; Corneloup, G.; Guy, Ph.; Guerjouma, R. El; Chassignole, B.

In the case of multi-pass welds, the material is very difficult to describe due to its anisotropic and heterogeneous properties. Anisotropy results from the metal solidification and is correlated with the grain orientation. A precise description of the material is one of the key points to obtain reliable results with wave propagation codes. A first advance is the model MINA which predicts the grain orientations in multi-pass 316-L steel welds. For flat position welding, good predictions of the grains orientations were obtained using 2D modelling. In case of welding in position the resulting grain structure may be 3D oriented. We indicate how the MINA model can be improved for 3D description. A second advance is a good quantification of the attenuation. Precise measurements are obtained using plane waves angular spectrum method together with the computation of the transmission coefficients for triclinic material. With these two first advances, the third one is now possible: developing an inverse method to obtain the material description through ultrasonic measurements at different positions.

A game theory-reinforcement learning (GT-RL) method to develop optimal operation policies for multi-operator reservoir systems

NASA Astrophysics Data System (ADS)

Madani, Kaveh; Hooshyar, Milad

2014-11-01

Reservoir systems with multiple operators can benefit from coordination of operation policies. To maximize the total benefit of these systems the literature has normally used the social planner's approach. Based on this approach operation decisions are optimized using a multi-objective optimization model with a compound system's objective. While the utility of the system can be increased this way, fair allocation of benefits among the operators remains challenging for the social planner who has to assign controversial weights to the system's beneficiaries and their objectives. Cooperative game theory provides an alternative framework for fair and efficient allocation of the incremental benefits of cooperation. To determine the fair and efficient utility shares of the beneficiaries, cooperative game theory solution methods consider the gains of each party in the status quo (non-cooperation) as well as what can be gained through the grand coalition (social planner's solution or full cooperation) and partial coalitions. Nevertheless, estimation of the benefits of different coalitions can be challenging in complex multi-beneficiary systems. Reinforcement learning can be used to address this challenge and determine the gains of the beneficiaries for different levels of cooperation, i.e., non-cooperation, partial cooperation, and full cooperation, providing the essential input for allocation based on cooperative game theory. This paper develops a game theory-reinforcement learning (GT-RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria. As the first step to underline the utility of the GT-RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system.

Systems Analysis - a new paradigm and decision support tools for the water framework directive

NASA Astrophysics Data System (ADS)

Bruen, M.

2008-05-01

In the early days of Systems Analysis the focus was on providing tools for optimisation, modelling and simulation for use by experts. Now there is a recognition of the need to develop and disseminate tools to assist in making decisions, negotiating compromises and communicating preferences that can easily be used by stakeholders without the need for specialist training. The Water Framework Directive (WFD) requires public participation and thus provides a strong incentive for progress in this direction. This paper places the new paradigm in the context of the classical one and discusses some of the new approaches which can be used in the implementation of the WFD. These include multi-criteria decision support methods suitable for environmental problems, adaptive management, cognitive mapping, social learning and cooperative design and group decision-making. Concordance methods (such as ELECTRE) and the Analytical Hierarchy Process (AHP) are identified as multi-criteria methods that can be readily integrated into Decision Support Systems (DSS) that deal with complex environmental issues with very many criteria, some of which are qualitative. The expanding use of the new paradigm provides an opportunity to observe and learn from the interaction of stakeholders with the new technology and to assess its effectiveness.

Analysis and simulation of wireless signal propagation applying geostatistical interpolation techniques

NASA Astrophysics Data System (ADS)

Kolyaie, S.; Yaghooti, M.; Majidi, G.

2011-12-01

This paper is a part of an ongoing research to examine the capability of geostatistical analysis for mobile networks coverage prediction, simulation and tuning. Mobile network coverage predictions are used to find network coverage gaps and areas with poor serviceability. They are essential data for engineering and management in order to make better decision regarding rollout, planning and optimisation of mobile networks.The objective of this research is to evaluate different interpolation techniques in coverage prediction. In method presented here, raw data collected from drive testing a sample of roads in study area is analysed and various continuous surfaces are created using different interpolation methods. Two general interpolation methods are used in this paper with different variables; first, Inverse Distance Weighting (IDW) with various powers and number of neighbours and second, ordinary kriging with Gaussian, spherical, circular and exponential semivariogram models with different number of neighbours. For the result comparison, we have used check points coming from the same drive test data. Prediction values for check points are extracted from each surface and the differences with actual value are computed. The output of this research helps finding an optimised and accurate model for coverage prediction.

Reference voltage calculation method based on zero-sequence component optimisation for a regional compensation DVR

NASA Astrophysics Data System (ADS)

Jian, Le; Cao, Wang; Jintao, Yang; Yinge, Wang

2018-04-01

This paper describes the design of a dynamic voltage restorer (DVR) that can simultaneously protect several sensitive loads from voltage sags in a region of an MV distribution network. A novel reference voltage calculation method based on zero-sequence voltage optimisation is proposed for this DVR to optimise cost-effectiveness in compensation of voltage sags with different characteristics in an ungrounded neutral system. Based on a detailed analysis of the characteristics of voltage sags caused by different types of faults and the effect of the wiring mode of the transformer on these characteristics, the optimisation target of the reference voltage calculation is presented with several constraints. The reference voltages under all types of voltage sags are calculated by optimising the zero-sequence component, which can reduce the degree of swell in the phase-to-ground voltage after compensation to the maximum extent and can improve the symmetry degree of the output voltages of the DVR, thereby effectively increasing the compensation ability. The validity and effectiveness of the proposed method are verified by simulation and experimental results.

Multi-Objective Optimization of an In situ Bioremediation Technology to Treat Perchlorate-Contaminated Groundwater

EPA Science Inventory

The presentation shows how a multi-objective optimization method is integrated into a transport simulator (MT3D) for estimating parameters and cost of in-situ bioremediation technology to treat perchlorate-contaminated groundwater.

Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?

PubMed Central

Kell, Douglas B

2012-01-01

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. PMID:22252984

An illustration of new methods in machine condition monitoring, Part I: stochastic resonance

NASA Astrophysics Data System (ADS)

Worden, K.; Antoniadou, I.; Marchesiello, S.; Mba, C.; Garibaldi, L.

2017-05-01

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach.

Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?

PubMed

Kell, Douglas B

2012-03-01

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a 'landscape' representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems 'hard', but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the 'best' experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. Copyright © 2012 WILEY Periodicals, Inc.

A robust algorithm for optimisation and customisation of fractal dimensions of time series modified by nonlinearly scaling their time derivatives: mathematical theory and practical applications.

PubMed

Fuss, Franz Konstantin

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals.

A Robust Algorithm for Optimisation and Customisation of Fractal Dimensions of Time Series Modified by Nonlinearly Scaling Their Time Derivatives: Mathematical Theory and Practical Applications

PubMed Central

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals. PMID:24151522

Variance approach for multi-objective linear programming with fuzzy random of objective function coefficients

NASA Astrophysics Data System (ADS)

Indarsih, Indrati, Ch. Rini

2016-02-01

In this paper, we define variance of the fuzzy random variables through alpha level. We have a theorem that can be used to know that the variance of fuzzy random variables is a fuzzy number. We have a multi-objective linear programming (MOLP) with fuzzy random of objective function coefficients. We will solve the problem by variance approach. The approach transform the MOLP with fuzzy random of objective function coefficients into MOLP with fuzzy of objective function coefficients. By weighted methods, we have linear programming with fuzzy coefficients and we solve by simplex method for fuzzy linear programming.

Optimisation and establishment of diagnostic reference levels in paediatric plain radiography

NASA Astrophysics Data System (ADS)

Paulo, Graciano do Nascimento Nobre

Purpose: This study aimed to propose Diagnostic Reference Levels (DRLs) in paediatric plain radiography and to optimise the most frequent paediatric plain radiography examinations in Portugal following an analysis and evaluation of current practice. Methods and materials: Anthropometric data (weight, patient height and thickness of the irradiated anatomy) was collected from 9,935 patients referred for a radiography procedure to one of the three dedicated paediatric hospitals in Portugal. National DRLs were calculated for the three most frequent X-ray procedures at the three hospitals: chest AP/PA projection; abdomen AP projection; pelvis AP projection. Exposure factors and patient dose were collected prospectively at the clinical sites. In order to analyse the relationship between exposure factors, the use of technical features and dose, experimental tests were made using two anthropomorphic phantoms: a) CIRSTM ATOM model 705; height: 110cm, weight: 19kg and b) Kyoto kagakuTM model PBU-60; height: 165cm, weight: 50kg. After phantom data collection, an objective image analysis was performed by analysing the variation of the mean value of the standard deviation, measured with OsiriX software (Pixmeo, Switzerland). After proposing new exposure criteria, a Visual Grading Characteristic image quality evaluation was performed blindly by four paediatric radiologists, each with a minimum of 10 years of professional experience, using anatomical criteria scoring. Results: DRLs by patient weight groups have been established for the first time. ESAKP75 DRLs for both patient age and weight groups were also obtained and are described in the thesis. Significant dose reduction was achieved through the implementation of an optimisation programme: an average reduction of 41% and 18% on KAPP75 and ESAKP75, respectively for chest plain radiography; an average reduction of 58% and 53% on KAPP75 and ESAKP75, respectively for abdomen plain radiography; and an average reduction of 47% and 48% on KAPP75 and ESAKP75, respectively for pelvis plain radiography. Conclusion: Portuguese DRLs for plain radiography were obtained for paediatric plain radiography (chest AP/PA, abdomen and pelvis). Experimental phantom tests identified adequate plain radiography exposure criteria, validated by objective and subjective image quality analysis. The new exposure criteria were put into practice in one of the paediatric hospitals, by introducing an optimisation programme. The implementation of the optimisation programme allowed a significant dose reduction to paediatric patients, without compromising image quality. (Abstract shortened by ProQuest.).

Automation of route identification and optimisation based on data-mining and chemical intuition.

PubMed

Lapkin, A A; Heer, P K; Jacob, P-M; Hutchby, M; Cunningham, W; Bull, S D; Davidson, M G

2017-09-21

Data-mining of Reaxys and network analysis of the combined literature and in-house reactions set were used to generate multiple possible reaction routes to convert a bio-waste feedstock, limonene, into a pharmaceutical API, paracetamol. The network analysis of data provides a rich knowledge-base for generation of the initial reaction screening and development programme. Based on the literature and the in-house data, an overall flowsheet for the conversion of limonene to paracetamol was proposed. Each individual reaction-separation step in the sequence was simulated as a combination of the continuous flow and batch steps. The linear model generation methodology allowed us to identify the reaction steps requiring further chemical optimisation. The generated model can be used for global optimisation and generation of environmental and other performance indicators, such as cost indicators. However, the identified further challenge is to automate model generation to evolve optimal multi-step chemical routes and optimal process configurations.

Optimisation of high-quality total ribonucleic acid isolation from cartilaginous tissues for real-time polymerase chain reaction analysis

PubMed Central

Peeters, M.; Huang, C. L.; Vonk, L. A.; Lu, Z. F.; Bank, R. A.; Doulabi, B. Zandieh

2016-01-01

Objectives Studies which consider the molecular mechanisms of degeneration and regeneration of cartilaginous tissues are seriously hampered by problematic ribonucleic acid (RNA) isolations due to low cell density and the dense, proteoglycan-rich extracellular matrix of cartilage. Proteoglycans tend to co-purify with RNA, they can absorb the full spectrum of UV light and they are potent inhibitors of polymerase chain reaction (PCR). Therefore, the objective of the present study is to compare and optimise different homogenisation methods and RNA isolation kits for an array of cartilaginous tissues. Materials and Methods Tissue samples such as the nucleus pulposus (NP), annulus fibrosus (AF), articular cartilage (AC) and meniscus, were collected from goats and homogenised by either the MagNA Lyser or Freezer Mill. RNA of duplicate samples was subsequently isolated by either TRIzol (benchmark), or the RNeasy Lipid Tissue, RNeasy Fibrous Tissue, or Aurum Total RNA Fatty and Fibrous Tissue kits. RNA yield, purity, and integrity were determined and gene expression levels of type II collagen and aggrecan were measured by real-time PCR. Results No differences between the two homogenisation methods were found. RNA isolation using the RNeasy Fibrous and Lipid kits resulted in the purest RNA (A260/A280 ratio), whereas TRIzol isolations resulted in RNA that is not as pure, and show a larger difference in gene expression of duplicate samples compared with both RNeasy kits. The Aurum kit showed low reproducibility. Conclusion For the extraction of high-quality RNA from cartilaginous structures, we suggest homogenisation of the samples by the MagNA Lyser. For AC, NP and AF we recommend the RNeasy Fibrous kit, whereas for the meniscus the RNeasy Lipid kit is advised. Cite this article: M. Peeters, C. L. Huang, L. A. Vonk, Z. F. Lu, R. A. Bank, M. N. Helder, B. Zandieh Doulabi. Optimisation of high-quality total ribonucleic acid isolation from cartilaginous tissues for real-time polymerase chain reaction analysis. Bone Joint Res 2016;5:560–568. DOI: 10.1302/2046-3758.511.BJR-2016-0033.R3. PMID:27881439

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

NASA Astrophysics Data System (ADS)

Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

2013-09-01

The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

A management and optimisation model for water supply planning in water deficit areas

NASA Astrophysics Data System (ADS)

Molinos-Senante, María; Hernández-Sancho, Francesc; Mocholí-Arce, Manuel; Sala-Garrido, Ramón

2014-07-01

The integrated water resources management approach has proven to be a suitable option for efficient, equitable and sustainable water management. In water-poor regions experiencing acute and/or chronic shortages, optimisation techniques are a useful tool for supporting the decision process of water allocation. In order to maximise the value of water use, an optimisation model was developed which involves multiple supply sources (conventional and non-conventional) and multiple users. Penalties, representing monetary losses in the event of an unfulfilled water demand, have been incorporated into the objective function. This model represents a novel approach which considers water distribution efficiency and the physical connections between water supply and demand points. Subsequent empirical testing using data from a Spanish Mediterranean river basin demonstrated the usefulness of the global optimisation model to solve existing water imbalances at the river basin level.

Objective perimetry using a four-channel multifocal VEP system: correlation with conventional perimetry and thickness of the retinal nerve fibre layer.

PubMed

Horn, Folkert K; Kaltwasser, Christoph; Jünemann, Anselm G; Kremers, Jan; Tornow, Ralf P

2012-04-01

There is evidence that multifocal visual evoked potentials (VEPs) can be used as an objective tool to detect visual field loss. The aim of this study was to correlate multifocal VEP amplitudes with standard perimetry data and retinal nerve fibre layer (RNFL) thickness. Multifocal VEP recordings were performed with a four-channel electrode array using 58 stimulus fields (pattern reversal dartboard). For each field, the recording from the channel with maximal signal-to-noise ratio (SNR) was retained, resulting in an SNR optimised virtual recording. Correlation with RNFL thickness, measured with spectral domain optical coherence tomography and with standard perimetry, was performed for nerve fibre bundle related areas. The mean amplitudes in nerve fibre related areas were smaller in glaucoma patients than in normal subjects. The differences between both groups were most significant in mid-peripheral areas. Amplitudes in these areas were significantly correlated with corresponding RNFL thickness (Spearman R=0.76) and with standard perimetry (R=0.71). The multifocal VEP amplitude was correlated with perimetric visual field data and the RNFL thickness of the corresponding regions. This method of SNR optimisation is useful for extracting data from recordings and may be appropriate for objective assessment of visual function at different locations. This study has been registered at http://www.clinicaltrials.gov (NCT00494923).

Research on connection structure of aluminumbody bus using multi-objective topology optimization

NASA Astrophysics Data System (ADS)

Peng, Q.; Ni, X.; Han, F.; Rhaman, K.; Ulianov, C.; Fang, X.

2018-01-01

For connecting Aluminum Alloy bus body aluminum components often occur the problem of failure, a new aluminum alloy connection structure is designed based on multi-objective topology optimization method. Determining the shape of the outer contour of the connection structure with topography optimization, establishing a topology optimization model of connections based on SIMP density interpolation method, going on multi-objective topology optimization, and improving the design of the connecting piece according to the optimization results. The results show that the quality of the aluminum alloy connector after topology optimization is reduced by 18%, and the first six natural frequencies are improved and the strength performance and stiffness performance are obviously improved.

A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle

NASA Astrophysics Data System (ADS)

Nejlaoui, Mohamed; Houidi, Ajmi; Affi, Zouhaier; Romdhane, Lotfi

2017-10-01

This paper deals with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. A combined multi-objective imperialist competitive algorithm and Monte Carlo method is developed and used for the robust multi-objective optimization of the rail vehicle system. This robust optimization of rail vehicle safety considers simultaneously the derailment angle and its standard deviation where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the rail vehicle safety to the design parameters uncertainties compared to the determinist one and to the literature results.

Modern multicore and manycore architectures: Modelling, optimisation and benchmarking a multiblock CFD code

NASA Astrophysics Data System (ADS)

Hadade, Ioan; di Mare, Luca

2016-08-01

Modern multicore and manycore processors exhibit multiple levels of parallelism through a wide range of architectural features such as SIMD for data parallel execution or threads for core parallelism. The exploitation of multi-level parallelism is therefore crucial for achieving superior performance on current and future processors. This paper presents the performance tuning of a multiblock CFD solver on Intel SandyBridge and Haswell multicore CPUs and the Intel Xeon Phi Knights Corner coprocessor. Code optimisations have been applied on two computational kernels exhibiting different computational patterns: the update of flow variables and the evaluation of the Roe numerical fluxes. We discuss at great length the code transformations required for achieving efficient SIMD computations for both kernels across the selected devices including SIMD shuffles and transpositions for flux stencil computations and global memory transformations. Core parallelism is expressed through threading based on a number of domain decomposition techniques together with optimisations pertaining to alleviating NUMA effects found in multi-socket compute nodes. Results are correlated with the Roofline performance model in order to assert their efficiency for each distinct architecture. We report significant speedups for single thread execution across both kernels: 2-5X on the multicore CPUs and 14-23X on the Xeon Phi coprocessor. Computations at full node and chip concurrency deliver a factor of three speedup on the multicore processors and up to 24X on the Xeon Phi manycore coprocessor.

Laser-driven x-ray and neutron source development for industrial applications of plasma accelerators

NASA Astrophysics Data System (ADS)

Brenner, C. M.; Mirfayzi, S. R.; Rusby, D. R.; Armstrong, C.; Alejo, A.; Wilson, L. A.; Clarke, R.; Ahmed, H.; Butler, N. M. H.; Haddock, D.; Higginson, A.; McClymont, A.; Murphy, C.; Notley, M.; Oliver, P.; Allott, R.; Hernandez-Gomez, C.; Kar, S.; McKenna, P.; Neely, D.

2016-01-01

Pulsed beams of energetic x-rays and neutrons from intense laser interactions with solid foils are promising for applications where bright, small emission area sources, capable of multi-modal delivery are ideal. Possible end users of laser-driven multi-modal sources are those requiring advanced non-destructive inspection techniques in industry sectors of high value commerce such as aerospace, nuclear and advanced manufacturing. We report on experimental work that demonstrates multi-modal operation of high power laser-solid interactions for neutron and x-ray beam generation. Measurements and Monte Carlo radiation transport simulations show that neutron yield is increased by a factor ~2 when a 1 mm copper foil is placed behind a 2 mm lithium foil, compared to using a 2 cm block of lithium only. We explore x-ray generation with a 10 picosecond drive pulse in order to tailor the spectral content for radiography with medium density alloy metals. The impact of using  >1 ps pulse duration on laser-accelerated electron beam generation and transport is discussed alongside the optimisation of subsequent bremsstrahlung emission in thin, high atomic number target foils. X-ray spectra are deconvolved from spectrometer measurements and simulation data generated using the GEANT4 Monte Carlo code. We also demonstrate the unique capability of laser-driven x-rays in being able to deliver single pulse high spatial resolution projection imaging of thick metallic objects. Active detector radiographic imaging of industrially relevant sample objects with a 10 ps drive pulse is presented for the first time, demonstrating that features of 200 μm size are resolved when projected at high magnification.

Efficient solution of a multi objective fuzzy transportation problem

NASA Astrophysics Data System (ADS)

Vidhya, V.; Ganesan, K.

2018-04-01

In this paper we present a methodology for the solution of multi-objective fuzzy transportation problem when all the cost and time coefficients are trapezoidal fuzzy numbers and the supply and demand are crisp numbers. Using a new fuzzy arithmetic on parametric form of trapezoidal fuzzy numbers and a new ranking method all efficient solutions are obtained. The proposed method is illustrated with an example.

Airfoil Shape Optimization based on Surrogate Model

NASA Astrophysics Data System (ADS)

Mukesh, R.; Lingadurai, K.; Selvakumar, U.

2018-02-01

Engineering design problems always require enormous amount of real-time experiments and computational simulations in order to assess and ensure the design objectives of the problems subject to various constraints. In most of the cases, the computational resources and time required per simulation are large. In certain cases like sensitivity analysis, design optimisation etc where thousands and millions of simulations have to be carried out, it leads to have a life time of difficulty for designers. Nowadays approximation models, otherwise called as surrogate models (SM), are more widely employed in order to reduce the requirement of computational resources and time in analysing various engineering systems. Various approaches such as Kriging, neural networks, polynomials, Gaussian processes etc are used to construct the approximation models. The primary intention of this work is to employ the k-fold cross validation approach to study and evaluate the influence of various theoretical variogram models on the accuracy of the surrogate model construction. Ordinary Kriging and design of experiments (DOE) approaches are used to construct the SMs by approximating panel and viscous solution algorithms which are primarily used to solve the flow around airfoils and aircraft wings. The method of coupling the SMs with a suitable optimisation scheme to carryout an aerodynamic design optimisation process for airfoil shapes is also discussed.

Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders

PubMed Central

Lim, Kian Sheng; Buyamin, Salinda; Ahmad, Anita; Shapiai, Mohd Ibrahim; Naim, Faradila; Mubin, Marizan; Kim, Dong Hwa

2014-01-01

The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms. PMID:24883386

The DUNE Mission

NASA Astrophysics Data System (ADS)

Castander, F. J.

The Dark UNiverse Explorer (DUNE) is a wide-field imaging mission concept whose primary goal is the study of dark energy and dark matter with unprecedented precision. To this end, DUNE is optimised for weak gravitational lensing, and also uses complementary cosmological probes, such as baryonic oscillations, the integrated Sachs-Wolf effect, and cluster counts. Besides its observational cosmology goals, the mission capabilities of DUNE allow the study of galaxy evolution, galactic structure and the demographics of Earth-mass planets. DUNE is a medium class mission consisting of a 1.2m telescope designed to carry out an all-sky survey in one visible and three NIR bands. The final data of the DUNE mission will form a unique legacy for the astronomy community. DUNE has been selected jointly with SPACE for an ESA Assessment phase which has led to the Euclid merged mission concept which combines wide-field deep imaging with low resolution multi-object spectroscopy.

SAMI Automated Plug Plate Configuration

NASA Astrophysics Data System (ADS)

Lorente, N. P. F.; Farrell, T.; Goodwin, M.

2013-10-01

The Sydney-AAO Multi-object Integral field spectrograph (SAMI) is a prototype wide-field system at the Anglo-Australian Telescope (AAT) which uses a plug-plate to mount its 13×61-core imaging fibre bundles (hexabundles) in the optical path at the telescope's prime focus. In this paper we describe the process of determining the positions of the plug-plate holes, where plates contain three or more stacked observation configurations. The process, which up until now has involved several separate processes and has required significant manual configuration and checking, is now being automated to increase efficiency and reduce error. This is carried out by means of a thin Java controller layer which drives the configuration cycle. This layer controls the user interface and the C++ algorithm layer where the plate configuration and optimisation is carried out. Additionally, through the Aladin display package, it provides visualisation and facilitates user verification of the resulting plates.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Accelerating clinical development of HIV vaccine strategies: methodological challenges and considerations in constructing an optimised multi-arm phase I/II trial design.

PubMed

Richert, Laura; Doussau, Adélaïde; Lelièvre, Jean-Daniel; Arnold, Vincent; Rieux, Véronique; Bouakane, Amel; Lévy, Yves; Chêne, Geneviève; Thiébaut, Rodolphe

2014-02-26

Many candidate vaccine strategies against human immunodeficiency virus (HIV) infection are under study, but their clinical development is lengthy and iterative. To accelerate HIV vaccine development optimised trial designs are needed. We propose a randomised multi-arm phase I/II design for early stage development of several vaccine strategies, aiming at rapidly discarding those that are unsafe or non-immunogenic. We explored early stage designs to evaluate both the safety and the immunogenicity of four heterologous prime-boost HIV vaccine strategies in parallel. One of the vaccines used as a prime and boost in the different strategies (vaccine 1) has yet to be tested in humans, thus requiring a phase I safety evaluation. However, its toxicity risk is considered minimal based on data from similar vaccines. We newly adapted a randomised phase II trial by integrating an early safety decision rule, emulating that of a phase I study. We evaluated the operating characteristics of the proposed design in simulation studies with either a fixed-sample frequentist or a continuous Bayesian safety decision rule and projected timelines for the trial. We propose a randomised four-arm phase I/II design with two independent binary endpoints for safety and immunogenicity. Immunogenicity evaluation at trial end is based on a single-stage Fleming design per arm, comparing the observed proportion of responders in an immunogenicity screening assay to an unacceptably low proportion, without direct comparisons between arms. Randomisation limits heterogeneity in volunteer characteristics between arms. To avoid exposure of additional participants to an unsafe vaccine during the vaccine boost phase, an early safety decision rule is imposed on the arm starting with vaccine 1 injections. In simulations of the design with either decision rule, the risks of erroneous conclusions were controlled <15%. Flexibility in trial conduct is greater with the continuous Bayesian rule. A 12-month gain in timelines is expected by this optimised design. Other existing designs such as bivariate or seamless phase I/II designs did not offer a clear-cut alternative. By combining phase I and phase II evaluations in a multi-arm trial, the proposed optimised design allows for accelerating early stage clinical development of HIV vaccine strategies.

Structural-electrical coupling optimisation for radiating and scattering performances of active phased array antenna

NASA Astrophysics Data System (ADS)

Wang, Congsi; Wang, Yan; Wang, Zhihai; Wang, Meng; Yuan, Shuai; Wang, Weifeng

2018-04-01

It is well known that calculating and reducing of radar cross section (RCS) of the active phased array antenna (APAA) are both difficult and complicated. It remains unresolved to balance the performance of the radiating and scattering when the RCS is reduced. Therefore, this paper develops a structure and scattering array factor coupling model of APAA based on the phase errors of radiated elements generated by structural distortion and installation error of the array. To obtain the optimal radiating and scattering performance, an integrated optimisation model is built to optimise the installation height of all the radiated elements in normal direction of the array, in which the particle swarm optimisation method is adopted and the gain loss and scattering array factor are selected as the fitness function. The simulation indicates that the proposed coupling model and integrated optimisation method can effectively decrease the RCS and that the necessary radiating performance can be simultaneously guaranteed, which demonstrate an important application value in engineering design and structural evaluation of APAA.

DryLab® optimised two-dimensional high performance liquid chromatography for differentiation of ephedrine and pseudoephedrine based methamphetamine samples.

PubMed

Andrighetto, Luke M; Stevenson, Paul G; Pearson, James R; Henderson, Luke C; Conlan, Xavier A

2014-11-01

In-silico optimised two-dimensional high performance liquid chromatographic (2D-HPLC) separations of a model methamphetamine seizure sample are described, where an excellent match between simulated and real separations was observed. Targeted separation of model compounds was completed with significantly reduced method development time. This separation was completed in the heart-cutting mode of 2D-HPLC where C18 columns were used in both dimensions taking advantage of the selectivity difference of methanol and acetonitrile as the mobile phases. This method development protocol is most significant when optimising the separation of chemically similar chemical compounds as it eliminates potentially hours of trial and error injections to identify the optimised experimental conditions. After only four screening injections the gradient profile for both 2D-HPLC dimensions could be optimised via simulations, ensuring the baseline resolution of diastereomers (ephedrine and pseudoephedrine) in 9.7 min. Depending on which diastereomer is present the potential synthetic pathway can be categorized.

Hydroxyapatite, fluor-hydroxyapatite and fluorapatite produced via the sol-gel method. Optimisation, characterisation and rheology.

PubMed

Tredwin, Christopher J; Young, Anne M; Georgiou, George; Shin, Song-Hee; Kim, Hae-Won; Knowles, Jonathan C

2013-02-01

Currently, most titanium implant coatings are made using hydroxyapatite and a plasma spraying technique. There are however limitations associated with plasma spraying processes including poor adherence, high porosity and cost. An alternative method utilising the sol-gel technique offers many potential advantages but is currently lacking research data for this application. It was the objective of this study to characterise and optimise the production of Hydroxyapatite (HA), fluorhydroxyapatite (FHA) and fluorapatite (FA) using a sol-gel technique and assess the rheological properties of these materials. HA, FHA and FA were synthesised by a sol-gel method. Calcium nitrate and triethylphosphite were used as precursors under an ethanol-water based solution. Different amounts of ammonium fluoride (NH4F) were incorporated for the preparation of the sol-gel derived FHA and FA. Optimisation of the chemistry and subsequent characterisation of the sol-gel derived materials was carried out using X-ray Diffraction (XRD) and Differential Thermal Analysis (DTA). Rheology of the sol-gels was investigated using a viscometer and contact angle measurement. A protocol was established that allowed synthesis of HA, FHA and FA that were at least 99% phase pure. The more fluoride incorporated into the apatite structure; the lower the crystallisation temperature, the smaller the unit cell size (changes in the a-axis), the higher the viscosity and contact angle of the sol-gel derived apatite. A technique has been developed for the production of HA, FHA and FA by the sol-gel technique. Increasing fluoride substitution in the apatite structure alters the potential coating properties. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

A stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

USDA-ARS?s Scientific Manuscript database

Proper parameterization enables hydrological models to make reliable estimates of non-point source pollution for effective control measures. The automatic calibration of hydrologic models requires significant computational power limiting its application. The study objective was to develop and eval...

Understanding the Models of Community Hospital rehabilitation Activity (MoCHA): a mixed-methods study

PubMed Central

Gladman, John; Buckell, John; Young, John; Smith, Andrew; Hulme, Clare; Saggu, Satti; Godfrey, Mary; Enderby, Pam; Teale, Elizabeth; Longo, Roberto; Gannon, Brenda; Holditch, Claire; Eardley, Heather; Tucker, Helen

2017-01-01

Introduction To understand the variation in performance between community hospitals, our objectives are: to measure the relative performance (cost efficiency) of rehabilitation services in community hospitals; to identify the characteristics of community hospital rehabilitation that optimise performance; to investigate the current impact of community hospital inpatient rehabilitation for older people on secondary care and the potential impact if community hospital rehabilitation was optimised to best practice nationally; to examine the relationship between the configuration of intermediate care and secondary care bed use; and to develop toolkits for commissioners and community hospital providers to optimise performance. Methods and analysis 4 linked studies will be performed. Study 1: cost efficiency modelling will apply econometric techniques to data sets from the National Health Service (NHS) Benchmarking Network surveys of community hospital and intermediate care. This will identify community hospitals' performance and estimate the gap between high and low performers. Analyses will determine the potential impact if the performance of all community hospitals nationally was optimised to best performance, and examine the association between community hospital configuration and secondary care bed use. Study 2: a national community hospital survey gathering detailed cost data and efficiency variables will be performed. Study 3: in-depth case studies of 3 community hospitals, 2 high and 1 low performing, will be undertaken. Case studies will gather routine hospital and local health economy data. Ward culture will be surveyed. Content and delivery of treatment will be observed. Patients and staff will be interviewed. Study 4: co-designed web-based quality improvement toolkits for commissioners and providers will be developed, including indicators of performance and the gap between local and best community hospitals performance. Ethics and dissemination Publications will be in peer-reviewed journals, reports will be distributed through stakeholder organisations. Ethical approval was obtained from the Bradford Research Ethics Committee (reference: 15/YH/0062). PMID:28242766

Computational aero-acoustics for fan duct propagation and radiation. Current status and application to turbofan liner optimisation

NASA Astrophysics Data System (ADS)

Astley, R. J.; Sugimoto, R.; Mustafi, P.

2011-08-01

Novel techniques are presented to reduce noise from turbofan aircraft engines by optimising the acoustic treatment in engine ducts. The application of Computational Aero-Acoustics (CAA) to predict acoustic propagation and absorption in turbofan ducts is reviewed and a critical assessment of performance indicates that validated and accurate techniques are now available for realistic engine predictions. A procedure for integrating CAA methods with state of the art optimisation techniques is proposed in the remainder of the article. This is achieved by embedding advanced computational methods for noise prediction within automated and semi-automated optimisation schemes. Two different strategies are described and applied to realistic nacelle geometries and fan sources to demonstrate the feasibility of this approach for industry scale problems.

A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences

NASA Astrophysics Data System (ADS)

Alderliesten, Tanja; Bosman, Peter A. N.; Sonke, Jan-Jakob; Bel, Arjan

2014-03-01

Currently, two major challenges dominate the field of deformable image registration. The first challenge is related to the tuning of the developed methods to specific problems (i.e. how to best combine different objectives such as similarity measure and transformation effort). This is one of the reasons why, despite significant progress, clinical implementation of such techniques has proven to be difficult. The second challenge is to account for large anatomical differences (e.g. large deformations, (dis)appearing structures) that occurred between image acquisitions. In this paper, we study a framework based on multi-objective optimization to improve registration robustness and to simplify tuning for specific applications. Within this framework we specifically consider the use of an advanced model-based evolutionary algorithm for optimization and a dual-dynamic transformation model (i.e. two "non-fixed" grids: one for the source- and one for the target image) to accommodate for large anatomical differences. The framework computes and presents multiple outcomes that represent efficient trade-offs between the different objectives (a so-called Pareto front). In image processing it is common practice, for reasons of robustness and accuracy, to use a multi-resolution strategy. This is, however, only well-established for single-objective registration methods. Here we describe how such a strategy can be realized for our multi-objective approach and compare its results with a single-resolution strategy. For this study we selected the case of prone-supine breast MRI registration. Results show that the well-known advantages of a multi-resolution strategy are successfully transferred to our multi-objective approach, resulting in superior (i.e. Pareto-dominating) outcomes.

Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture.

PubMed

Ferrández-Pastor, Francisco Javier; García-Chamizo, Juan Manuel; Nieto-Hidalgo, Mario; Mora-Pascual, Jerónimo; Mora-Martínez, José

2016-07-22

The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched.

Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture

PubMed Central

Ferrández-Pastor, Francisco Javier; García-Chamizo, Juan Manuel; Nieto-Hidalgo, Mario; Mora-Pascual, Jerónimo; Mora-Martínez, José

2016-01-01

The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched. PMID:27455265

Boundary element based multiresolution shape optimisation in electrostatics

NASA Astrophysics Data System (ADS)

Bandara, Kosala; Cirak, Fehmi; Of, Günther; Steinbach, Olaf; Zapletal, Jan

2015-09-01

We consider the shape optimisation of high-voltage devices subject to electrostatic field equations by combining fast boundary elements with multiresolution subdivision surfaces. The geometry of the domain is described with subdivision surfaces and different resolutions of the same geometry are used for optimisation and analysis. The primal and adjoint problems are discretised with the boundary element method using a sufficiently fine control mesh. For shape optimisation the geometry is updated starting from the coarsest control mesh with increasingly finer control meshes. The multiresolution approach effectively prevents the appearance of non-physical geometry oscillations in the optimised shapes. Moreover, there is no need for mesh regeneration or smoothing during the optimisation due to the absence of a volume mesh. We present several numerical experiments and one industrial application to demonstrate the robustness and versatility of the developed approach.

Coil optimisation for transcranial magnetic stimulation in realistic head geometry.

PubMed

Koponen, Lari M; Nieminen, Jaakko O; Mutanen, Tuomas P; Stenroos, Matti; Ilmoniemi, Risto J

Transcranial magnetic stimulation (TMS) allows focal, non-invasive stimulation of the cortex. A TMS pulse is inherently weakly coupled to the cortex; thus, magnetic stimulation requires both high current and high voltage to reach sufficient intensity. These requirements limit, for example, the maximum repetition rate and the maximum number of consecutive pulses with the same coil due to the rise of its temperature. To develop methods to optimise, design, and manufacture energy-efficient TMS coils in realistic head geometry with an arbitrary overall coil shape. We derive a semi-analytical integration scheme for computing the magnetic field energy of an arbitrary surface current distribution, compute the electric field induced by this distribution with a boundary element method, and optimise a TMS coil for focal stimulation. Additionally, we introduce a method for manufacturing such a coil by using Litz wire and a coil former machined from polyvinyl chloride. We designed, manufactured, and validated an optimised TMS coil and applied it to brain stimulation. Our simulations indicate that this coil requires less than half the power of a commercial figure-of-eight coil, with a 41% reduction due to the optimised winding geometry and a partial contribution due to our thinner coil former and reduced conductor height. With the optimised coil, the resting motor threshold of abductor pollicis brevis was reached with the capacitor voltage below 600 V and peak current below 3000 A. The described method allows designing practical TMS coils that have considerably higher efficiency than conventional figure-of-eight coils. Copyright © 2017 Elsevier Inc. All rights reserved.

Economic impact of optimising antiretroviral treatment in human immunodeficiency virus-infected adults with suppressed viral load in Spain, by implementing the grade A-1 evidence recommendations of the 2015 GESIDA/National AIDS Plan.

PubMed

Ribera, Esteban; Martínez-Sesmero, José Manuel; Sánchez-Rubio, Javier; Rubio, Rafael; Pasquau, Juan; Poveda, José Luis; Pérez-Mitru, Alejandro; Roldán, Celia; Hernández-Novoa, Beatriz

2018-03-01

The objective of this study is to estimate the economic impact associated with the optimisation of triple antiretroviral treatment (ART) in patients with undetectable viral load according to the recommendations from the GeSIDA/PNS (2015) Consensus and their applicability in the Spanish clinical practice. A pharmacoeconomic model was developed based on data from a National Hospital Prescription Survey on ART (2014) and the A-I evidence recommendations for the optimisation of ART from the GeSIDA/PNS (2015) consensus. The optimisation model took into account the willingness to optimise a particular regimen and other assumptions, and the results were validated by an expert panel in HIV infection (Infectious Disease Specialists and Hospital Pharmacists). The analysis was conducted from the NHS perspective, considering the annual wholesale price and accounting for deductions stated in the RD-Law 8/2010 and the VAT. The expert panel selected six optimisation strategies, and estimated that 10,863 (13.4%) of the 80,859 patients in Spain currently on triple ART, would be candidates to optimise their ART, leading to savings of €15.9M/year (2.4% of total triple ART drug cost). The most feasible strategies (>40% of patients candidates for optimisation, n=4,556) would be optimisations to ATV/r+3TC therapy. These would produce savings between €653 and €4,797 per patient per year depending on baseline triple ART. Implementation of the main optimisation strategies recommended in the GeSIDA/PNS (2015) Consensus into Spanish clinical practice would lead to considerable savings, especially those based in dual therapy with ATV/r+3TC, thus contributing to the control of pharmaceutical expenditure and NHS sustainability. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

The use of genomic coancestry matrices in the optimisation of contributions to maintain genetic diversity at specific regions of the genome.

PubMed

Gómez-Romano, Fernando; Villanueva, Beatriz; Fernández, Jesús; Woolliams, John A; Pong-Wong, Ricardo

2016-01-13

Optimal contribution methods have proved to be very efficient for controlling the rates at which coancestry and inbreeding increase and therefore, for maintaining genetic diversity. These methods have usually relied on pedigree information for estimating genetic relationships between animals. However, with the large amount of genomic information now available such as high-density single nucleotide polymorphism (SNP) chips that contain thousands of SNPs, it becomes possible to calculate more accurate estimates of relationships and to target specific regions in the genome where there is a particular interest in maximising genetic diversity. The objective of this study was to investigate the effectiveness of using genomic coancestry matrices for: (1) minimising the loss of genetic variability at specific genomic regions while restricting the overall loss in the rest of the genome; or (2) maximising the overall genetic diversity while restricting the loss of diversity at specific genomic regions. Our study shows that the use of genomic coancestry was very successful at minimising the loss of diversity and outperformed the use of pedigree-based coancestry (genetic diversity even increased in some scenarios). The results also show that genomic information allows a targeted optimisation to maintain diversity at specific genomic regions, whether they are linked or not. The level of variability maintained increased when the targeted regions were closely linked. However, such targeted management leads to an important loss of diversity in the rest of the genome and, thus, it is necessary to take further actions to constrain this loss. Optimal contribution methods also proved to be effective at restricting the loss of diversity in the rest of the genome, although the resulting rate of coancestry was higher than the constraint imposed. The use of genomic matrices when optimising contributions permits the control of genetic diversity and inbreeding at specific regions of the genome through the minimisation of partial genomic coancestry matrices. The formula used to predict coancestry in the next generation produces biased results and therefore it is necessary to refine the theory of genetic contributions when genomic matrices are used to optimise contributions.

Object recognition through a multi-mode fiber

NASA Astrophysics Data System (ADS)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

Secure content objects

DOEpatents

Evans, William D [Cupertino, CA

2009-02-24

A secure content object protects electronic documents from unauthorized use. The secure content object includes an encrypted electronic document, a multi-key encryption table having at least one multi-key component, an encrypted header and a user interface device. The encrypted document is encrypted using a document encryption key associated with a multi-key encryption method. The encrypted header includes an encryption marker formed by a random number followed by a derivable variation of the same random number. The user interface device enables a user to input a user authorization. The user authorization is combined with each of the multi-key components in the multi-key encryption key table and used to try to decrypt the encrypted header. If the encryption marker is successfully decrypted, the electronic document may be decrypted. Multiple electronic documents or a document and annotations may be protected by the secure content object.

Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates

NASA Astrophysics Data System (ADS)

Todorovic, Andrijana; Plavsic, Jasna

2015-04-01

A conceptual rainfall-runoff model is defined by its structure and parameters, which are commonly inferred through model calibration. Parameter estimates depend on objective function(s), optimisation method, and calibration period. Model calibration over different periods may result in dissimilar parameter estimates, while model efficiency decreases outside calibration period. Problem of model (parameter) transferability, which conditions reliability of hydrologic simulations, has been investigated for decades. In this paper, dependence of the parameter estimates and model performance on calibration period is analysed. The main question that is addressed is: are there any changes in optimised parameters and model efficiency that can be linked to the changes in hydrologic or meteorological variables (flow, precipitation and temperature)? Conceptual, semi-distributed HBV-light model is calibrated over five-year periods shifted by a year (sliding time windows). Length of the calibration periods is selected to enable identification of all parameters. One water year of model warm-up precedes every simulation, which starts with the beginning of a water year. The model is calibrated using the built-in GAP optimisation algorithm. The objective function used for calibration is composed of Nash-Sutcliffe coefficient for flows and logarithms of flows, and volumetric error, all of which participate in the composite objective function with approximately equal weights. Same prior parameter ranges are used in all simulations. The model is calibrated against flows observed at the Slovac stream gauge on the Kolubara River in Serbia (records from 1954 to 2013). There are no trends in precipitation nor in flows, however, there is a statistically significant increasing trend in temperatures at this catchment. Parameter variability across the calibration periods is quantified in terms of standard deviations of normalised parameters, enabling detection of the most variable parameters. Correlation coefficients among optimised model parameters and total precipitation P, mean temperature T and mean flow Q are calculated to give an insight into parameter dependence on the hydrometeorological drivers. The results reveal high sensitivity of almost all model parameters towards calibration period. The highest variability is displayed by the refreezing coefficient, water holding capacity, and temperature gradient. The only statistically significant (decreasing) trend is detected in the evapotranspiration reduction threshold. Statistically significant correlation is detected between the precipitation gradient and precipitation depth, and between the time-area histogram base and flows. All other correlations are not statistically significant, implying that changes in optimised parameters cannot generally be linked to the changes in P, T or Q. As for the model performance, the model reproduces the observed runoff satisfactorily, though the runoff is slightly overestimated in wet periods. The Nash-Sutcliffe efficiency coefficient (NSE) ranges from 0.44 to 0.79. Higher NSE values are obtained over wetter periods, what is supported by statistically significant correlation between NSE and flows. Overall, no systematic variations in parameters or in model performance are detected. Parameter variability may therefore rather be attributed to errors in data or inadequacies in the model structure. Further research is required to examine the impact of the calibration strategy or model structure on the variability in optimised parameters in time.

Evaluation of a stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

USDA-ARS?s Scientific Manuscript database

Hydrologic models are essential tools for environmental assessment of agricultural non-point source pollution. The automatic calibration of hydrologic models, though efficient, demands significant computational power, which can limit its application. The study objective was to investigate a cost e...

Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory

NASA Astrophysics Data System (ADS)

Ahmadi, Bahman; Nariman-zadeh, Nader; Jamali, Ali

2017-06-01

In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms.

Metaheuristic optimisation methods for approximate solving of singular boundary value problems

NASA Astrophysics Data System (ADS)

Sadollah, Ali; Yadav, Neha; Gao, Kaizhou; Su, Rong

2017-07-01

This paper presents a novel approximation technique based on metaheuristics and weighted residual function (WRF) for tackling singular boundary value problems (BVPs) arising in engineering and science. With the aid of certain fundamental concepts of mathematics, Fourier series expansion, and metaheuristic optimisation algorithms, singular BVPs can be approximated as an optimisation problem with boundary conditions as constraints. The target is to minimise the WRF (i.e. error function) constructed in approximation of BVPs. The scheme involves generational distance metric for quality evaluation of the approximate solutions against exact solutions (i.e. error evaluator metric). Four test problems including two linear and two non-linear singular BVPs are considered in this paper to check the efficiency and accuracy of the proposed algorithm. The optimisation task is performed using three different optimisers including the particle swarm optimisation, the water cycle algorithm, and the harmony search algorithm. Optimisation results obtained show that the suggested technique can be successfully applied for approximate solving of singular BVPs.

Polinsar Experiments of Multi-Mode X-Band Data Over South Area of China

NASA Astrophysics Data System (ADS)

Lu, L.; Yan, Q.; Duan, M.; Zhang, Y.

2012-08-01

This paper makes the polarimetric and polarimetric interferometric synthetic aperture radar (PolInSAR) experiments with the high-resolution X-band data acquired by Multi-mode airborne SAR system over an area around Linshui, south of China containing tropic vegetation and urban areas. Polarimetric analysis for typical tropic vegetations and man-made objects are presented, some polarimetric descriptors sensitive to vegetations and man-made objects are selected. Then, the PolInSAR information contained in the data is investigated, considering characteristics of the Multi-mode-XSAR dataset, a dual-baseline polarimetric interferometry method is proposed in this paper. The method both guarantees the high coherence on fully polarimetric data and combines the benefits of short and long baseline that helpful to the phase unwrapping and height sensitivity promotion. PolInSAR experiment results displayed demonstrates Multi-mode-XSAR datasets have intuitive capabilities for amount of application of land classification, objects detection and DSM mapping.

MO-DE-204-03: Radiology Dose Optimisation - An Australian Perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schick, D.

2016-06-15

The main topic of the session is to show how dose optimization is being implemented in various regions of the world, including Europe, Australia, North America and other regions. A multi-national study conducted under International Atomic Energy Agency (IAEA) across more than 50 less resourced countries gave insight into patient radiation doses and safety practices in CT, mammography, radiography and interventional procedures, both for children and adults. An important outcome was the capability development on dose assessment and management. An overview of recent European projects related to CT radiation dose and optimization both to adults and children will be presented.more » Existing data on DRLs together with a European methodology proposed on establishing and using DRLs for paediatric radiodiagnostic imaging and interventional radiology practices will be shown. Compared with much of Europe at least, many Australian imaging practices are relatively new to the task of diagnostic imaging dose optimisation. In 2008 the Australian Government prescribed a requirement to periodically compare patient radiation doses with diagnostic reference levels (DRLs), where DRLs have been established. Until recently, Australia had only established DRLs for computed tomography (CT). Regardless, both professional society and individual efforts to improved data collection and develop optimisation strategies across a range of modalities continues. Progress in this field, principally with respect to CT and interventional fluoroscopy will be presented. In the US, dose reduction and optimization efforts for computed tomography have been promoted and mandated by several organizations and accrediting entities. This presentation will cover the general motivation, implementation, and implications of such efforts. Learning Objectives: Understand importance of the dose optimization in Diagnostic Radiology. See how this goal is achieved in different regions of the World. Learn about the global trend in the dose optimization and future prospectives. M. Rehani, The work was a part of the work of IAEA where I was an employee and IAEA is a United Nations organization.« less

Fabrication of Organic Radar Absorbing Materials: A Report on the TIF Project

DTIC Science & Technology

2005-05-01

thickness, permittivity and permeability. The ability to measure the permittivity and permeability is an essential requirement for designing an optimised...absorber. And good optimisations codes are required in order to achieve the best possible absorber designs . In this report, the results from a...through measurement of their conductivity and permittivity at microwave frequencies. Methods were then developed for optimising the design of

Chromatic illumination discrimination ability reveals that human colour constancy is optimised for blue daylight illuminations.

PubMed

Pearce, Bradley; Crichton, Stuart; Mackiewicz, Michal; Finlayson, Graham D; Hurlbert, Anya

2014-01-01

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed.

Application of Three Existing Stope Boundary Optimisation Methods in an Operating Underground Mine

NASA Astrophysics Data System (ADS)

Erdogan, Gamze; Yavuz, Mahmut

2017-12-01

The underground mine planning and design optimisation process have received little attention because of complexity and variability of problems in underground mines. Although a number of optimisation studies and software tools are available and some of them, in special, have been implemented effectively to determine the ultimate-pit limits in an open pit mine, there is still a lack of studies for optimisation of ultimate stope boundaries in underground mines. The proposed approaches for this purpose aim at maximizing the economic profit by selecting the best possible layout under operational, technical and physical constraints. In this paper, the existing three heuristic techniques including Floating Stope Algorithm, Maximum Value Algorithm and Mineable Shape Optimiser (MSO) are examined for optimisation of stope layout in a case study. Each technique is assessed in terms of applicability, algorithm capabilities and limitations considering the underground mine planning challenges. Finally, the results are evaluated and compared.

Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective, multi-site supply-chain planning problem

NASA Astrophysics Data System (ADS)

Ayadi, Omar; Felfel, Houssem; Masmoudi, Faouzi

2017-07-01

The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.

Evolutionary algorithms for multi-objective optimization: fuzzy preference aggregation and multisexual EAs

NASA Astrophysics Data System (ADS)

Bonissone, Stefano R.

2001-11-01

There are many approaches to solving multi-objective optimization problems using evolutionary algorithms. We need to select methods for representing and aggregating preferences, as well as choosing strategies for searching in multi-dimensional objective spaces. First we suggest the use of linguistic variables to represent preferences and the use of fuzzy rule systems to implement tradeoff aggregations. After a review of alternatives EA methods for multi-objective optimizations, we explore the use of multi-sexual genetic algorithms (MSGA). In using a MSGA, we need to modify certain parts of the GAs, namely the selection and crossover operations. The selection operator groups solutions according to their gender tag to prepare them for crossover. The crossover is modified by appending a gender tag at the end of the chromosome. We use single and double point crossovers. We determine the gender of the offspring by the amount of genetic material provided by each parent. The parent that contributed the most to the creation of a specific offspring determines the gender that the offspring will inherit. This is still a work in progress, and in the conclusion we examine many future extensions and experiments.

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm.

PubMed

Devikanniga, D; Joshua Samuel Raj, R

2018-04-01

Osteoporosis is a life threatening disease which commonly affects women mostly after their menopause. It primarily causes mild bone fractures, which on advanced stage leads to the death of an individual. The diagnosis of osteoporosis is done based on bone mineral density (BMD) values obtained through various clinical methods experimented from various skeletal regions. The main objective of the authors' work is to develop a hybrid classifier model that discriminates the osteoporotic patient from healthy person, based on BMD values. In this Letter, the authors propose the monarch butterfly optimisation-based artificial neural network classifier which helps in earlier diagnosis and prevention of osteoporosis. The experiments were conducted using 10-fold cross-validation method for two datasets lumbar spine and femoral neck. The results were compared with other similar hybrid approaches. The proposed method resulted with the accuracy, specificity and sensitivity of 97.9% ± 0.14, 98.33% ± 0.03 and 95.24% ± 0.08, respectively, for lumbar spine dataset and 99.3% ± 0.16%, 99.2% ± 0.13 and 100, respectively, for femoral neck dataset. Further, its performance is compared using receiver operating characteristics analysis and Wilcoxon signed-rank test. The results proved that the proposed classifier is efficient and it outperformed the other approaches in all the cases.

Automated model optimisation using the Cylc workflow engine (Cyclops v1.0)

NASA Astrophysics Data System (ADS)

Gorman, Richard M.; Oliver, Hilary J.

2018-06-01

Most geophysical models include many parameters that are not fully determined by theory, and can be tuned to improve the model's agreement with available data. We might attempt to automate this tuning process in an objective way by employing an optimisation algorithm to find the set of parameters that minimises a cost function derived from comparing model outputs with measurements. A number of algorithms are available for solving optimisation problems, in various programming languages, but interfacing such software to a complex geophysical model simulation presents certain challenges. To tackle this problem, we have developed an optimisation suite (Cyclops) based on the Cylc workflow engine that implements a wide selection of optimisation algorithms from the NLopt Python toolbox (Johnson, 2014). The Cyclops optimisation suite can be used to calibrate any modelling system that has itself been implemented as a (separate) Cylc model suite, provided it includes computation and output of the desired scalar cost function. A growing number of institutions are using Cylc to orchestrate complex distributed suites of interdependent cycling tasks within their operational forecast systems, and in such cases application of the optimisation suite is particularly straightforward. As a test case, we applied the Cyclops to calibrate a global implementation of the WAVEWATCH III (v4.18) third-generation spectral wave model, forced by ERA-Interim input fields. This was calibrated over a 1-year period (1997), before applying the calibrated model to a full (1979-2016) wave hindcast. The chosen error metric was the spatial average of the root mean square error of hindcast significant wave height compared with collocated altimeter records. We describe the results of a calibration in which up to 19 parameters were optimised.

Production of biosolid fuels from municipal sewage sludge: Technical and economic optimisation.

PubMed

Wzorek, Małgorzata; Tańczuk, Mariusz

2015-08-01

The article presents the technical and economic analysis of the production of fuels from municipal sewage sludge. The analysis involved the production of two types of fuel compositions: sewage sludge with sawdust (PBT fuel) and sewage sludge with meat and bone meal (PBM fuel). The technology of the production line of these sewage fuels was proposed and analysed. The main objective of the study is to find the optimal production capacity. The optimisation analysis was performed for the adopted technical and economic parameters under Polish conditions. The objective function was set as a maximum of the net present value index and the optimisation procedure was carried out for the fuel production line input capacity from 0.5 to 3 t h(-1), using the search step 0.5 t h(-1). On the basis of technical and economic assumptions, economic efficiency indexes of the investment were determined for the case of optimal line productivity. The results of the optimisation analysis show that under appropriate conditions, such as prices of components and prices of produced fuels, the production of fuels from sewage sludge can be profitable. In the case of PBT fuel, calculated economic indexes show the best profitability for the capacity of a plant over 1.5 t h(-1) output, while production of PBM fuel is beneficial for a plant with the maximum of searched capacities: 3.0 t h(-1). Sensitivity analyses carried out during the investigation show that influence of both technical and economic assessments on the location of maximum of objective function (net present value) is significant. © The Author(s) 2015.

A study on the impact of prioritising emergency department arrivals on the patient waiting time.

PubMed

Van Bockstal, Ellen; Maenhout, Broos

2018-05-03

In the past decade, the crowding of the emergency department has gained considerable attention of researchers as the number of medical service providers is typically insufficient to fulfil the demand for emergency care. In this paper, we solve the stochastic emergency department workforce planning problem and consider the planning of nurses and physicians simultaneously for a real-life case study in Belgium. We study the patient arrival pattern of the emergency department in depth and consider different patient acuity classes by disaggregating the arrival pattern. We determine the personnel staffing requirements and the design of the shifts based on the patient arrival rates per acuity class such that the resource staffing cost and the weighted patient waiting time are minimised. In order to solve this multi-objective optimisation problem, we construct a Pareto set of optimal solutions via the -constraints method. For a particular staffing composition, the proposed model minimises the patient waiting time subject to upper bounds on the staffing size using the Sample Average Approximation Method. In our computational experiments, we discern the impact of prioritising the emergency department arrivals. Triaging results in lower patient waiting times for higher priority acuity classes and to a higher waiting time for the lowest priority class, which does not require immediate care. Moreover, we perform a sensitivity analysis to verify the impact of the arrival and service pattern characteristics, the prioritisation weights between different acuity classes and the incorporated shift flexibility in the model.

Analysis of the car body stability performance after coupler jack-knifing during braking

NASA Astrophysics Data System (ADS)

Guo, Lirong; Wang, Kaiyun; Chen, Zaigang; Shi, Zhiyong; Lv, Kaikai; Ji, Tiancheng

2018-06-01

This paper aims to improve car body stability performance by optimising locomotive parameters when coupler jack-knifing occurs during braking. In order to prevent car body instability behaviour caused by coupler jack-knifing, a multi-locomotive simulation model and a series of field braking tests are developed to analyse the influence of the secondary suspension and the secondary lateral stopper on the car body stability performance during braking. According to simulation and test results, increasing secondary lateral stiffness contributes to limit car body yaw angle during braking. However, it seriously affects the dynamic performance of the locomotive. For the secondary lateral stopper, its lateral stiffness and free clearance have a significant influence on improving the car body stability capacity, and have less effect on the dynamic performance of the locomotive. An optimised measure was proposed and adopted on the test locomotive. For the optimised locomotive, the lateral stiffness of secondary lateral stopper is increased to 7875 kN/m, while its free clearance is decreased to 10 mm. The optimised locomotive has excellent dynamic and safety performance. Comparing with the original locomotive, the maximum car body yaw angle and coupler rotation angle of the optimised locomotive were reduced by 59.25% and 53.19%, respectively, according to the practical application. The maximum derailment coefficient was 0.32, and the maximum wheelset lateral force was 39.5 kN. Hence, reasonable parameters of secondary lateral stopper can improve the car body stability capacity and the running safety of the heavy haul locomotive.

Automatic 3D kidney segmentation based on shape constrained GC-OAAM

NASA Astrophysics Data System (ADS)

Chen, Xinjian; Summers, Ronald M.; Yao, Jianhua

2011-03-01

The kidney can be classified into three main tissue types: renal cortex, renal medulla and renal pelvis (or collecting system). Dysfunction of different renal tissue types may cause different kidney diseases. Therefore, accurate and efficient segmentation of kidney into different tissue types plays a very important role in clinical research. In this paper, we propose an automatic 3D kidney segmentation method which segments the kidney into the three different tissue types: renal cortex, medulla and pelvis. The proposed method synergistically combines active appearance model (AAM), live wire (LW) and graph cut (GC) methods, GC-OAAM for short. Our method consists of two main steps. First, a pseudo 3D segmentation method is employed for kidney initialization in which the segmentation is performed slice-by-slice via a multi-object oriented active appearance model (OAAM) method. An improved iterative model refinement algorithm is proposed for the AAM optimization, which synergistically combines the AAM and LW method. Multi-object strategy is applied to help the object initialization. The 3D model constraints are applied to the initialization result. Second, the object shape information generated from the initialization step is integrated into the GC cost computation. A multi-label GC method is used to segment the kidney into cortex, medulla and pelvis. The proposed method was tested on 19 clinical arterial phase CT data sets. The preliminary results showed the feasibility and efficiency of the proposed method.

a Heuristic Approach for Multi Objective Distribution Feeder Reconfiguration: Using Fuzzy Sets in Normalization of Objective Functions

NASA Astrophysics Data System (ADS)

Milani, Armin Ebrahimi; Haghifam, Mahmood Reza

2008-10-01

The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. In this paper each objectives is normalized with inspiration from fuzzy sets-to cause optimization more flexible- and formulized as a unique multi-objective function. The genetic algorithm is used for solving the suggested model, in which there is no risk of non-liner objective functions and constraints. The effectiveness of the proposed method is demonstrated through the examples.

Electrochemical determination of estrogenic compound bisphenol F in food packaging using carboxyl functionalized multi-walled carbon nanotubes modified glassy carbon electrode.

PubMed

Wang, Xin; Yang, Lijun; Jin, Xudong; Zhang, Lei

2014-08-15

A simple and highly sensitive electroanalytical method for the determination of bisphenol F (BPF) was developed, which was carried out on multi-walled carbon nanotubes-COOH (MWCNT-COOH) modified glassy carbon electrode (GCE) using cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The results showed that MWCNT-COOH remarkably enhanced the oxidation of BPF, which improved the anodic peak current of BPF significantly. The mechanism was oxidation of BPF lose electrons on the electrode surface via adsorption-controlled process, electrode reaction is the two electrons/two protons process. Under the optimised conditions, the oxidation peak current was proportional to BPF concentration the range from 0.12 to 6.01 μg mL(-1). The detection limit was 0.11 μg mL(-1) (S/N=3), and the relative standard deviation (R.S.D.) was 3.5% (n=9). Moreover, the MWCNT-COOH/GCE electrode showed good reproducibility, stability and anti-interference. Therefore, the proposed method was successfully applied to determine BPF in food packing and the results were satisfactory. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automatic registration of optical imagery with 3d lidar data using local combined mutual information

NASA Astrophysics Data System (ADS)

Parmehr, E. G.; Fraser, C. S.; Zhang, C.; Leach, J.

2013-10-01

Automatic registration of multi-sensor data is a basic step in data fusion for photogrammetric and remote sensing applications. The effectiveness of intensity-based methods such as Mutual Information (MI) for automated registration of multi-sensor image has been previously reported for medical and remote sensing applications. In this paper, a new multivariable MI approach that exploits complementary information of inherently registered LiDAR DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented. LiDAR DSM and intensity information has been utilised in measuring the similarity of LiDAR and optical imagery via the Combined MI. An effective histogramming technique is adopted to facilitate estimation of a 3D probability density function (pdf). In addition, a local similarity measure is introduced to decrease the complexity of optimisation at higher dimensions and computation cost. Therefore, the reliability of registration is improved due to the use of redundant observations of similarity. The performance of the proposed method for registration of satellite and aerial images with LiDAR data in urban and rural areas is experimentally evaluated and the results obtained are discussed.

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

PubMed

Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

2017-07-01

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimisation of the Management of Higher Activity Waste in the UK - 13537

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walsh, Ciara; Buckley, Matthew

2013-07-01

The Upstream Optioneering project was created in the Nuclear Decommissioning Authority (UK) to support the development and implementation of significant opportunities to optimise activities across all the phases of the Higher Activity Waste management life cycle (i.e. retrieval, characterisation, conditioning, packaging, storage, transport and disposal). The objective of the Upstream Optioneering project is to work in conjunction with other functions within NDA and the waste producers to identify and deliver solutions to optimise the management of higher activity waste. Historically, optimisation may have occurred on aspects of the waste life cycle (considered here to include retrieval, conditioning, treatment, packaging, interimmore » storage, transport to final end state, which may be geological disposal). By considering the waste life cycle as a whole, critical analysis of assumed constraints may lead to cost savings for the UK Tax Payer. For example, it may be possible to challenge the requirements for packaging wastes for disposal to deliver an optimised waste life cycle. It is likely that the challenges faced in the UK are shared in other countries. It is therefore likely that the opportunities identified may also apply elsewhere, with the potential for sharing information to enable value to be shared. (authors)« less

Optimising Inter-Disciplinary Problem-Based Learning in Postgraduate Environmental and Science Education: Recommendations from a Case Study

ERIC Educational Resources Information Center

Redshaw, Clare H; Frampton, Ian

2014-01-01

As the value of multi-disciplinary working in the business and research worlds is becoming more recognised, the number of inter-disciplinary postgraduate environmental and health sciences courses is also increasing. Equally, the popularity of problem-based learning (PBL) is expected to grow and influence instructional approaches in many…

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Availability and feasibility of structured, routine collection of comorbidity data in a colorectal cancer multi-disciplinary team (MDT) setting.

PubMed

Abukar, A A; Ramsanahie, A; Martin-Lumbard, K; Herrington, E R; Winslow, V; Wong, S; Ahmed, S; Thaha, M A

2018-05-03

Availability of comorbidity assessment at multi-disciplinary team (MDT) discussions is cornerstone in making the MDT process more robust and decisive in optimising treatment and improving quality of survivorship. Comorbidity assessments using tools, such as the ACE-27 questionnaire would aid in optimising the decision-making process at MDTs so that treatment decisions can be made without delay. This study determined the availability of comorbidity data in a CRC MDT and the feasibility of routine comorbidity data collection using the validated ACE-27 questionnaire. Secondary aims determined the optimal time and method of collecting comorbidity data. A retrospective mapping exercise (phase I; 6-months) examined the availability of comorbidity data within the MDT. Phase II prospectively collected comorbidity data using ACE-27 for a 3-month period following a short pilot. In phase I, 73/135 (54%) patients had comorbidity data readily available informing the MDT discussion; 62 patients lacked this information. After a review of the patient records, it was clear that 41 of these 62 also had comorbidities and 21 out of the 135 had ≥ 2 major system disorders. Common referral sources to the MDT were surgical outpatient clinics (42%) and the endoscopy unit (13%). The average lead-time from referral to MDT discussion was 14 days. In phase II, an ACE-27 questionnaire was prospectively administered in 50 patients, mean age 54 years (range 20-84). Male: female ratio 26:24. Average time to administer ACE-27 was 4.8 min (range 1-15). The phase I study confirmed the widely acknowledged view of poor comorbidity data availability within a CRC MDT. Phase II demonstrated the feasibility of routinely collecting comorbidity data using ACE-27.

Systems and methods for detecting an image of an object using multi-beam imaging from an X-ray beam having a polychromatic distribution

DOEpatents

Parham, Christopher A; Zhong, Zhong; Pisano, Etta; Connor, Jr., Dean M

2015-03-03

Systems and methods for detecting an image of an object using a multi-beam imaging system from an x-ray beam having a polychromatic energy distribution are disclosed. According to one aspect, a method can include generating a first X-ray beam having a polychromatic energy distribution. Further, the method can include positioning a plurality of monochromator crystals in a predetermined position to directly intercept the first X-ray beam such that a plurality of second X-ray beams having predetermined energy levels are produced. Further, an object can be positioned in the path of the second X-ray beams for transmission of the second X-ray beams through the object and emission from the object as transmitted X-ray beams. The transmitted X-ray beams can each be directed at an angle of incidence upon one or more crystal analyzers. Further, an image of the object can be detected from the beams diffracted from the analyzer crystals.

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions.

PubMed

Qu, Shaojian; Ji, Ying

2016-01-01

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Wang, J

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidentialmore » reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.« less

The role of multi-target policy instruments in agri-environmental policy mixes.

PubMed

Schader, Christian; Lampkin, Nicholas; Muller, Adrian; Stolze, Matthias

2014-12-01

The Tinbergen Rule has been used to criticise multi-target policy instruments for being inefficient. The aim of this paper is to clarify the role of multi-target policy instruments using the case of agri-environmental policy. Employing an analytical linear optimisation model, this paper demonstrates that there is no general contradiction between multi-target policy instruments and the Tinbergen Rule, if multi-target policy instruments are embedded in a policy-mix with a sufficient number of targeted instruments. We show that the relation between cost-effectiveness of the instruments, related to all policy targets, is the key determinant for an economically sound choice of policy instruments. If economies of scope with respect to achieving policy targets are realised, a higher cost-effectiveness of multi-target policy instruments can be achieved. Using the example of organic farming support policy, we discuss several reasons why economies of scope could be realised by multi-target agri-environmental policy instruments. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sampling with poling-based flux balance analysis: optimal versus sub-optimal flux space analysis of Actinobacillus succinogenes.

PubMed

Binns, Michael; de Atauri, Pedro; Vlysidis, Anestis; Cascante, Marta; Theodoropoulos, Constantinos

2015-02-18

Flux balance analysis is traditionally implemented to identify the maximum theoretical flux for some specified reaction and a single distribution of flux values for all the reactions present which achieve this maximum value. However it is well known that the uncertainty in reaction networks due to branches, cycles and experimental errors results in a large number of combinations of internal reaction fluxes which can achieve the same optimal flux value. In this work, we have modified the applied linear objective of flux balance analysis to include a poling penalty function, which pushes each new set of reaction fluxes away from previous solutions generated. Repeated poling-based flux balance analysis generates a sample of different solutions (a characteristic set), which represents all the possible functionality of the reaction network. Compared to existing sampling methods, for the purpose of generating a relatively "small" characteristic set, our new method is shown to obtain a higher coverage than competing methods under most conditions. The influence of the linear objective function on the sampling (the linear bias) constrains optimisation results to a subspace of optimal solutions all producing the same maximal fluxes. Visualisation of reaction fluxes plotted against each other in 2 dimensions with and without the linear bias indicates the existence of correlations between fluxes. This method of sampling is applied to the organism Actinobacillus succinogenes for the production of succinic acid from glycerol. A new method of sampling for the generation of different flux distributions (sets of individual fluxes satisfying constraints on the steady-state mass balances of intermediates) has been developed using a relatively simple modification of flux balance analysis to include a poling penalty function inside the resulting optimisation objective function. This new methodology can achieve a high coverage of the possible flux space and can be used with and without linear bias to show optimal versus sub-optimal solution spaces. Basic analysis of the Actinobacillus succinogenes system using sampling shows that in order to achieve the maximal succinic acid production CO₂ must be taken into the system. Solutions involving release of CO₂ all give sub-optimal succinic acid production.

Optimising, generalising and integrating educational practice using neuroscience

NASA Astrophysics Data System (ADS)

Colvin, Robert

2016-07-01

Practical collaboration at the intersection of education and neuroscience research is difficult because the combined discipline encompasses both the activity of microscopic neurons and the complex social interactions of teachers and students in a classroom. Taking a pragmatic view, this paper discusses three education objectives to which neuroscience can be effectively applied: optimising, generalising and integrating instructional techniques. These objectives are characterised by: (1) being of practical importance; (2) building on existing education and cognitive research; and (3) being infeasible to address based on behavioural experiments alone. The focus of the neuroscientific aspect of collaborative research should be on the activity of the brain before, during and after learning a task, as opposed to performance of a task. The objectives are informed by literature that highlights possible pitfalls with educational neuroscience research, and are described with respect to the static and dynamic aspects of brain physiology that can be measured by current technology.

On the design and optimisation of new fractal antenna using PSO

NASA Astrophysics Data System (ADS)

Rani, Shweta; Singh, A. P.

2013-10-01

An optimisation technique for newly shaped fractal structure using particle swarm optimisation with curve fitting is presented in this article. The aim of particle swarm optimisation is to find the geometry of the antenna for the required user-defined frequency. To assess the effectiveness of the presented method, a set of representative numerical simulations have been done and the results are compared with the measurements from experimental prototypes built according to the design specifications coming from the optimisation procedure. The proposed fractal antenna resonates at the 5.8 GHz industrial, scientific and medical band which is suitable for wireless telemedicine applications. The antenna characteristics have been studied using extensive numerical simulations and are experimentally verified. The antenna exhibits well-defined radiation patterns over the band.

Multi-Mounted X-Ray Computed Tomography.

PubMed

Fu, Jian; Liu, Zhenzhong; Wang, Jingzheng

2016-01-01

Most existing X-ray computed tomography (CT) techniques work in single-mounted mode and need to scan the inspected objects one by one. It is time-consuming and not acceptable for the inspection in a large scale. In this paper, we report a multi-mounted CT method and its first engineering implementation. It consists of a multi-mounted scanning geometry and the corresponding algebraic iterative reconstruction algorithm. This approach permits the CT rotation scanning of multiple objects simultaneously without the increase of penetration thickness and the signal crosstalk. Compared with the conventional single-mounted methods, it has the potential to improve the imaging efficiency and suppress the artifacts from the beam hardening and the scatter. This work comprises a numerical study of the method and its experimental verification using a dataset measured with a developed multi-mounted X-ray CT prototype system. We believe that this technique is of particular interest for pushing the engineering applications of X-ray CT.

Fixed-dose combination therapy for the prevention of cardiovascular disease

PubMed Central

de Cates, Angharad N; Farr, Matthew RB; Rees, Karen; Casas, Juan P; Huffman, Mark

2014-01-01

This is the protocol for a review and there is no abstract. The objectives are as follows: To determine the effectiveness of fixed-dose combination therapy on optimising CVD risk factors and reducing CVD fatal and non-fatal events for both primary and secondary prevention of CVD. Details of CVD events and risk factors included are listed in the methods. We will also determine any adverse events associated with taking fixed-dose combination therapy. This will include studies conducted in both developed and developing regions of the world. PMID:25267903

Stability, structure and scale: improvements in multi-modal vessel extraction for SEEG trajectory planning.

PubMed

Zuluaga, Maria A; Rodionov, Roman; Nowell, Mark; Achhala, Sufyan; Zombori, Gergely; Mendelson, Alex F; Cardoso, M Jorge; Miserocchi, Anna; McEvoy, Andrew W; Duncan, John S; Ourselin, Sébastien

2015-08-01

Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying significantly associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer-assisted planning systems that can optimise the safety profile of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Twelve paired data sets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coefficient was 0.89 ± 0.04, representing a statistically significantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ± 0.03). Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity.

Optimisation of multi-layer rotationally moulded foamed structures

NASA Astrophysics Data System (ADS)

Pritchard, A. J.; McCourt, M. P.; Kearns, M. P.; Martin, P. J.; Cunningham, E.

2018-05-01

Multi-layer skin-foam and skin-foam-skin sandwich constructions are of increasing interest in the rotational moulding process for two reasons. Firstly, multi-layer constructions can improve the thermal insulation properties of a part. Secondly, foamed polyethylene sandwiched between solid polyethylene skins can increase the mechanical properties of rotationally moulded structural components, in particular increasing flexural properties and impact strength (IS). The processing of multiple layers of polyethylene and polyethylene foam presents unique challenges such as the control of chemical blowing agent decomposition temperature, and the optimisation of cooling rates to prevent destruction of the foam core; therefore, precise temperature control is paramount to success. Long cooling cycle times are associated with the creation of multi-layer foam parts due to their insulative nature; consequently, often making the costs of production prohibitive. Devices such as Rotocooler®, a rapid internal mould water spray cooling system, have been shown to have the potential to significantly decrease cooling times in rotational moulding. It is essential to monitor and control such devices to minimise the warpage associated with the rapid cooling of a moulding from only one side. The work presented here demonstrates the use of threaded thermocouples to monitor the polymer melt in multi-layer sandwich constructions, in order to analyse the cooling cycle of multi-layer foamed structures. A series of polyethylene skin-foam test mouldings were produced, and the effect of cooling medium on foam characteristics, mechanical properties, and process cycle time were investigated. Cooling cycle time reductions of 45%, 26%, and 29% were found for increasing (1%, 2%, and 3%) chemical blowing agent (CBA) amount when using internal water cooling technology from ˜123°C compared with forced air cooling (FAC). Subsequently, a reduction of IS for the same skin-foam parts was found to be 1%, 4%, and 16% compared with FAC.

Optimal design of groundwater remediation system using a probabilistic multi-objective fast harmony search algorithm under uncertainty

NASA Astrophysics Data System (ADS)

Luo, Qiankun; Wu, Jianfeng; Yang, Yun; Qian, Jiazhong; Wu, Jichun

2014-11-01

This study develops a new probabilistic multi-objective fast harmony search algorithm (PMOFHS) for optimal design of groundwater remediation systems under uncertainty associated with the hydraulic conductivity (K) of aquifers. The PMOFHS integrates the previously developed deterministic multi-objective optimization method, namely multi-objective fast harmony search algorithm (MOFHS) with a probabilistic sorting technique to search for Pareto-optimal solutions to multi-objective optimization problems in a noisy hydrogeological environment arising from insufficient K data. The PMOFHS is then coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, to identify the optimal design of groundwater remediation systems for a two-dimensional hypothetical test problem and a three-dimensional Indiana field application involving two objectives: (i) minimization of the total remediation cost through the engineering planning horizon, and (ii) minimization of the mass remaining in the aquifer at the end of the operational period, whereby the pump-and-treat (PAT) technology is used to clean up contaminated groundwater. Also, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology. Comprehensive analysis indicates that the proposed PMOFHS can find Pareto-optimal solutions with low variability and high reliability and is a potentially effective tool for optimizing multi-objective groundwater remediation problems under uncertainty.

Optimisation of micro-perforated cylindrical silencers in linear and nonlinear regimes

NASA Astrophysics Data System (ADS)

Bravo, Teresa; Maury, Cédric; Pinhède, Cédric

2016-02-01

This paper describes analytical and experimental studies conducted to understand the potential of lightweight non-fibrous alternatives to dissipative mufflers for in-duct noise control problems, especially under high sound pressure levels (SPLs) and in the low frequency domain. The cost-efficient multi-modal propagation method has been extended to predict nonlinear effects in the dissipation and the transmission loss (TL) of micro-perforated cylindrical liners with sub-millimetric holes diameter. A validation experiment was performed in a standing wave tube to measure the power dissipated and transmitted by a nonlocally reacting liner under moderate and high SPLs. Although nonlinear effects significantly reduce the dissipation and TL around the liner maximum damping frequency, these power quantities may be enhanced below the half-bandwidth resonance. An optimal value of the in-hole peak particle velocity has been found that maximizes the TL of locally reacting liners at low frequencies. Optimisation studies based on dissipation or TL maximization showed the sensitivity of the liner constituting parameters to variations in the design target range such as the center frequency, the levels of acoustic excitation and the nature of the surface impedance (locally or nonlocally reacting). An analysis is proposed of the deviation observed at low frequencies between the optimum impedance of the locally reacting liner under moderate SPLs and Cremer's optimum impedances.

GNAQPMS v1.1: accelerating the Global Nested Air Quality Prediction Modeling System (GNAQPMS) on Intel Xeon Phi processors

NASA Astrophysics Data System (ADS)

Wang, Hui; Chen, Huansheng; Wu, Qizhong; Lin, Junmin; Chen, Xueshun; Xie, Xinwei; Wang, Rongrong; Tang, Xiao; Wang, Zifa

2017-08-01

The Global Nested Air Quality Prediction Modeling System (GNAQPMS) is the global version of the Nested Air Quality Prediction Modeling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present the porting and optimisation of GNAQPMS on a second-generation Intel Xeon Phi processor, codenamed Knights Landing (KNL). Compared with the first-generation Xeon Phi coprocessor (codenamed Knights Corner, KNC), KNL has many new hardware features such as a bootable processor, high-performance in-package memory and ISA compatibility with Intel Xeon processors. In particular, we describe the five optimisations we applied to the key modules of GNAQPMS, including the CBM-Z gas-phase chemistry, advection, convection and wet deposition modules. These optimisations work well on both the KNL 7250 processor and the Intel Xeon E5-2697 V4 processor. They include (1) updating the pure Message Passing Interface (MPI) parallel mode to the hybrid parallel mode with MPI and OpenMP in the emission, advection, convection and gas-phase chemistry modules; (2) fully employing the 512 bit wide vector processing units (VPUs) on the KNL platform; (3) reducing unnecessary memory access to improve cache efficiency; (4) reducing the thread local storage (TLS) in the CBM-Z gas-phase chemistry module to improve its OpenMP performance; and (5) changing the global communication from writing/reading interface files to MPI functions to improve the performance and the parallel scalability. These optimisations greatly improved the GNAQPMS performance. The same optimisations also work well for the Intel Xeon Broadwell processor, specifically E5-2697 v4. Compared with the baseline version of GNAQPMS, the optimised version was 3.51 × faster on KNL and 2.77 × faster on the CPU. Moreover, the optimised version ran at 26 % lower average power on KNL than on the CPU. With the combined performance and energy improvement, the KNL platform was 37.5 % more efficient on power consumption compared with the CPU platform. The optimisations also enabled much further parallel scalability on both the CPU cluster and the KNL cluster scaled to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 and 42.2 %, respectively.

Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

NASA Astrophysics Data System (ADS)

Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

2016-03-01

The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.

Multi-point objective-oriented sequential sampling strategy for constrained robust design

NASA Astrophysics Data System (ADS)

Zhu, Ping; Zhang, Siliang; Chen, Wei

2015-03-01

Metamodelling techniques are widely used to approximate system responses of expensive simulation models. In association with the use of metamodels, objective-oriented sequential sampling methods have been demonstrated to be effective in balancing the need for searching an optimal solution versus reducing the metamodelling uncertainty. However, existing infilling criteria are developed for deterministic problems and restricted to one sampling point in one iteration. To exploit the use of multiple samples and identify the true robust solution in fewer iterations, a multi-point objective-oriented sequential sampling strategy is proposed for constrained robust design problems. In this article, earlier development of objective-oriented sequential sampling strategy for unconstrained robust design is first extended to constrained problems. Next, a double-loop multi-point sequential sampling strategy is developed. The proposed methods are validated using two mathematical examples followed by a highly nonlinear automotive crashworthiness design example. The results show that the proposed method can mitigate the effect of both metamodelling uncertainty and design uncertainty, and identify the robust design solution more efficiently than the single-point sequential sampling approach.

Hybrid insulation coordination and optimisation for 1 MV operation of pulsed electron accelerator KALI-30GW

DOE Office of Scientific and Technical Information (OSTI.GOV)

Senthil, K.; Mitra, S.; Sandeep, S., E-mail: sentilk@barc.gov.in

In a multi-gigawatt pulsed power system like KALI-30 GW, insulation coordination is required to achieve high voltages ranging from 0.3 MV to 1 MV. At the same time optimisation of the insulation parameters is required to minimize the inductance of the system, so that nanoseconds output can be achieved. The KALI-30GW pulse power system utilizes a combination of Perspex, delrin, epoxy, transformer oil, nitrogen/SF{sub 6} gas and vacuum insulation at its various stages in compressing DC high voltage to a nanoseconds pulse. This paper describes the operation and performance of the system from 400 kV to 1030 kV output voltagemore » pulse and insulation parameters utilized for obtaining peak 1 MV output. (author)« less

Determining flexor-tendon repair techniques via soft computing

NASA Technical Reports Server (NTRS)

Johnson, M.; Firoozbakhsh, K.; Moniem, M.; Jamshidi, M.

2001-01-01

An SC-based multi-objective decision-making method for determining the optimal flexor-tendon repair technique from experimental and clinical survey data, and with variable circumstances, was presented. Results were compared with those from the Taguchi method. Using the Taguchi method results in the need to perform ad-hoc decisions when the outcomes for individual objectives are contradictory to a particular preference or circumstance, whereas the SC-based multi-objective technique provides a rigorous straightforward computational process in which changing preferences and importance of differing objectives are easily accommodated. Also, adding more objectives is straightforward and easily accomplished. The use of fuzzy-set representations of information categories provides insight into their performance throughout the range of their universe of discourse. The ability of the technique to provide a "best" medical decision given a particular physician, hospital, patient, situation, and other criteria was also demonstrated.

Determining flexor-tendon repair techniques via soft computing.

PubMed

Johnson, M; Firoozbakhsh, K; Moniem, M; Jamshidi, M

2001-01-01

An SC-based multi-objective decision-making method for determining the optimal flexor-tendon repair technique from experimental and clinical survey data, and with variable circumstances, was presented. Results were compared with those from the Taguchi method. Using the Taguchi method results in the need to perform ad-hoc decisions when the outcomes for individual objectives are contradictory to a particular preference or circumstance, whereas the SC-based multi-objective technique provides a rigorous straightforward computational process in which changing preferences and importance of differing objectives are easily accommodated. Also, adding more objectives is straightforward and easily accomplished. The use of fuzzy-set representations of information categories provides insight into their performance throughout the range of their universe of discourse. The ability of the technique to provide a "best" medical decision given a particular physician, hospital, patient, situation, and other criteria was also demonstrated.

An improved design method based on polyphase components for digital FIR filters

NASA Astrophysics Data System (ADS)

Kumar, A.; Kuldeep, B.; Singh, G. K.; Lee, Heung No

2017-11-01

This paper presents an efficient design of digital finite impulse response (FIR) filter, based on polyphase components and swarm optimisation techniques (SOTs). For this purpose, the design problem is formulated as mean square error between the actual response and ideal response in frequency domain using polyphase components of a prototype filter. To achieve more precise frequency response at some specified frequency, fractional derivative constraints (FDCs) have been applied, and optimal FDCs are computed using SOTs such as cuckoo search and modified cuckoo search algorithms. A comparative study of well-proved swarm optimisation, called particle swarm optimisation and artificial bee colony algorithm is made. The excellence of proposed method is evaluated using several important attributes of a filter. Comparative study evidences the excellence of proposed method for effective design of FIR filter.

A brief understanding of process optimisation in microwave-assisted extraction of botanical materials: options and opportunities with chemometric tools.

PubMed

Das, Anup Kumar; Mandal, Vivekananda; Mandal, Subhash C

2014-01-01

Extraction forms the very basic step in research on natural products for drug discovery. A poorly optimised and planned extraction methodology can jeopardise the entire mission. To provide a vivid picture of different chemometric tools and planning for process optimisation and method development in extraction of botanical material, with emphasis on microwave-assisted extraction (MAE) of botanical material. A review of studies involving the application of chemometric tools in combination with MAE of botanical materials was undertaken in order to discover what the significant extraction factors were. Optimising a response by fine-tuning those factors, experimental design or statistical design of experiment (DoE), which is a core area of study in chemometrics, was then used for statistical analysis and interpretations. In this review a brief explanation of the different aspects and methodologies related to MAE of botanical materials that were subjected to experimental design, along with some general chemometric tools and the steps involved in the practice of MAE, are presented. A detailed study on various factors and responses involved in the optimisation is also presented. This article will assist in obtaining a better insight into the chemometric strategies of process optimisation and method development, which will in turn improve the decision-making process in selecting influential extraction parameters. Copyright © 2013 John Wiley & Sons, Ltd.

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.

PubMed

Van Geit, Werner; Gevaert, Michael; Chindemi, Giuseppe; Rössert, Christian; Courcol, Jean-Denis; Muller, Eilif B; Schürmann, Felix; Segev, Idan; Markram, Henry

2016-01-01

At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardizes several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases.

The Social Essentials of Learning: An Experimental Investigation of Collaborative Problem Solving and Knowledge Construction in Mathematics Classrooms in Australia and China

ERIC Educational Resources Information Center

Chan, Man Ching Esther; Clarke, David; Cao, Yiming

2018-01-01

Interactive problem solving and learning are priorities in contemporary education, but these complex processes have proved difficult to research. This project addresses the question "How do we optimise social interaction for the promotion of learning in a mathematics classroom?" Employing the logic of multi-theoretic research design,…

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

NASA Astrophysics Data System (ADS)

Faizrahnemoon, Mahsa; Schlote, Arieh; Maggi, Lorenzo; Crisostomi, Emanuele; Shorten, Robert

2015-11-01

This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach.

Application of dragonfly algorithm for optimal performance analysis of process parameters in turn-mill operations- A case study

NASA Astrophysics Data System (ADS)

Vikram, K. Arun; Ratnam, Ch; Lakshmi, VVK; Kumar, A. Sunny; Ramakanth, RT

2018-02-01

Meta-heuristic multi-response optimization methods are widely in use to solve multi-objective problems to obtain Pareto optimal solutions during optimization. This work focuses on optimal multi-response evaluation of process parameters in generating responses like surface roughness (Ra), surface hardness (H) and tool vibration displacement amplitude (Vib) while performing operations like tangential and orthogonal turn-mill processes on A-axis Computer Numerical Control vertical milling center. Process parameters like tool speed, feed rate and depth of cut are considered as process parameters machined over brass material under dry condition with high speed steel end milling cutters using Taguchi design of experiments (DOE). Meta-heuristic like Dragonfly algorithm is used to optimize the multi-objectives like ‘Ra’, ‘H’ and ‘Vib’ to identify the optimal multi-response process parameters combination. Later, the results thus obtained from multi-objective dragonfly algorithm (MODA) are compared with another multi-response optimization technique Viz. Grey relational analysis (GRA).

Multi-camera digital image correlation method with distributed fields of view

NASA Astrophysics Data System (ADS)

Malowany, Krzysztof; Malesa, Marcin; Kowaluk, Tomasz; Kujawinska, Malgorzata

2017-11-01

A multi-camera digital image correlation (DIC) method and system for measurements of large engineering objects with distributed, non-overlapping areas of interest are described. The data obtained with individual 3D DIC systems are stitched by an algorithm which utilizes the positions of fiducial markers determined simultaneously by Stereo-DIC units and laser tracker. The proposed calibration method enables reliable determination of transformations between local (3D DIC) and global coordinate systems. The applicability of the method was proven during in-situ measurements of a hall made of arch-shaped (18 m span) self-supporting metal-plates. The proposed method is highly recommended for 3D measurements of shape and displacements of large and complex engineering objects made from multiple directions and it provides the suitable accuracy of data for further advanced structural integrity analysis of such objects.

Prediction of protein-protein interaction network using a multi-objective optimization approach.

PubMed

Chowdhury, Archana; Rakshit, Pratyusha; Konar, Amit

2016-06-01

Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

Group search optimiser-based optimal bidding strategies with no Karush-Kuhn-Tucker optimality conditions

NASA Astrophysics Data System (ADS)

Yadav, Naresh Kumar; Kumar, Mukesh; Gupta, S. K.

2017-03-01

General strategic bidding procedure has been formulated in the literature as a bi-level searching problem, in which the offer curve tends to minimise the market clearing function and to maximise the profit. Computationally, this is complex and hence, the researchers have adopted Karush-Kuhn-Tucker (KKT) optimality conditions to transform the model into a single-level maximisation problem. However, the profit maximisation problem with KKT optimality conditions poses great challenge to the classical optimisation algorithms. The problem has become more complex after the inclusion of transmission constraints. This paper simplifies the profit maximisation problem as a minimisation function, in which the transmission constraints, the operating limits and the ISO market clearing functions are considered with no KKT optimality conditions. The derived function is solved using group search optimiser (GSO), a robust population-based optimisation algorithm. Experimental investigation is carried out on IEEE 14 as well as IEEE 30 bus systems and the performance is compared against differential evolution-based strategic bidding, genetic algorithm-based strategic bidding and particle swarm optimisation-based strategic bidding methods. The simulation results demonstrate that the obtained profit maximisation through GSO-based bidding strategies is higher than the other three methods.

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions

PubMed Central

Qu, Shaojian; Ji, Ying

2016-01-01

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our “worst-case weighted multi-objective game” model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call “robust-weighted Nash equilibrium”. We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications. PMID:26820512

Multi-objective decision-making model based on CBM for an aircraft fleet

NASA Astrophysics Data System (ADS)

Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

Products recognition on shop-racks from local scale-invariant features

NASA Astrophysics Data System (ADS)

Zawistowski, Jacek; Kurzejamski, Grzegorz; Garbat, Piotr; Naruniec, Jacek

2016-04-01

This paper presents a system designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. System uses well known binary keypoint detection algorithms for finding characteristic points in the image. One of the main idea is object recognition based on Implicit Shape Model method. Authors of the article proposed many improvements of the algorithm. Originally fiducial points are matched with a very simple function. This leads to the limitations in the number of objects parts being success- fully separated, while various methods of classification may be validated in order to achieve higher performance. Such an extension implies research on training procedure able to deal with many objects categories. Proposed solution opens a new possibilities for many algorithms demanding fast and robust multi-object recognition.

Chromatic Illumination Discrimination Ability Reveals that Human Colour Constancy Is Optimised for Blue Daylight Illuminations

PubMed Central

Pearce, Bradley; Crichton, Stuart; Mackiewicz, Michal; Finlayson, Graham D.; Hurlbert, Anya

2014-01-01

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed. PMID:24586299

Automated Reconstruction of Three-Dimensional Fish Motion, Forces, and Torques

PubMed Central

Voesenek, Cees J.; Pieters, Remco P. M.; van Leeuwen, Johan L.

2016-01-01

Fish can move freely through the water column and make complex three-dimensional motions to explore their environment, escape or feed. Nevertheless, the majority of swimming studies is currently limited to two-dimensional analyses. Accurate experimental quantification of changes in body shape, position and orientation (swimming kinematics) in three dimensions is therefore essential to advance biomechanical research of fish swimming. Here, we present a validated method that automatically tracks a swimming fish in three dimensions from multi-camera high-speed video. We use an optimisation procedure to fit a parameterised, morphology-based fish model to each set of video images. This results in a time sequence of position, orientation and body curvature. We post-process this data to derive additional kinematic parameters (e.g. velocities, accelerations) and propose an inverse-dynamics method to compute the resultant forces and torques during swimming. The presented method for quantifying 3D fish motion paves the way for future analyses of swimming biomechanics. PMID:26752597

Global Topology Optimisation

DTIC Science & Technology

2016-10-31

statistical physics. Sec. IV includes several examples of the application of the stochastic method, including matching of a shape to a fixed design, and...an important part of any future application of this method. Second, re-initialization of the level set can lead to small but significant movements of...of engineering design problems [6, 17]. However, many of the relevant applications involve non-convex optimisation problems with multiple locally

Measuring research impact in Australia's medical research institutes: a scoping literature review of the objectives for and an assessment of the capabilities of research impact assessment frameworks.

PubMed

Deeming, Simon; Searles, Andrew; Reeves, Penny; Nilsson, Michael

2017-03-21

Realising the economic potential of research institutions, including medical research institutes, represents a policy imperative for many Organisation for Economic Co-operation and Development nations. The assessment of research impact has consequently drawn increasing attention. Research impact assessment frameworks (RIAFs) provide a structure to assess research translation, but minimal research has examined whether alternative RIAFs realise the intended policy outcomes. This paper examines the objectives presented for RIAFs in light of economic imperatives to justify ongoing support for health and medical research investment, leverage productivity via commercialisation and outcome-efficiency gains in health systems, and ensure that translation and impact considerations are embedded into the research process. This paper sought to list the stated objectives for RIAFs, to identify existing frameworks and to evaluate whether the identified frameworks possessed the capabilities necessary to address the specified objectives. A scoping review of the literature to identify objectives specified for RIAFs, inform upon descriptive criteria for each objective and identify existing RIAFs. Criteria were derived for each objective. The capability for the existing RIAFs to realise the alternative objectives was evaluated based upon these criteria. The collated objectives for RIAFs included accountability (top-down), transparency/accountability (bottom-up), advocacy, steering, value for money, management/learning and feedback/allocation, prospective orientation, and speed of translation. Of the 25 RIAFs identified, most satisfied objectives such as accountability and advocacy, which are largely sufficient for the first economic imperative to justify research investment. The frameworks primarily designed to optimise the speed of translation or enable the prospective orientation of research possessed qualities most likely to optimise the productive outcomes from research. However, the results show that few frameworks met the criteria for these objectives. It is imperative that the objective(s) for an assessment framework are explicit and that RIAFs are designed to realise these objectives. If the objectives include the capability to pro-actively drive productive research impacts, the potential for prospective orientation and a focus upon the speed of translation merits prioritisation. Frameworks designed to optimise research translation and impact, rather than simply assess impact, offer greater promise to contribute to the economic imperatives compelling their implementation.

Optimisation of a propagation-based x-ray phase-contrast micro-CT system

NASA Astrophysics Data System (ADS)

Nesterets, Yakov I.; Gureyev, Timur E.; Dimmock, Matthew R.

2018-03-01

Micro-CT scanners find applications in many areas ranging from biomedical research to material sciences. In order to provide spatial resolution on a micron scale, these scanners are usually equipped with micro-focus, low-power x-ray sources and hence require long scanning times to produce high resolution 3D images of the object with acceptable contrast-to-noise. Propagation-based phase-contrast tomography (PB-PCT) has the potential to significantly improve the contrast-to-noise ratio (CNR) or, alternatively, reduce the image acquisition time while preserving the CNR and the spatial resolution. We propose a general approach for the optimisation of the PB-PCT imaging system. When applied to an imaging system with fixed parameters of the source and detector this approach requires optimisation of only two independent geometrical parameters of the imaging system, i.e. the source-to-object distance R 1 and geometrical magnification M, in order to produce the best spatial resolution and CNR. If, in addition to R 1 and M, the system parameter space also includes the source size and the anode potential this approach allows one to find a unique configuration of the imaging system that produces the required spatial resolution and the best CNR.

Object Manifold Alignment for Multi-Temporal High Resolution Remote Sensing Images Classification

NASA Astrophysics Data System (ADS)

Gao, G.; Zhang, M.; Gu, Y.

2017-05-01

Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, "pepper and salt" appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and "pepper and salt" problem, an object-based manifold alignment method is proposed. Firstly, multi-temporal multispectral images are cut to superpixels by simple linear iterative clustering (SLIC) respectively. Secondly, some features obtained from superpixels are formed as vector. Thirdly, a majority voting manifold alignment method aiming at solving high resolution problem is proposed and mapping the vector data to alignment space. At last, all the data in the alignment space are classified by using KNN method. Multi-temporal images from different areas or the same area are both considered in this paper. In the experiments, 2 groups of multi-temporal HR images collected by China GF1 and GF2 satellites are used for performance evaluation. Experimental results indicate that the proposed method not only has significantly outperforms than traditional domain adaptation methods in classification accuracy, but also effectively overcome the problem of "pepper and salt".

2D and 3D X-ray phase retrieval of multi-material objects using a single defocus distance.

PubMed

Beltran, M A; Paganin, D M; Uesugi, K; Kitchen, M J

2010-03-29

A method of tomographic phase retrieval is developed for multi-material objects whose components each has a distinct complex refractive index. The phase-retrieval algorithm, based on the Transport-of-Intensity equation, utilizes propagation-based X-ray phase contrast images acquired at a single defocus distance for each tomographic projection. The method requires a priori knowledge of the complex refractive index for each material present in the sample, together with the total projected thickness of the object at each orientation. The requirement of only a single defocus distance per projection simplifies the experimental setup and imposes no additional dose compared to conventional tomography. The algorithm was implemented using phase contrast data acquired at the SPring-8 Synchrotron facility in Japan. The three-dimensional (3D) complex refractive index distribution of a multi-material test object was quantitatively reconstructed using a single X-ray phase-contrast image per projection. The technique is robust in the presence of noise, compared to conventional absorption based tomography.

A robust optimisation approach to the problem of supplier selection and allocation in outsourcing

NASA Astrophysics Data System (ADS)

Fu, Yelin; Keung Lai, Kin; Liang, Liang

2016-03-01

We formulate the supplier selection and allocation problem in outsourcing under an uncertain environment as a stochastic programming problem. Both the decision-maker's attitude towards risk and the penalty parameters for demand deviation are considered in the objective function. A service level agreement, upper bound for each selected supplier's allocation and the number of selected suppliers are considered as constraints. A novel robust optimisation approach is employed to solve this problem under different economic situations. Illustrative examples are presented with managerial implications highlighted to support decision-making.

Model-based registration of multi-rigid-body for augmented reality

NASA Astrophysics Data System (ADS)

Ikeda, Sei; Hori, Hajime; Imura, Masataka; Manabe, Yoshitsugu; Chihara, Kunihiro

2009-02-01

Geometric registration between a virtual object and the real space is the most basic problem in augmented reality. Model-based tracking methods allow us to estimate three-dimensional (3-D) position and orientation of a real object by using a textured 3-D model instead of visual marker. However, it is difficult to apply existing model-based tracking methods to the objects that have movable parts such as a display of a mobile phone, because these methods suppose a single, rigid-body model. In this research, we propose a novel model-based registration method for multi rigid-body objects. For each frame, the 3-D models of each rigid part of the object are first rendered according to estimated motion and transformation from the previous frame. Second, control points are determined by detecting the edges of the rendered image and sampling pixels on these edges. Motion and transformation are then simultaneously calculated from distances between the edges and the control points. The validity of the proposed method is demonstrated through experiments using synthetic videos.

Multi-objective game-theory models for conflict analysis in reservoir watershed management.

PubMed

Lee, Chih-Sheng

2012-05-01

This study focuses on the development of a multi-objective game-theory model (MOGM) for balancing economic and environmental concerns in reservoir watershed management and for assistance in decision. Game theory is used as an alternative tool for analyzing strategic interaction between economic development (land use and development) and environmental protection (water-quality protection and eutrophication control). Geographic information system is used to concisely illustrate and calculate the areas of various land use types. The MOGM methodology is illustrated in a case study of multi-objective watershed management in the Tseng-Wen reservoir, Taiwan. The innovation and advantages of MOGM can be seen in the results, which balance economic and environmental concerns in watershed management and which can be interpreted easily by decision makers. For comparison, the decision-making process using conventional multi-objective method to produce many alternatives was found to be more difficult. Copyright © 2012 Elsevier Ltd. All rights reserved.

Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

Combining environment-driven adaptation and task-driven optimisation in evolutionary robotics.

PubMed

Haasdijk, Evert; Bredeche, Nicolas; Eiben, A E

2014-01-01

Embodied evolutionary robotics is a sub-field of evolutionary robotics that employs evolutionary algorithms on the robotic hardware itself, during the operational period, i.e., in an on-line fashion. This enables robotic systems that continuously adapt, and are therefore capable of (re-)adjusting themselves to previously unknown or dynamically changing conditions autonomously, without human oversight. This paper addresses one of the major challenges that such systems face, viz. that the robots must satisfy two sets of requirements. Firstly, they must continue to operate reliably in their environment (viability), and secondly they must competently perform user-specified tasks (usefulness). The solution we propose exploits the fact that evolutionary methods have two basic selection mechanisms-survivor selection and parent selection. This allows evolution to tackle the two sets of requirements separately: survivor selection is driven by the environment and parent selection is based on task-performance. This idea is elaborated in the Multi-Objective aNd open-Ended Evolution (monee) framework, which we experimentally validate. Experiments with robotic swarms of 100 simulated e-pucks show that monee does indeed promote task-driven behaviour without compromising environmental adaptation. We also investigate an extension of the parent selection process with a 'market mechanism' that can ensure equitable distribution of effort over multiple tasks, a particularly pressing issue if the environment promotes specialisation in single tasks.

Research on Multi - Person Parallel Modeling Method Based on Integrated Model Persistent Storage

NASA Astrophysics Data System (ADS)

Qu, MingCheng; Wu, XiangHu; Tao, YongChao; Liu, Ying

2018-03-01

This paper mainly studies the multi-person parallel modeling method based on the integrated model persistence storage. The integrated model refers to a set of MDDT modeling graphics system, which can carry out multi-angle, multi-level and multi-stage description of aerospace general embedded software. Persistent storage refers to converting the data model in memory into a storage model and converting the storage model into a data model in memory, where the data model refers to the object model and the storage model is a binary stream. And multi-person parallel modeling refers to the need for multi-person collaboration, the role of separation, and even real-time remote synchronization modeling.

Manufacturing process for the WEAVE prime focus corrector optics for the 4.2m William Hershel Telescope

NASA Astrophysics Data System (ADS)

Lhomé, Emilie; Agócs, Tibor; Abrams, Don Carlos; Dee, Kevin M.; Middleton, Kevin F.; Tosh, Ian A.; Jaskó, Attila; Connor, Peter; Cochrane, Dave; Gers, Luke; Jonas, Graeme; Rakich, Andrew; Benn, Chris R.; Balcells, Marc; Trager, Scott C.; Dalton, Gavin B.; Carrasco, Esperanza; Vallenari, Antonella; Bonifacio, Piercarlo; Aguerri, J. Alfonso L.

2016-07-01

In this paper, we detail the manufacturing process for the lenses that will constitute the new two-degree field-of-view Prime Focus Corrector (PFC) for the 4.2m William Herschel Telescope (WHT) optimised for the upcoming WEAVE Multi-Object Spectroscopy (MOS) facility. The corrector, including an Atmospheric Dispersion Corrector (ADC), is made of six large lenses, the largest being 1.1-meter diameter. We describe how the prescriptions of the optical design were translated into manufacturing specifications for the blanks and lenses. We explain how the as-built glass blank parameters were fed back into the optical design and how the specifications for the lenses were subsequently modified. We review the critical issues for the challenging manufacturing process and discuss the trade-offs that were necessary to deliver the lenses while maintaining the optimal optical performance. A short description of the lens optical testing is also presented. Finally, the subsequent manufacturing steps, including assembly, integration, and alignment are outlined.

Solving multi-objective water management problems using evolutionary computation.

PubMed

Lewis, A; Randall, M

2017-12-15

Water as a resource is becoming increasingly more valuable given the changes in global climate. In an agricultural sense, the role of water is vital to ensuring food security. Therefore the management of it has become a subject of increasing attention and the development of effective tools to support participative decision-making in water management will be a valuable contribution. In this paper, evolutionary computation techniques and Pareto optimisation are incorporated in a model-based system for water management. An illustrative test case modelling optimal crop selection across dry, average and wet years based on data from the Murrumbidgee Irrigation Area in Australia is presented. It is shown that sets of trade-off solutions that provide large net revenues, or minimise environmental flow deficits can be produced rapidly, easily and automatically. The system is capable of providing detailed information on optimal solutions to achieve desired outcomes, responding to a variety of factors including climate conditions and economics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Second Announcement - ESO/ST-ECF Workshop on NICMOS and the VLT: A New Era of High-Resolution Near-Infrared Imaging and Spectroscopy - May 26-27, 1998 - Hotel Baia di Nora, Pula, Sardinia, Italy

NASA Astrophysics Data System (ADS)

1998-03-01

ST-ECF and ESO are organising in collaboration with the NICMOS IDT and STScI a workshop on near infrared imaging from space and ground. The purpose of the workshop is to review what has been achieved with the Near Infrared and Multi Object Spectrograph (NICMOS) on board of HST, what can be achieved in the remaining lifetime of the instrument, and how NICMOS observations can be optimised taking into account the availability of IR imaging and spectroscopy on ESO's Very large Telescope (VLT) in the near future. The meeting will be held in May 1998, about one year after science observations started with NICMOS, and about half a year before the Infrared Spectrometer and Array Camera (ISAAC) starts to operate on the VLT. Currently, it is expected that NICMOS will operate until the end of 1998.

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

NASA Astrophysics Data System (ADS)

Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

2015-10-01

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

Real-Time Visual Tracking through Fusion Features

PubMed Central

Ruan, Yang; Wei, Zhenzhong

2016-01-01

Due to their high-speed, correlation filters for object tracking have begun to receive increasing attention. Traditional object trackers based on correlation filters typically use a single type of feature. In this paper, we attempt to integrate multiple feature types to improve the performance, and we propose a new DD-HOG fusion feature that consists of discriminative descriptors (DDs) and histograms of oriented gradients (HOG). However, fusion features as multi-vector descriptors cannot be directly used in prior correlation filters. To overcome this difficulty, we propose a multi-vector correlation filter (MVCF) that can directly convolve with a multi-vector descriptor to obtain a single-channel response that indicates the location of an object. Experiments on the CVPR2013 tracking benchmark with the evaluation of state-of-the-art trackers show the effectiveness and speed of the proposed method. Moreover, we show that our MVCF tracker, which uses the DD-HOG descriptor, outperforms the structure-preserving object tracker (SPOT) in multi-object tracking because of its high-speed and ability to address heavy occlusion. PMID:27347951

Development of an optimised 1:1 physiotherapy intervention post first-time lumbar discectomy: a mixed-methods study

PubMed Central

Rushton, A; White, L; Heap, A; Heneghan, N; Goodwin, P

2016-01-01

Objectives To develop an optimised 1:1 physiotherapy intervention that reflects best practice, with flexibility to tailor management to individual patients, thereby ensuring patient-centred practice. Design Mixed-methods combining evidence synthesis, expert review and focus groups. Setting Secondary care involving 5 UK specialist spinal centres. Participants A purposive panel of clinical experts from the 5 spinal centres, comprising spinal surgeons, inpatient and outpatient physiotherapists, provided expert review of the draft intervention. Purposive samples of patients (n=10) and physiotherapists (n=10) (inpatient/outpatient physiotherapists managing patients with lumbar discectomy) were invited to participate in the focus groups at 1 spinal centre. Methods A draft intervention developed from 2 systematic reviews; a survey of current practice and research related to stratified care was circulated to the panel of clinical experts. Lead physiotherapists collaborated with physiotherapy and surgeon colleagues to provide feedback that informed the intervention presented at 2 focus groups investigating acceptability to patients and physiotherapists. The focus groups were facilitated by an experienced facilitator, recorded in written and tape-recorded forms by an observer. Tape recordings were transcribed verbatim. Data analysis, conducted by 2 independent researchers, employed an iterative and constant comparative process of (1) initial descriptive coding to identify categories and subsequent themes, and (2) deeper, interpretive coding and thematic analysis enabling concepts to emerge and overarching pattern codes to be identified. Results The intervention reflected best available evidence and provided flexibility to ensure patient-centred care. The intervention comprised up to 8 sessions of 1:1 physiotherapy over 8 weeks, starting 4 weeks postsurgery. The intervention was acceptable to patients and physiotherapists. Conclusions A rigorous process informed an optimised 1:1 physiotherapy intervention post-lumbar discectomy that reflects best practice. The developed intervention was agreed on by the 5 spinal centres for implementation in a randomised controlled trial to evaluate its effectiveness. PMID:26916690

Application of Multi-Hypothesis Sequential Monte Carlo for Breakup Analysis

NASA Astrophysics Data System (ADS)

Faber, W. R.; Zaidi, W.; Hussein, I. I.; Roscoe, C. W. T.; Wilkins, M. P.; Schumacher, P. W., Jr.

As more objects are launched into space, the potential for breakup events and space object collisions is ever increasing. These events create large clouds of debris that are extremely hazardous to space operations. Providing timely, accurate, and statistically meaningful Space Situational Awareness (SSA) data is crucial in order to protect assets and operations in space. The space object tracking problem, in general, is nonlinear in both state dynamics and observations, making it ill-suited to linear filtering techniques such as the Kalman filter. Additionally, given the multi-object, multi-scenario nature of the problem, space situational awareness requires multi-hypothesis tracking and management that is combinatorially challenging in nature. In practice, it is often seen that assumptions of underlying linearity and/or Gaussianity are used to provide tractable solutions to the multiple space object tracking problem. However, these assumptions are, at times, detrimental to tracking data and provide statistically inconsistent solutions. This paper details a tractable solution to the multiple space object tracking problem applicable to space object breakup events. Within this solution, simplifying assumptions of the underlying probability density function are relaxed and heuristic methods for hypothesis management are avoided. This is done by implementing Sequential Monte Carlo (SMC) methods for both nonlinear filtering as well as hypothesis management. This goal of this paper is to detail the solution and use it as a platform to discuss computational limitations that hinder proper analysis of large breakup events.

Dietary changes needed to reach nutritional adequacy without increasing diet cost according to income: An analysis among French adults

PubMed Central

Maillot, Matthieu; Vieux, Florent; Delaere, Fabien; Lluch, Anne; Darmon, Nicole

2017-01-01

Objective To explore the dietary changes needed to achieve nutritional adequacy across income levels at constant energy and diet cost. Materials and methods Individual diet modelling was used to design iso-caloric, nutritionally adequate optimised diets for each observed diet in a sample of adult normo-reporters aged ≥20 years (n = 1,719) from the Individual and National Dietary Survey (INCA2), 2006–2007. Diet cost was estimated from mean national food prices (2006–2007). A first set of free-cost models explored the impact of optimisation on the variation of diet cost. A second set of iso-cost models explored the dietary changes induced by the optimisation with cost set equal to the observed one. Analyses of dietary changes were conducted by income quintiles, adjusting for energy intake, sociodemographic and socioeconomic variables, and smoking status. Results The cost of observed diets increased with increasing income quintiles. In free-cost models, the optimisation increased diet cost on average (+0.22 ± 1.03 euros/d) and within each income quintile, with no significant difference between quintiles, but with systematic increases for observed costs lower than 3.85 euros/d. In iso-cost models, it was possible to design nutritionally adequate diets whatever the initial observed cost. On average, the optimisation at iso-cost increased fruits and vegetables (+171 g/day), starchy foods (+121 g/d), water and beverages (+91 g/d), and dairy products (+20 g/d), and decreased the other food groups (e.g. mixed dishes and salted snacks), leading to increased total diet weight (+300 g/d). Those changes were mostly similar across income quintiles, but lower-income individuals needed to introduce significantly more fruit and vegetables than higher-income ones. Conclusions In France, the dietary changes needed to reach nutritional adequacy without increasing cost are similar regardless of income, but may be more difficult to implement when the budget for food is lower than 3.85 euros/d. PMID:28358837

Optimized scheme in coal-fired boiler combustion based on information entropy and modified K-prototypes algorithm

NASA Astrophysics Data System (ADS)

Gu, Hui; Zhu, Hongxia; Cui, Yanfeng; Si, Fengqi; Xue, Rui; Xi, Han; Zhang, Jiayu

2018-06-01

An integrated combustion optimization scheme is proposed for the combined considering the restriction in coal-fired boiler combustion efficiency and outlet NOx emissions. Continuous attribute discretization and reduction techniques are handled as optimization preparation by E-Cluster and C_RED methods, in which the segmentation numbers don't need to be provided in advance and can be continuously adapted with data characters. In order to obtain results of multi-objections with clustering method for mixed data, a modified K-prototypes algorithm is then proposed. This algorithm can be divided into two stages as K-prototypes algorithm for clustering number self-adaptation and clustering for multi-objective optimization, respectively. Field tests were carried out at a 660 MW coal-fired boiler to provide real data as a case study for controllable attribute discretization and reduction in boiler system and obtaining optimization parameters considering [ maxηb, minyNOx ] multi-objective rule.

Optimisation of reconstruction--reprojection-based motion correction for cardiac SPECT.

PubMed

Kangasmaa, Tuija S; Sohlberg, Antti O

2014-07-01

Cardiac motion is a challenging cause of image artefacts in myocardial perfusion SPECT. A wide range of motion correction methods have been developed over the years, and so far automatic algorithms based on the reconstruction--reprojection principle have proved to be the most effective. However, these methods have not been fully optimised in terms of their free parameters and implementational details. Two slightly different implementations of reconstruction--reprojection-based motion correction techniques were optimised for effective, good-quality motion correction and then compared with each other. The first of these methods (Method 1) was the traditional reconstruction-reprojection motion correction algorithm, where the motion correction is done in projection space, whereas the second algorithm (Method 2) performed motion correction in reconstruction space. The parameters that were optimised include the type of cost function (squared difference, normalised cross-correlation and mutual information) that was used to compare measured and reprojected projections, and the number of iterations needed. The methods were tested with motion-corrupt projection datasets, which were generated by adding three different types of motion (lateral shift, vertical shift and vertical creep) to motion-free cardiac perfusion SPECT studies. Method 2 performed slightly better overall than Method 1, but the difference between the two implementations was small. The execution time for Method 2 was much longer than for Method 1, which limits its clinical usefulness. The mutual information cost function gave clearly the best results for all three motion sets for both correction methods. Three iterations were sufficient for a good quality correction using Method 1. The traditional reconstruction--reprojection-based method with three update iterations and mutual information cost function is a good option for motion correction in clinical myocardial perfusion SPECT.

Multi-Mounted X-Ray Computed Tomography

PubMed Central

Fu, Jian; Liu, Zhenzhong; Wang, Jingzheng

2016-01-01

Most existing X-ray computed tomography (CT) techniques work in single-mounted mode and need to scan the inspected objects one by one. It is time-consuming and not acceptable for the inspection in a large scale. In this paper, we report a multi-mounted CT method and its first engineering implementation. It consists of a multi-mounted scanning geometry and the corresponding algebraic iterative reconstruction algorithm. This approach permits the CT rotation scanning of multiple objects simultaneously without the increase of penetration thickness and the signal crosstalk. Compared with the conventional single-mounted methods, it has the potential to improve the imaging efficiency and suppress the artifacts from the beam hardening and the scatter. This work comprises a numerical study of the method and its experimental verification using a dataset measured with a developed multi-mounted X-ray CT prototype system. We believe that this technique is of particular interest for pushing the engineering applications of X-ray CT. PMID:27073911

Medical Image Segmentation by Combining Graph Cut and Oriented Active Appearance Models

PubMed Central

Chen, Xinjian; Udupa, Jayaram K.; Bağcı, Ulaş; Zhuge, Ying; Yao, Jianhua

2017-01-01

In this paper, we propose a novel 3D segmentation method based on the effective combination of the active appearance model (AAM), live wire (LW), and graph cut (GC). The proposed method consists of three main parts: model building, initialization, and segmentation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the initialization part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW method, resulting in Oriented AAM (OAAM). A multi-object strategy is utilized to help in object initialization. We employ a pseudo-3D initialization strategy, and segment the organs slice by slice via multi-object OAAM method. For the segmentation part, a 3D shape constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT dataset and also tested on the MICCAI 2007 grand challenge for liver segmentation training dataset. The results show the following: (a) An overall segmentation accuracy of true positive volume fraction (TPVF) > 94.3%, false positive volume fraction (FPVF) < 0.2% can be achieved. (b) The initialization performance can be improved by combining AAM and LW. (c) The multi-object strategy greatly facilitates the initialization. (d) Compared to the traditional 3D AAM method, the pseudo 3D OAAM method achieves comparable performance while running 12 times faster. (e) The performance of proposed method is comparable to the state of the art liver segmentation algorithm. The executable version of 3D shape constrained GC with user interface can be downloaded from website http://xinjianchen.wordpress.com/research/. PMID:22311862

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience

PubMed Central

Van Geit, Werner; Gevaert, Michael; Chindemi, Giuseppe; Rössert, Christian; Courcol, Jean-Denis; Muller, Eilif B.; Schürmann, Felix; Segev, Idan; Markram, Henry

2016-01-01

At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardizes several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases. PMID:27375471

Multi-response optimization of T300/epoxy prepreg tape-wound cylinder by grey relational analysis coupled with the response surface method

NASA Astrophysics Data System (ADS)

Kang, Chao; Shi, Yaoyao; He, Xiaodong; Yu, Tao; Deng, Bo; Zhang, Hongji; Sun, Pengcheng; Zhang, Wenbin

2017-09-01

This study investigates the multi-objective optimization of quality characteristics for a T300/epoxy prepreg tape-wound cylinder. The method integrates the Taguchi method, grey relational analysis (GRA) and response surface methodology, and is adopted to improve tensile strength and reduce residual stress. In the winding process, the main process parameters involving winding tension, pressure, temperature and speed are selected to evaluate the parametric influences on tensile strength and residual stress. Experiments are conducted using the Box-Behnken design. Based on principal component analysis, the grey relational grades are properly established to convert multi-responses into an individual objective problem. Then the response surface method is used to build a second-order model of grey relational grade and predict the optimum parameters. The predictive accuracy of the developed model is proved by two test experiments with a low prediction error of less than 7%. The following process parameters, namely winding tension 124.29 N, pressure 2000 N, temperature 40 °C and speed 10.65 rpm, have the highest grey relational grade and give better quality characteristics in terms of tensile strength and residual stress. The confirmation experiment shows that better results are obtained with GRA improved by the proposed method than with ordinary GRA. The proposed method is proved to be feasible and can be applied to optimize the multi-objective problem in the filament winding process.

Systems analysis - a new paradigm and decision support tools for the water framework directive

NASA Astrophysics Data System (ADS)

Bruen, M.

2007-06-01

In the early days of Systems Analysis the focus was on providing tools for optimisation, modelling and simulation for use by experts. Now there is a recognition of the need to develop and disseminate tools to assist in making decisions, negotiating compromises and communicating preferences that can easily be used by stakeholders without the need for specialist training. The Water Framework Directive (WFD) requires public participation and thus provides a strong incentive for progress in this direction. This paper places the new paradigm in the context of the classical one and discusses some of the new approaches which can be used in the implementation of the WFD. These include multi-criteria decision support methods suitable for environmental problems, adaptive management, cognitive mapping, social learning and cooperative design and group decision-making. Concordance methods (such as ELECTRE) and the Analytical Hierarchy Process (AHP) are identified as multi-criteria methods that can be readily integrated into Decision Support Systems (DSS) that deal with complex environmental issues with very many criteria, some of which are qualitative. The expanding use of the new paradigm provides an opportunity to observe and learn from the interaction of stakeholders with the new technology and to assess its effectiveness. This is best done by trained sociologists fully integrated into the processes. The WINCOMS research project is an example applied to the implementation of the WFD in Ireland.

A multi-analyte serum test for the detection of non-small cell lung cancer

PubMed Central

Farlow, E C; Vercillo, M S; Coon, J S; Basu, S; Kim, A W; Faber, L P; Warren, W H; Bonomi, P; Liptay, M J; Borgia, J A

2010-01-01

Background: In this study, we appraised a wide assortment of biomarkers previously shown to have diagnostic or prognostic value for non-small cell lung cancer (NSCLC) with the intent of establishing a multi-analyte serum test capable of identifying patients with lung cancer. Methods: Circulating levels of 47 biomarkers were evaluated against patient cohorts consisting of 90 NSCLC and 43 non-cancer controls using commercial immunoassays. Multivariate statistical methods were used on all biomarkers achieving statistical relevance to define an optimised panel of diagnostic biomarkers for NSCLC. The resulting biomarkers were fashioned into a classification algorithm and validated against serum from a second patient cohort. Results: A total of 14 analytes achieved statistical relevance upon evaluation. Multivariate statistical methods then identified a panel of six biomarkers (tumour necrosis factor-α, CYFRA 21-1, interleukin-1ra, matrix metalloproteinase-2, monocyte chemotactic protein-1 and sE-selectin) as being the most efficacious for diagnosing early stage NSCLC. When tested against a second patient cohort, the panel successfully classified 75 of 88 patients. Conclusions: Here, we report the development of a serum algorithm with high specificity for classifying patients with NSCLC against cohorts of various ‘high-risk' individuals. A high rate of false positives was observed within the cohort in which patients had non-neoplastic lung nodules, possibly as a consequence of the inflammatory nature of these conditions. PMID:20859284

A Multi-Method Approach to Studying the Relationship between Character Strengths and Vocational Interests in Adolescents

ERIC Educational Resources Information Center

Proyer, Rene T.; Sidler, Nicole; Weber, Marco; Ruch, Willibald

2012-01-01

The relationship between character strengths and vocational interests was tested. In an online study, 197 thirteen to eighteen year-olds completed a questionnaire measuring character strengths and a multi-method measure for interests (questionnaire, nonverbal test, and objective personality tests). The main findings were that intellectual…

An overlapped grid method for multigrid, finite volume/difference flow solvers: MaGGiE

NASA Technical Reports Server (NTRS)

Baysal, Oktay; Lessard, Victor R.

1990-01-01

The objective is to develop a domain decomposition method via overlapping/embedding the component grids, which is to be used by upwind, multi-grid, finite volume solution algorithms. A computer code, given the name MaGGiE (Multi-Geometry Grid Embedder) is developed to meet this objective. MaGGiE takes independently generated component grids as input, and automatically constructs the composite mesh and interpolation data, which can be used by the finite volume solution methods with or without multigrid convergence acceleration. Six demonstrative examples showing various aspects of the overlap technique are presented and discussed. These cases are used for developing the procedure for overlapping grids of different topologies, and to evaluate the grid connection and interpolation data for finite volume calculations on a composite mesh. Time fluxes are transferred between mesh interfaces using a trilinear interpolation procedure. Conservation losses are minimal at the interfaces using this method. The multi-grid solution algorithm, using the coaser grid connections, improves the convergence time history as compared to the solution on composite mesh without multi-gridding.

Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop.

PubMed

Li, Lian-Hui; Mo, Rong

2015-01-01

The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility.

Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop

PubMed Central

Li, Lian-hui; Mo, Rong

2015-01-01

The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility. PMID:26414758

Optimisation of an idealised primitive equation ocean model using stochastic parameterization

NASA Astrophysics Data System (ADS)

Cooper, Fenwick C.

2017-05-01

Using a simple parameterization, an idealised low resolution (biharmonic viscosity coefficient of 5 × 1012 m4s-1 , 128 × 128 grid) primitive equation baroclinic ocean gyre model is optimised to have a much more accurate climatological mean, variance and response to forcing, in all model variables, with respect to a high resolution (biharmonic viscosity coefficient of 8 × 1010 m4s-1 , 512 × 512 grid) equivalent. For example, the change in the climatological mean due to a small change in the boundary conditions is more accurate in the model with parameterization. Both the low resolution and high resolution models are strongly chaotic. We also find that long timescales in the model temperature auto-correlation at depth are controlled by the vertical temperature diffusion parameter and time mean vertical advection and are caused by short timescale random forcing near the surface. This paper extends earlier work that considered a shallow water barotropic gyre. Here the analysis is extended to a more turbulent multi-layer primitive equation model that includes temperature as a prognostic variable. The parameterization consists of a constant forcing, applied to the velocity and temperature equations at each grid point, which is optimised to obtain a model with an accurate climatological mean, and a linear stochastic forcing, that is optimised to also obtain an accurate climatological variance and 5 day lag auto-covariance. A linear relaxation (nudging) is not used. Conservation of energy and momentum is discussed in an appendix.

Phospholipid fingerprints of milk from different mammalians determined by 31P NMR: towards specific interest in human health.

PubMed

Garcia, Cyrielle; Lutz, Norbert W; Confort-Gouny, Sylviane; Cozzone, Patrick J; Armand, Martine; Bernard, Monique

2012-12-01

Our objective was to identify and quantify phospholipids in milk from different species (human HM, cow CoM, camel CaM, and mare MM) using an optimised (31)P NMR spectroscopy procedure. The phospholipid fingerprints were species-specific with a broader variety of classes found in HM and MM; HM and CaM were richer in sphingomyelin (78.3 and 117.5μg/ml) and plasmalogens (27.3 and 24μg/ml), possibly important for infant development. Total phospholipid content was higher in CaM (0.503mM) and lower in MM (0.101mM) compared to HM (0.324mM) or CoM (0.265mM). Our optimised method showed good sensitivity, high resolution, and easy sample preparation with minimal loss of target molecules. It is suitable for determining the accurate composition of a large number of bioactive phospholipids with putative health benefits, including plasmalogens, and should aid in selecting appropriate ingredient sources for infant milk substitutes or fortifiers, and for functional foods dedicated to adults. Copyright © 2012 Elsevier Ltd. All rights reserved.

A 0.4-2.3 GHz broadband power amplifier extended continuous class-F design technology

NASA Astrophysics Data System (ADS)

Chen, Peng; He, Songbai

2015-08-01

A 0.4-2.3 GHz broadband power amplifier (PA) extended continuous class-F design technology is proposed in this paper. Traditional continuous class-F PA performs in high-efficiency only in one octave bandwidth. With the increasing development of wireless communication, the PA is in demand to cover the mainstream communication standards' working frequencies from 0.4 GHz to 2.2 GHz. In order to achieve this objective, the bandwidths of class-F and continuous class-F PA are analysed and discussed by Fourier series. Also, two criteria, which could reduce the continuous class-F PA's implementation complexity, are presented and explained to investigate the overlapping area of the transistor's current and voltage waveforms. The proposed PA design technology is based on the continuous class-F design method and divides the bandwidth into two parts: the first part covers the bandwidth from 1.3 GHz to 2.3 GHz, where the impedances are designed by the continuous class-F method; the other part covers the bandwidth from 0.4 GHz to 1.3 GHz, where the impedance to guarantee PA to be in high-efficiency over this bandwidth is selected and controlled. The improved particle swarm optimisation is employed for realising the multi-impedances of output and input network. A PA based on a commercial 10 W GaN high electron mobility transistor is designed and fabricated to verify the proposed design method. The simulation and measurement results show that the proposed PA could deliver 40-76% power added efficiency and more than 11 dB power gain with more than 40 dBm output power over the bandwidth from 0.4-2.3 GHz.

Robust Sensitivity Analysis for Multi-Attribute Deterministic Hierarchical Value Models

DTIC Science & Technology

2002-03-01

such as weighted sum method, weighted 5 product method, and the Analytic Hierarchy Process ( AHP ). This research focuses on only weighted sum...different groups. They can be termed as deterministic, stochastic, or fuzzy multi-objective decision methods if they are classified according to the...weighted product model (WPM), and analytic hierarchy process ( AHP ). His method attempts to identify the most important criteria weight and the most

Multi-tissue partial volume quantification in multi-contrast MRI using an optimised spectral unmixing approach.

PubMed

Collewet, Guylaine; Moussaoui, Saïd; Deligny, Cécile; Lucas, Tiphaine; Idier, Jérôme

2018-06-01

Multi-tissue partial volume estimation in MRI images is investigated with a viewpoint related to spectral unmixing as used in hyperspectral imaging. The main contribution of this paper is twofold. It firstly proposes a theoretical analysis of the statistical optimality conditions of the proportion estimation problem, which in the context of multi-contrast MRI data acquisition allows to appropriately set the imaging sequence parameters. Secondly, an efficient proportion quantification algorithm based on the minimisation of a penalised least-square criterion incorporating a regularity constraint on the spatial distribution of the proportions is proposed. Furthermore, the resulting developments are discussed using empirical simulations. The practical usefulness of the spectral unmixing approach for partial volume quantification in MRI is illustrated through an application to food analysis on the proving of a Danish pastry. Copyright © 2018 Elsevier Inc. All rights reserved.

An adaptive block-based fusion method with LUE-SSIM for multi-focus images

NASA Astrophysics Data System (ADS)

Zheng, Jianing; Guo, Yongcai; Huang, Yukun

2016-09-01

Because of the lenses' limited depth of field, digital cameras are incapable of acquiring an all-in-focus image of objects at varying distances in a scene. Multi-focus image fusion technique can effectively solve this problem. Aiming at the block-based multi-focus image fusion methods, the problem that blocking-artifacts often occurs. An Adaptive block-based fusion method based on lifting undistorted-edge structural similarity (LUE-SSIM) is put forward. In this method, image quality metrics LUE-SSIM is firstly proposed, which utilizes the characteristics of human visual system (HVS) and structural similarity (SSIM) to make the metrics consistent with the human visual perception. Particle swarm optimization(PSO) algorithm which selects LUE-SSIM as the object function is used for optimizing the block size to construct the fused image. Experimental results on LIVE image database shows that LUE-SSIM outperform SSIM on Gaussian defocus blur images quality assessment. Besides, multi-focus image fusion experiment is carried out to verify our proposed image fusion method in terms of visual and quantitative evaluation. The results show that the proposed method performs better than some other block-based methods, especially in reducing the blocking-artifact of the fused image. And our method can effectively preserve the undistorted-edge details in focus region of the source images.

On the characteristics of optimal transfers

NASA Astrophysics Data System (ADS)

Iorfida, Elisabetta

In the past 50 years the scientists have been developing and analysing methods and new algorithms that optimise an interplanetary trajectory according to one or more objectives. Within this field, in 1963 Lawden derived, from Pontryagin's minimum principle, the so-called `primer vector theory'. The main goal of this thesis is to develop a theoretical understanding of Lawden's theory, getting an insight into the optimality of a trajectory when mid-course corrections need to be applied. The novelty of the research is represented by a different approach to the primer vector theory, which simplifies the structure of the problem.

Multi-objective Analysis for a Sequencing Planning of Mixed-model Assembly Line

NASA Astrophysics Data System (ADS)

Shimizu, Yoshiaki; Waki, Toshiya; Yoo, Jae Kyu

Diversified customer demands are raising importance of just-in-time and agile manufacturing much more than before. Accordingly, introduction of mixed-model assembly lines becomes popular to realize the small-lot-multi-kinds production. Since it produces various kinds on the same assembly line, a rational management is of special importance. With this point of view, this study focuses on a sequencing problem of mixed-model assembly line including a paint line as its preceding process. By taking into account the paint line together, reducing work-in-process (WIP) inventory between these heterogeneous lines becomes a major concern of the sequencing problem besides improving production efficiency. Finally, we have formulated the sequencing problem as a bi-objective optimization problem to prevent various line stoppages, and to reduce the volume of WIP inventory simultaneously. Then we have proposed a practical method for the multi-objective analysis. For this purpose, we applied the weighting method to derive the Pareto front. Actually, the resulting problem is solved by a meta-heuristic method like SA (Simulated Annealing). Through numerical experiments, we verified the validity of the proposed approach, and discussed the significance of trade-off analysis between the conflicting objectives.

Development and evaluation of a novel biodegradable sustained release microsphere formulation of paclitaxel intended to treat breast cancer

PubMed Central

Shiny, Jacob; Ramchander, Thadkapally; Goverdhan, Puchchakayala; Habibuddin, Mohammad; Aukunuru, Jithan Venkata

2013-01-01

Objective: The objective of this study was to develop a novel 1 month depot paclitaxel (PTX) microspheres that give a sustained and complete drug release. Materials and Methods: PTX loaded microspheres were prepared by o/w emulsion solvent evaporation technique using the blends of poly(lactic-co-glycolic acid) (PLGA) 75/25, polycaprolactone 14,000 and polycaprolactone 80,000. Fourier transform infrared spectroscopy was used to investigate drug excipient compatibility. Compatible blends were used to prepare F1-F6 microspheres, the process was characterised and the optimum formulation was selected based on the release. Optimised formulation was characterised for solid state of the drug using the differential scanning calorimetry (DSC) studies, surface morphology using the scanning electron microscopy (SEM), in vivo drug release, in vitro in vivo correlation (IVIVC) and anticancer activity. Anticancer activity of release medium was determined using the cell viability assay in Michigan Cancer Foundation (MCF-7) cell line. Results: Blend of PLGA with polycaprolactone (Mwt 14,000) at a ratio of 1:1 (F5) resulted in complete release of the drug in a time frame of 30 days. F5 was considered as the optimised formulation. Incomplete release of the drug resulted from other formulations. The surface of the optimised formulation was smooth and the drug changed its solid state upon fabrication. The formulation also resulted in 1-month drug release in vivo. The released drug from F5 demonstrated anticancer activity for 1-month. Cell viability was reduced drastically with the release medium from F5 formulation. A 100% IVIVC was obtained with F5 formulation suggesting the authenticity of in vitro release, in vivo release and the use of the formulation in breast cancer. Conclusions: From our study, it was concluded that with careful selection of different polymers and their combinations, PTX 1 month depot formulation with 100% drug release and that can be used in breast cancer was developed. PMID:24167783

Optimising parameters for the differentiation of SH-SY5Y cells to study cell adhesion and cell migration.

PubMed

Dwane, Susan; Durack, Edel; Kiely, Patrick A

2013-09-11

Cell migration is a fundamental biological process and has an important role in the developing brain by regulating a highly specific pattern of connections between nerve cells. Cell migration is required for axonal guidance and neurite outgrowth and involves a series of highly co-ordinated and overlapping signalling pathways. The non-receptor tyrosine kinase, Focal Adhesion Kinase (FAK) has an essential role in development and is the most highly expressed kinase in the developing CNS. FAK activity is essential for neuronal cell adhesion and migration. The objective of this study was to optimise a protocol for the differentiation of the neuroblastoma cell line, SH-SY5Y. We determined the optimal extracellular matrix proteins and growth factor combinations required for the optimal differentiation of SH-SY5Y cells into neuronal-like cells and determined those conditions that induce the expression of FAK. It was confirmed that the cells were morphologically and biochemically differentiated when compared to undifferentiated cells. This is in direct contrast to commonly used differentiation methods that induce morphological differentiation but not biochemical differentiation. We conclude that we have optimised a protocol for the differentiation of SH-SY5Y cells that results in a cell population that is both morphologically and biochemically distinct from undifferentiated SH-SY5Y cells and has a distinct adhesion and spreading pattern and display extensive neurite outgrowth. This protocol will provide a neuronal model system for studying FAK activity during cell adhesion and migration events.

Heavy liquid metals: Research programs at PSI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Takeda, Y.

1996-06-01

The author describes work at PSI on thermohydraulics, thermal shock, and material tests for mechnical properties. In the presentation, the focus is on two main programs. (1) SINQ LBE target: The phase II study program for SINQ is planned. A new LBE loop is being constructed. The study has the following three objectives: (a) Pump study - design work on an electromagnetic pump to be integrated into the target. (b) Heat pipe performance test - the use of heat pipes as an additional component of the target cooling system is being considered, and it may be a way to futhermore » decouple the liquid metal and water coolant loops. (c) Mixed convection experiment - in order to find an optimal configuration of the additional flow guide for window cooling, mixed convection around the window is to be studied. The experiment will be started using water and then with LBE. (2) ESS Mercury target: For ESS target study, the following experimental studies are planned, some of which are exampled by trial experiments. (a) Flow around the window: Flow mapping around the hemi-cylindrical window will be made for optimising the flow channels and structures, (b) Geometry optimisation for minimizing a recirculation zone behind the edge of the flow separator, (c) Flow induced vibration and buckling problem for a optimised structure of the flow separator and (d) Gas-liquid two-phase flow will be studied by starting to establish the new experimental method of measuring various kinds of two-phase flow characteristics.« less

Optimisation of wire-cut EDM process parameter by Grey-based response surface methodology

NASA Astrophysics Data System (ADS)

Kumar, Amit; Soota, Tarun; Kumar, Jitendra

2018-03-01

Wire electric discharge machining (WEDM) is one of the advanced machining processes. Response surface methodology coupled with Grey relation analysis method has been proposed and used to optimise the machining parameters of WEDM. A face centred cubic design is used for conducting experiments on high speed steel (HSS) M2 grade workpiece material. The regression model of significant factors such as pulse-on time, pulse-off time, peak current, and wire feed is considered for optimising the responses variables material removal rate (MRR), surface roughness and Kerf width. The optimal condition of the machining parameter was obtained using the Grey relation grade. ANOVA is applied to determine significance of the input parameters for optimising the Grey relation grade.

A GIS-BASED METHOD FOR MULTI-OBJECTIVE EVALUATION OF PARK VEGETATION. (R824766)

EPA Science Inventory

Abstract

In this paper we describe a method for evaluating the concordance between a set of mapped landscape attributes and a set of quantitatively expressed management priorities. The method has proved to be useful in planning urban green areas, allowing objectively d...

An optimal design of wind turbine and ship structure based on neuro-response surface method

NASA Astrophysics Data System (ADS)

Lee, Jae-Chul; Shin, Sung-Chul; Kim, Soo-Young

2015-07-01

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

Multi-channel feature dictionaries for RGB-D object recognition

NASA Astrophysics Data System (ADS)

Lan, Xiaodong; Li, Qiming; Chong, Mina; Song, Jian; Li, Jun

2018-04-01

Hierarchical matching pursuit (HMP) is a popular feature learning method for RGB-D object recognition. However, the feature representation with only one dictionary for RGB channels in HMP does not capture sufficient visual information. In this paper, we propose multi-channel feature dictionaries based feature learning method for RGB-D object recognition. The process of feature extraction in the proposed method consists of two layers. The K-SVD algorithm is used to learn dictionaries in sparse coding of these two layers. In the first-layer, we obtain features by performing max pooling on sparse codes of pixels in a cell. And the obtained features of cells in a patch are concatenated to generate patch jointly features. Then, patch jointly features in the first-layer are used to learn the dictionary and sparse codes in the second-layer. Finally, spatial pyramid pooling can be applied to the patch jointly features of any layer to generate the final object features in our method. Experimental results show that our method with first or second-layer features can obtain a comparable or better performance than some published state-of-the-art methods.

An approximate dynamic programming approach to resource management in multi-cloud scenarios

NASA Astrophysics Data System (ADS)

Pietrabissa, Antonio; Priscoli, Francesco Delli; Di Giorgio, Alessandro; Giuseppi, Alessandro; Panfili, Martina; Suraci, Vincenzo

2017-03-01

The programmability and the virtualisation of network resources are crucial to deploy scalable Information and Communications Technology (ICT) services. The increasing demand of cloud services, mainly devoted to the storage and computing, requires a new functional element, the Cloud Management Broker (CMB), aimed at managing multiple cloud resources to meet the customers' requirements and, simultaneously, to optimise their usage. This paper proposes a multi-cloud resource allocation algorithm that manages the resource requests with the aim of maximising the CMB revenue over time. The algorithm is based on Markov decision process modelling and relies on reinforcement learning techniques to find online an approximate solution.

Optimisation of flame parameters for simultaneous multi-element atomic absorption spectrometric determination of trace elements in rocks

USGS Publications Warehouse

Kane, J.S.

1988-01-01

A study is described that identifies the optimum operating conditions for the accurate determination of Co, Cu, Mn, Ni, Pb, Zn, Ag, Bi and Cd using simultaneous multi-element atomic absorption spectrometry. Accuracy was measured in terms of the percentage recoveries of the analytes based on certified values in nine standard reference materials. In addition to identifying optimum operating conditions for accurate analysis, conditions resulting in serious matrix interferences and the magnitude of the interferences were determined. The listed elements can be measured with acceptable accuracy in a lean to stoicheiometric flame at measurement heights ???5-10 mm above the burner.

Non-contact radio frequency shielding and wave guiding by multi-folded transformation optics method

PubMed Central

Madni, Hamza Ahmad; Zheng, Bin; Yang, Yihao; Wang, Huaping; Zhang, Xianmin; Yin, Wenyan; Li, Erping; Chen, Hongsheng

2016-01-01

Compared with conventional radio frequency (RF) shielding methods in which the conductive coating material encloses the circuits design and the leakage problem occurs due to the gap in such conductive material, non-contact RF shielding at a distance is very promising but still impossible to achieve so far. In this paper, a multi-folded transformation optics method is proposed to design a non-contact device for RF shielding. This “open-shielded” device can shield any object at a distance from the electromagnetic waves at the operating frequency, while the object is still physically open to the outer space. Based on this, an open-carpet cloak is proposed and the functionality of the open-carpet cloak is demonstrated. Furthermore, we investigate a scheme of non-contact wave guiding to remotely control the propagation of surface waves over any obstacles. The flexibilities of such multi-folded transformation optics method demonstrate the powerfulness of the method in the design of novel remote devices with impressive new functionalities. PMID:27841358

Variability-selected active galactic nuclei from supernova search in the Chandra deep field south

NASA Astrophysics Data System (ADS)

Trevese, D.; Boutsia, K.; Vagnetti, F.; Cappellaro, E.; Puccetti, S.

2008-09-01

Context: Variability is a property shared by virtually all active galactic nuclei (AGNs), and was adopted as a criterion for their selection using data from multi epoch surveys. Low Luminosity AGNs (LLAGNs) are contaminated by the light of their host galaxies, and cannot therefore be detected by the usual colour techniques. For this reason, their evolution in cosmic time is poorly known. Consistency with the evolution derived from X-ray detected samples has not been clearly established so far, also because the low luminosity population consists of a mixture of different object types. LLAGNs can be detected by the nuclear optical variability of extended objects. Aims: Several variability surveys have been, or are being, conducted for the detection of supernovae (SNe). We propose to re-analyse these SNe data using a variability criterion optimised for AGN detection, to select a new AGN sample and study its properties. Methods: We analysed images acquired with the wide field imager at the 2.2 m ESO/MPI telescope, in the framework of the STRESS supernova survey. We selected the AXAF field centred on the Chandra Deep Field South where, besides the deep X-ray survey, various optical data exist, originating in the EIS and COMBO-17 photometric surveys and the spectroscopic database of GOODS. Results: We obtained a catalogue of 132 variable AGN candidates. Several of the candidates are X-ray sources. We compare our results with an HST variability study of X-ray and IR detected AGNs, finding consistent results. The relatively high fraction of confirmed AGNs in our sample (60%) allowed us to extract a list of reliable AGN candidates for spectroscopic follow-up observations. Table [see full text] is only available in electronic form at http://www.aanda.org

Multi-objective decision-making under uncertainty: Fuzzy logic methods

NASA Technical Reports Server (NTRS)

Hardy, Terry L.

1995-01-01

Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

Use of a genetic algorithm to improve the rail profile on Stockholm underground

NASA Astrophysics Data System (ADS)

Persson, Ingemar; Nilsson, Rickard; Bik, Ulf; Lundgren, Magnus; Iwnicki, Simon

2010-12-01

In this paper, a genetic algorithm optimisation method has been used to develop an improved rail profile for Stockholm underground. An inverted penalty index based on a number of key performance parameters was generated as a fitness function and vehicle dynamics simulations were carried out with the multibody simulation package Gensys. The effectiveness of each profile produced by the genetic algorithm was assessed using the roulette wheel method. The method has been applied to the rail profile on the Stockholm underground, where problems with rolling contact fatigue on wheels and rails are currently managed by grinding. From a starting point of the original BV50 and the UIC60 rail profiles, an optimised rail profile with some shoulder relief has been produced. The optimised profile seems similar to measured rail profiles on the Stockholm underground network and although initial grinding is required, maintenance of the profile will probably not require further grinding.

rPM6 parameters for phosphorous and sulphur-containing open-shell molecules

NASA Astrophysics Data System (ADS)

Saito, Toru; Takano, Yu

2018-03-01

In this article, we have introduced a reparameterisation of PM6 (rPM6) for phosphorus and sulphur to achieve a better description of open-shell species containing the two elements. Two sets of the parameters have been optimised separately using our training sets. The performance of the spin-unrestricted rPM6 (UrPM6) method with the optimised parameters is evaluated against 14 radical species, which contain either phosphorus or sulphur atom, comparing with the original UPM6 and the spin-unrestricted density functional theory (UDFT) methods. The standard UPM6 calculations fail to describe the adiabatic singlet-triplet energy gaps correctly, and may cause significant structural mismatches with UDFT-optimised geometries. Leaving aside three difficult cases, tests on 11 open-shell molecules strongly indicate the superior performance of UrPM6, which provides much better agreement with the results of UDFT methods for geometric and electronic properties.

Analysis of the Multi Strategy Goal Programming for Micro-Grid Based on Dynamic ant Genetic Algorithm

NASA Astrophysics Data System (ADS)

Qiu, J. P.; Niu, D. X.

Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.

Surface-region context in optimal multi-object graph-based segmentation: robust delineation of pulmonary tumors.

PubMed

Song, Qi; Chen, Mingqing; Bai, Junjie; Sonka, Milan; Wu, Xiaodong

2011-01-01

Multi-object segmentation with mutual interaction is a challenging task in medical image analysis. We report a novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field. The approach incorporates context information used during simultaneous segmentation of multiple objects. The object-surface interaction information is encoded by adding weighted inter-graph arcs to our graph model. A globally optimal solution is achieved by solving a single maximum flow problem in a low-order polynomial time. The performance of the method was evaluated in robust delineation of lung tumors in megavoltage cone-beam CT images in comparison with an expert-defined independent standard. The evaluation showed that our method generated highly accurate tumor segmentations. Compared with the conventional graph-cut method, our new approach provided significantly better results (p < 0.001). The Dice coefficient obtained by the conventional graph-cut approach (0.76 +/- 0.10) was improved to 0.84 +/- 0.05 when employing our new method for pulmonary tumor segmentation.

Hospital discharge: What are the problems, information needs and objectives of community pharmacists? A mixed method approach.

PubMed

Brühwiler, Lea D; Hersberger, Kurt E; Lutters, Monika

2017-01-01

After hospital discharge, community pharmacists are often the first health care professionals the discharged patient encounters. They reconcile and dispense prescribed medicines and provide pharmaceutical care. Compared to the roles of general practitioners, the pharmacists' needs to perform these tasks are not well known. This study aims to a) Identify community pharmacists' current problems and roles at hospital discharge, b) Assess their information needs, specifically the availability and usefulness of information, and c) Gain insight into pharmacists' objectives and ideas for discharge optimisation. A focus group was conducted with a sample of six community pharmacists from different Swiss regions. Based on these qualitative results, a nationwide online-questionnaire was sent to 1348 Swiss pharmacies. The focus group participants were concerned about their extensive workload with discharge prescriptions and about gaps in therapy. They emphasised the importance of more extensive information transfer. This applied especially to medication changes, unclear prescriptions, and information about a patient's care. Participants identified treatment continuity as a main objective when it comes to discharge optimisation. There were 194 questionnaires returned (response rate 14.4%). The majority of respondents reported to fulfil their role as defined by the Joint-FIP/WHO Guideline on Good Pharmacy Practice (rather) badly. They reported many unavailable but useful information items, like therapy changes, allergies, specifications for "off-label" medication use or contact information. Information should be delivered in a structured way, but no clear preference for one particular transfer method was found. Pharmacists requested this information in order to improve treatment continuity and patient safety, and to be able to provide better pharmaceutical care services. Surveyed Swiss community pharmacists rarely receive sufficient information along with discharge prescriptions, although it would be needed for medication reconciliation. According to the pharmacist's opinions, appropriate pharmaceutical care is therefore impeded.

Stream Clustering of Growing Objects

NASA Astrophysics Data System (ADS)

Siddiqui, Zaigham Faraz; Spiliopoulou, Myra

We study incremental clustering of objects that grow and accumulate over time. The objects come from a multi-table stream e.g. streams of Customer and Transaction. As the Transactions stream accumulates, the Customers’ profiles grow. First, we use an incremental propositionalisation to convert the multi-table stream into a single-table stream upon which we apply clustering. For this purpose, we develop an online version of K-Means algorithm that can handle these swelling objects and any new objects that arrive. The algorithm also monitors the quality of the model and performs re-clustering when it deteriorates. We evaluate our method on the PKDD Challenge 1999 dataset.

ℓ0 -based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation

NASA Astrophysics Data System (ADS)

Xu, Xia; Shi, Zhenwei; Pan, Bin

2018-07-01

Sparse unmixing aims at recovering pure materials from hyperpspectral images and estimating their abundance fractions. Sparse unmixing is actually ℓ0 problem which is NP-h ard, and a relaxation is often used. In this paper, we attempt to deal with ℓ0 problem directly via a multi-objective based method, which is a non-convex manner. The characteristics of hyperspectral images are integrated into the proposed method, which leads to a new spectra and multi-objective based sparse unmixing method (SMoSU). In order to solve the ℓ0 norm optimization problem, the spectral library is encoded in a binary vector, and a bit-wise flipping strategy is used to generate new individuals in the evolution process. However, a multi-objective method usually produces a number of non-dominated solutions, while sparse unmixing requires a single solution. How to make the final decision for sparse unmixing is challenging. To handle this problem, we integrate the spectral characteristic of hyperspectral images into SMoSU. By considering the spectral correlation in hyperspectral data, we improve the Tchebycheff decomposition function in SMoSU via a new regularization item. This regularization item is able to enforce the individual divergence in the evolution process of SMoSU. In this way, the diversity and convergence of population is further balanced, which is beneficial to the concentration of individuals. In the experiments part, three synthetic datasets and one real-world data are used to analyse the effectiveness of SMoSU, and several state-of-art sparse unmixing algorithms are compared.

Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures.

PubMed

Bonmati, Ester; Hu, Yipeng; Gibson, Eli; Uribarri, Laura; Keane, Geri; Gurusami, Kurinchi; Davidson, Brian; Pereira, Stephen P; Clarkson, Matthew J; Barratt, Dean C

2018-06-01

Navigation of endoscopic ultrasound (EUS)-guided procedures of the upper gastrointestinal (GI) system can be technically challenging due to the small fields-of-view of ultrasound and optical devices, as well as the anatomical variability and limited number of orienting landmarks during navigation. Co-registration of an EUS device and a pre-procedure 3D image can enhance the ability to navigate. However, the fidelity of this contextual information depends on the accuracy of registration. The purpose of this study was to develop and test the feasibility of a simulation-based planning method for pre-selecting patient-specific EUS-visible anatomical landmark locations to maximise the accuracy and robustness of a feature-based multimodality registration method. A registration approach was adopted in which landmarks are registered to anatomical structures segmented from the pre-procedure volume. The predicted target registration errors (TREs) of EUS-CT registration were estimated using simulated visible anatomical landmarks and a Monte Carlo simulation of landmark localisation error. The optimal planes were selected based on the 90th percentile of TREs, which provide a robust and more accurate EUS-CT registration initialisation. The method was evaluated by comparing the accuracy and robustness of registrations initialised using optimised planes versus non-optimised planes using manually segmented CT images and simulated ([Formula: see text]) or retrospective clinical ([Formula: see text]) EUS landmarks. The results show a lower 90th percentile TRE when registration is initialised using the optimised planes compared with a non-optimised initialisation approach (p value [Formula: see text]). The proposed simulation-based method to find optimised EUS planes and landmarks for EUS-guided procedures may have the potential to improve registration accuracy. Further work will investigate applying the technique in a clinical setting.

Informed multi-objective decision-making in environmental management using Pareto optimality

Treesearch

Maureen C. Kennedy; E. David Ford; Peter Singleton; Mark Finney; James K. Agee

2008-01-01

Effective decisionmaking in environmental management requires the consideration of multiple objectives that may conflict. Common optimization methods use weights on the multiple objectives to aggregate them into a single value, neglecting valuable insight into the relationships among the objectives in the management problem.

"Slit Mask Design for the Giant Magellan Telescope Multi-object Astronomical and Cosmological Spectrograph"

NASA Astrophysics Data System (ADS)

Williams, Darius; Marshall, Jennifer L.; Schmidt, Luke M.; Prochaska, Travis; DePoy, Darren L.

2018-01-01

The Giant Magellan Telescope Multi-object Astronomical and Cosmological Spectrograph (GMACS) is currently in development for the Giant Magellan Telescope (GMT). GMACS will employ slit masks with a usable diameter of approximately 0.450 m for the purpose of multi-slit spectroscopy. Of significant importance are the design constraints and parameters of the multi-object slit masks themselves as well as the means for mapping astronomical targets to physical mask locations. Analytical methods are utilized to quantify deformation effects on a potential slit mask due to thermal expansion and vignetting of target light cones. Finite element analysis (FEA) is utilized to simulate mask flexure in changing gravity vectors. The alpha version of the mask creation program for GMACS, GMACS Mask Simulator (GMS), a derivative of the OSMOS Mask Simulator (OMS), is introduced.

Multi-hybrid method for investigation of EM scattering from inhomogeneous object above a dielectric rough surface

NASA Astrophysics Data System (ADS)

Li, Jie; Guo, LiXin; He, Qiong; Wei, Bing

2012-10-01

An iterative strategy combining Kirchhoff approximation^(KA) with the hybrid finite element-boundary integral (FE-BI) method is presented in this paper to study the interactions between the inhomogeneous object and the underlying rough surface. KA is applied to study scattering from underlying rough surfaces, whereas FE-BI deals with scattering from the above target. Both two methods use updated excitation sources. Huygens equivalence principle and an iterative strategy are employed to consider the multi-scattering effects. This hybrid FE-BI-KA scheme is an improved and generalized version of previous hybrid Kirchhoff approximation-method of moments (KA-MoM). This newly presented hybrid method has the following advantages: (1) the feasibility of modeling multi-scale scattering problems (large scale underlying surface and small scale target); (2) low memory requirement as in hybrid KA-MoM; (3) the ability to deal with scattering from inhomogeneous (including coated or layered) scatterers above rough surfaces. The numerical results are given to evaluate the accuracy of the multi-hybrid technique; the computing time and memory requirements consumed in specific numerical simulation of FE-BI-KA are compared with those of MoM. The convergence performance is analyzed by studying the iteration number variation caused by related parameters. Then bistatic scattering from inhomogeneous object of different configurations above dielectric Gaussian rough surface is calculated and the influences of dielectric compositions and surface roughness on the scattering pattern are discussed.

Multi objective multi refinery optimization with environmental and catastrophic failure effects objectives

NASA Astrophysics Data System (ADS)

Khogeer, Ahmed Sirag

2005-11-01

Petroleum refining is a capital-intensive business. With stringent environmental regulations on the processing industry and declining refining margins, political instability, increased risk of war and terrorist attacks in which refineries and fuel transportation grids may be targeted, higher pressures are exerted on refiners to optimize performance and find the best combination of feed and processes to produce salable products that meet stricter product specifications, while at the same time meeting refinery supply commitments and of course making profit. This is done through multi objective optimization. For corporate refining companies and at the national level, Intea-Refinery and Inter-Refinery optimization is the second step in optimizing the operation of the whole refining chain as a single system. Most refinery-wide optimization methods do not cover multiple objectives such as minimizing environmental impact, avoiding catastrophic failures, or enhancing product spec upgrade effects. This work starts by carrying out a refinery-wide, single objective optimization, and then moves to multi objective-single refinery optimization. The last step is multi objective-multi refinery optimization, the objectives of which are analysis of the effects of economic, environmental, product spec, strategic, and catastrophic failure. Simulation runs were carried out using both MATLAB and ASPEN PIMS utilizing nonlinear techniques to solve the optimization problem. The results addressed the need to debottleneck some refineries or transportation media in order to meet the demand for essential products under partial or total failure scenarios. They also addressed how importing some high spec products can help recover some of the losses and what is needed in order to accomplish this. In addition, the results showed nonlinear relations among local and global objectives for some refineries. The results demonstrate that refineries can have a local multi objective optimum that does not follow the same trends as either global or local single objective optimums. Catastrophic failure effects on refinery operations and on local objectives are more significant than environmental objective effects, and changes in the capacity or the local objectives follow a discrete behavioral pattern, in contrast to environmental objective cases in which the effects are smoother. (Abstract shortened by UMI.)

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

PubMed

Mitra, Ramkrishna; Bandyopadhyay, Sanghamitra

2011-01-01

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.

Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

PubMed

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2015-06-01

To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

Real-time electroholography using a multiple-graphics processing unit cluster system with a single spatial light modulator and the InfiniBand network

NASA Astrophysics Data System (ADS)

Niwase, Hiroaki; Takada, Naoki; Araki, Hiromitsu; Maeda, Yuki; Fujiwara, Masato; Nakayama, Hirotaka; Kakue, Takashi; Shimobaba, Tomoyoshi; Ito, Tomoyoshi

2016-09-01

Parallel calculations of large-pixel-count computer-generated holograms (CGHs) are suitable for multiple-graphics processing unit (multi-GPU) cluster systems. However, it is not easy for a multi-GPU cluster system to accomplish fast CGH calculations when CGH transfers between PCs are required. In these cases, the CGH transfer between the PCs becomes a bottleneck. Usually, this problem occurs only in multi-GPU cluster systems with a single spatial light modulator. To overcome this problem, we propose a simple method using the InfiniBand network. The computational speed of the proposed method using 13 GPUs (NVIDIA GeForce GTX TITAN X) was more than 3000 times faster than that of a CPU (Intel Core i7 4770) when the number of three-dimensional (3-D) object points exceeded 20,480. In practice, we achieved ˜40 tera floating point operations per second (TFLOPS) when the number of 3-D object points exceeded 40,960. Our proposed method was able to reconstruct a real-time movie of a 3-D object comprising 95,949 points.

Development and validation of a rapid capillary zone electrophoresis method for the determination of aconite alkaloids in aconite roots.

PubMed

Song, Jing-Zheng; Han, Quan-Bin; Qiao, Chun-Feng; But, Paul Pui-Hay; Xu, Hong-Xi

2010-01-01

Aconites, with aconite alkaloids as the major therapeutic and toxic components, are used for the treatment of analgesic, antirheumatic and neurological symptoms. Quantification of the aconite alkaloids is important for the quality control of aconite-containing drugs. To establish a validated capillary zone electrophoresis (CZE) method for the simultaneous determination of six major alkaloids, namely aconitine, mesaconitine, hypaconitine, benzoylaconine, benzoylmesaconine and benzoylhypaconine, in crude and processed aconite roots. The CZE method was optimised and validated using a stability-indicating method. The optimised running buffer was a mixture of 200 mm Tris, 150 mm perchloric acid and 40% 1,4-dioxane (pH 7.8) with the capillary thermostated at 25 degrees C. Using the optimised method, six aconite alkaloids were well separated. The established method showed good precision, accuracy and recovery. Contents of these alkaloids in crude and processed aconites were determined and it was observed that the levels of individual alkaloids varied between samples. The developed CZE method was reliable for the quality control of aconites contained in herbal medicines. The method could also be used as an approach for toxicological studies.

Evaluation of laser ablation crater relief by white light micro interferometer

NASA Astrophysics Data System (ADS)

Gurov, Igor; Volkov, Mikhail; Zhukova, Ekaterina; Ivanov, Nikita; Margaryants, Nikita; Potemkin, Andrey; Samokhvalov, Andrey; Shelygina, Svetlana

2017-06-01

A multi-view scanning method is suggested to assess a complicated surface relief by white light interferometer. Peculiarities of the method are demonstrated on a special object in the form of quadrangular pyramid cavity, which is formed at measurement of micro-hardness of materials using a hardness gauge. An algorithm of the joint processing of multi-view scanning results is developed that allows recovering correct relief values. Laser ablation craters were studied experimentally, and their relief was recovered using the developed method. It is shown that the multi-view scanning reduces ambiguity when determining the local depth of the laser ablation craters micro relief. Results of experimental studies of the multi-view scanning method and data processing algorithm are presented.

Freeze-dried, mucoadhesive system for vaginal delivery of the HIV microbicide, dapivirine: optimisation by an artificial neural network.

PubMed

Woolfson, A David; Umrethia, Manish L; Kett, Victoria L; Malcolm, R Karl

2010-03-30

Dapivirine mucoadhesive gels and freeze-dried tablets were prepared using a 3x3x2 factorial design. An artificial neural network (ANN) with multi-layer perception was used to investigate the effect of hydroxypropyl-methylcellulose (HPMC): polyvinylpyrrolidone (PVP) ratio (X1), mucoadhesive concentration (X2) and delivery system (gel or freeze-dried mucoadhesive tablet, X3) on response variables; cumulative release of dapivirine at 24h (Q(24)), mucoadhesive force (F(max)) and zero-rate viscosity. Optimisation was performed by minimising the error between the experimental and predicted values of responses by ANN. The method was validated using check point analysis by preparing six formulations of gels and their corresponding freeze-dried tablets randomly selected from within the design space of contour plots. Experimental and predicted values of response variables were not significantly different (p>0.05, two-sided paired t-test). For gels, Q(24) values were higher than their corresponding freeze-dried tablets. F(max) values for freeze-dried tablets were significantly different (2-4 times greater, p>0.05, two-sided paired t-test) compared to equivalent gels. Freeze-dried tablets having lower values for X1 and higher values for X2 components offered the best compromise between effective dapivirine release, mucoadhesion and viscosity such that increased vaginal residence time was likely to be achieved. Copyright (c) 2009 Elsevier B.V. All rights reserved.

Methods of increasing the performance of radionuclide generators used in nuclear medicine: daughter nuclide build-up optimisation, elution-purification-concentration integration, and effective control of radionuclidic purity.

PubMed

Le, Van So; Do, Zoe Phuc-Hien; Le, Minh Khoi; Le, Vicki; Le, Natalie Nha-Truc

2014-06-10

Methods of increasing the performance of radionuclide generators used in nuclear medicine radiotherapy and SPECT/PET imaging were developed and detailed for 99Mo/99mTc and 68Ge/68Ga radionuclide generators as the cases. Optimisation methods of the daughter nuclide build-up versus stand-by time and/or specific activity using mean progress functions were developed for increasing the performance of radionuclide generators. As a result of this optimisation, the separation of the daughter nuclide from its parent one should be performed at a defined optimal time to avoid the deterioration in specific activity of the daughter nuclide and wasting stand-by time of the generator, while the daughter nuclide yield is maintained to a reasonably high extent. A new characteristic parameter of the formation-decay kinetics of parent/daughter nuclide system was found and effectively used in the practice of the generator production and utilisation. A method of "early elution schedule" was also developed for increasing the daughter nuclide production yield and specific radioactivity, thus saving the cost of the generator and improving the quality of the daughter radionuclide solution. These newly developed optimisation methods in combination with an integrated elution-purification-concentration system of radionuclide generators recently developed is the most suitable way to operate the generator effectively on the basis of economic use and improvement of purposely suitable quality and specific activity of the produced daughter radionuclides. All these features benefit the economic use of the generator, the improved quality of labelling/scan, and the lowered cost of nuclear medicine procedure. Besides, a new method of quality control protocol set-up for post-delivery test of radionuclidic purity has been developed based on the relationship between gamma ray spectrometric detection limit, required limit of impure radionuclide activity and its measurement certainty with respect to optimising decay/measurement time and product sample activity used for QC quality control. The optimisation ensures a certainty of measurement of the specific impure radionuclide and avoids wasting the useful amount of valuable purified/concentrated daughter nuclide product. This process is important for the spectrometric measurement of very low activity of impure radionuclide contamination in the radioisotope products of much higher activity used in medical imaging and targeted radiotherapy.

A Multi-Objective Optimization Technique to Model the Pareto Front of Organic Dielectric Polymers

NASA Astrophysics Data System (ADS)

Gubernatis, J. E.; Mannodi-Kanakkithodi, A.; Ramprasad, R.; Pilania, G.; Lookman, T.

Multi-objective optimization is an area of decision making that is concerned with mathematical optimization problems involving more than one objective simultaneously. Here we describe two new Monte Carlo methods for this type of optimization in the context of their application to the problem of designing polymers with more desirable dielectric and optical properties. We present results of applying these Monte Carlo methods to a two-objective problem (maximizing the total static band dielectric constant and energy gap) and a three objective problem (maximizing the ionic and electronic contributions to the static band dielectric constant and energy gap) of a 6-block organic polymer. Our objective functions were constructed from high throughput DFT calculations of 4-block polymers, following the method of Sharma et al., Nature Communications 5, 4845 (2014) and Mannodi-Kanakkithodi et al., Scientific Reports, submitted. Our high throughput and Monte Carlo methods of analysis extend to general N-block organic polymers. This work was supported in part by the LDRD DR program of the Los Alamos National Laboratory and in part by a Multidisciplinary University Research Initiative (MURI) Grant from the Office of Naval Research.

Critical induction a key quantity for the optimisation of transformer core operation

NASA Astrophysics Data System (ADS)

Ilo, A.; Pfützner, H.; Nakata, T.

2000-06-01

No-load losses P of transformers core have been considerably decreased through introduction of the so-called multi-step-lap designs. However, profound guidelines for the optimum step-number N do not exist. This study shows that the combination of both N and working induction B characterises the flux distribution. Transformer cores can operate in an over or an under-critical way depending on N and B.

Salient object detection based on multi-scale contrast.

PubMed

Wang, Hai; Dai, Lei; Cai, Yingfeng; Sun, Xiaoqiang; Chen, Long

2018-05-01

Due to the development of deep learning networks, a salient object detection based on deep learning networks, which are used to extract the features, has made a great breakthrough compared to the traditional methods. At present, the salient object detection mainly relies on very deep convolutional network, which is used to extract the features. In deep learning networks, an dramatic increase of network depth may cause more training errors instead. In this paper, we use the residual network to increase network depth and to mitigate the errors caused by depth increase simultaneously. Inspired by image simplification, we use color and texture features to obtain simplified image with multiple scales by means of region assimilation on the basis of super-pixels in order to reduce the complexity of images and to improve the accuracy of salient target detection. We refine the feature on pixel level by the multi-scale feature correction method to avoid the feature error when the image is simplified at the above-mentioned region level. The final full connection layer not only integrates features of multi-scale and multi-level but also works as classifier of salient targets. The experimental results show that proposed model achieves better results than other salient object detection models based on original deep learning networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Simultaneous segmentation of the bone and cartilage surfaces of a knee joint in 3D

NASA Astrophysics Data System (ADS)

Yin, Y.; Zhang, X.; Anderson, D. D.; Brown, T. D.; Hofwegen, C. Van; Sonka, M.

2009-02-01

We present a novel framework for the simultaneous segmentation of multiple interacting surfaces belonging to multiple mutually interacting objects. The method is a non-trivial extension of our previously reported optimal multi-surface segmentation. Considering an example application of knee-cartilage segmentation, the framework consists of the following main steps: 1) Shape model construction: Building a mean shape for each bone of the joint (femur, tibia, patella) from interactively segmented volumetric datasets. Using the resulting mean-shape model - identification of cartilage, non-cartilage, and transition areas on the mean-shape bone model surfaces. 2) Presegmentation: Employment of iterative optimal surface detection method to achieve approximate segmentation of individual bone surfaces. 3) Cross-object surface mapping: Detection of inter-bone equidistant separating sheets to help identify corresponding vertex pairs for all interacting surfaces. 4) Multi-object, multi-surface graph construction and final segmentation: Construction of a single multi-bone, multi-surface graph so that two surfaces (bone and cartilage) with zero and non-zero intervening distances can be detected for each bone of the joint, according to whether or not cartilage can be locally absent or present on the bone. To define inter-object relationships, corresponding vertex pairs identified using the separating sheets were interlinked in the graph. The graph optimization algorithm acted on the entire multiobject, multi-surface graph to yield a globally optimal solution. The segmentation framework was tested on 16 MR-DESS knee-joint datasets from the Osteoarthritis Initiative database. The average signed surface positioning error for the 6 detected surfaces ranged from 0.00 to 0.12 mm. When independently initialized, the signed reproducibility error of bone and cartilage segmentation ranged from 0.00 to 0.26 mm. The results showed that this framework provides robust, accurate, and reproducible segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multi-object segmentation problems.

Development and Evaluation of Vectorised and Multi-Core Event Reconstruction Algorithms within the CMS Software Framework

NASA Astrophysics Data System (ADS)

Hauth, T.; Innocente and, V.; Piparo, D.

2012-12-01

The processing of data acquired by the CMS detector at LHC is carried out with an object-oriented C++ software framework: CMSSW. With the increasing luminosity delivered by the LHC, the treatment of recorded data requires extraordinary large computing resources, also in terms of CPU usage. A possible solution to cope with this task is the exploitation of the features offered by the latest microprocessor architectures. Modern CPUs present several vector units, the capacity of which is growing steadily with the introduction of new processor generations. Moreover, an increasing number of cores per die is offered by the main vendors, even on consumer hardware. Most recent C++ compilers provide facilities to take advantage of such innovations, either by explicit statements in the programs sources or automatically adapting the generated machine instructions to the available hardware, without the need of modifying the existing code base. Programming techniques to implement reconstruction algorithms and optimised data structures are presented, that aim to scalable vectorization and parallelization of the calculations. One of their features is the usage of new language features of the C++11 standard. Portions of the CMSSW framework are illustrated which have been found to be especially profitable for the application of vectorization and multi-threading techniques. Specific utility components have been developed to help vectorization and parallelization. They can easily become part of a larger common library. To conclude, careful measurements are described, which show the execution speedups achieved via vectorised and multi-threaded code in the context of CMSSW.

Multi-residue analysis of pharmaceuticals in aqueous environmental samples by online solid-phase extraction-ultra-high-performance liquid chromatography-tandem mass spectrometry: optimisation and matrix effects reduction by quick, easy, cheap, effective, rugged and safe extraction.

PubMed

Bourdat-Deschamps, Marjolaine; Leang, Sokha; Bernet, Nathalie; Daudin, Jean-Jacques; Nélieu, Sylvie

2014-07-04

The aim of this study was to develop and optimise an analytical method for the quantification of a bactericide and 13 pharmaceutical products, including 8 antibiotics (fluoroquinolones, tetracyclines, sulfonamides, macrolide), in various aqueous environmental samples: soil water and aqueous fractions of pig slurry, digested pig slurry and sewage sludge. The analysis was performed by online solid-phase extraction coupled to ultra-high performance liquid chromatography with tandem mass spectrometry (online SPE-UHPLC-MS-MS). The main challenge was to minimize the matrix effects observed in mass spectrometry, mostly due to ion suppression. They depended on the dissolved organic carbon (DOC) content and its origin, and ranged between -22% and +20% and between -38% and -93% of the signal obtained without matrix, in soil water and slurry supernatant, respectively. The very variable levels of these matrix effects suggested DOC content cut-offs above which sample purification was required. These cut-offs depended on compounds, with concentrations ranging from 30 to 290mgC/L for antibiotics (except tylosine) up to 600-6400mgC/L for the most apolar compounds. A modified Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) extraction procedure was therefore optimised using an experimental design methodology, in order to purify samples with high DOC contents. Its performance led to a compromise, allowing fluoroquinolone and tetracycline analysis. The QuEChERS extraction salts consisted therefore of sodium acetate, sodium sulfate instead of magnesium sulfate, and sodium ethylenediaminetetraacetate (EDTA) as a ligand of divalent cations. The modified QuEChERS procedure employed for the extraction of pharmaceuticals in slurry and digested slurry liquid phases reduced the matrix effects for almost all the compounds, with extraction recoveries generally above 75%. The performance characteristics of the method were evaluated in terms of linearity, intra-day and inter-day precision, accuracy and limits of quantification, which reached concentration ranges of 5-270ng/L in soil water and sludge supernatant, and 31-2400ng/L in slurry and digested slurry supernatants, depending on the compounds. The new method was then successfully applied for the determination of the target compounds in environmental samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Multi-objective optimization of solid waste flows: environmentally sustainable strategies for municipalities.

PubMed

Minciardi, Riccardo; Paolucci, Massimo; Robba, Michela; Sacile, Roberto

2008-11-01

An approach to sustainable municipal solid waste (MSW) management is presented, with the aim of supporting the decision on the optimal flows of solid waste sent to landfill, recycling and different types of treatment plants, whose sizes are also decision variables. This problem is modeled with a non-linear, multi-objective formulation. Specifically, four objectives to be minimized have been taken into account, which are related to economic costs, unrecycled waste, sanitary landfill disposal and environmental impact (incinerator emissions). An interactive reference point procedure has been developed to support decision making; these methods are considered appropriate for multi-objective decision problems in environmental applications. In addition, interactive methods are generally preferred by decision makers as they can be directly involved in the various steps of the decision process. Some results deriving from the application of the proposed procedure are presented. The application of the procedure is exemplified by considering the interaction with two different decision makers who are assumed to be in charge of planning the MSW system in the municipality of Genova (Italy).

Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning.

PubMed

Huang, Chang-Qin; Yang, Shang-Ming; Pan, Yan; Lai, Han-Jiang

2018-09-01

Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.

An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems

PubMed Central

Yu, Hao; Solvang, Wei Deng

2016-01-01

Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment. PMID:27258293

An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems.

PubMed

Yu, Hao; Solvang, Wei Deng

2016-05-31

Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment.

SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Iyengar, P

2016-06-15

Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PETmore » and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.« less

Discontinuous permeable adsorptive barrier design and cost analysis: a methodological approach to optimisation.

PubMed

Santonastaso, Giovanni Francesco; Bortone, Immacolata; Chianese, Simeone; Di Nardo, Armando; Di Natale, Michele; Erto, Alessandro; Karatza, Despina; Musmarra, Dino

2017-09-19

The following paper presents a method to optimise a discontinuous permeable adsorptive barrier (PAB-D). This method is based on the comparison of different PAB-D configurations obtained by changing some of the main PAB-D design parameters. In particular, the well diameters, the distance between two consecutive passive wells and the distance between two consecutive well lines were varied, and a cost analysis for each configuration was carried out in order to define the best performing and most cost-effective PAB-D configuration. As a case study, a benzene-contaminated aquifer located in an urban area in the north of Naples (Italy) was considered. The PAB-D configuration with a well diameter of 0.8 m resulted the best optimised layout in terms of performance and cost-effectiveness. Moreover, in order to identify the best configuration for the remediation of the aquifer studied, a comparison with a continuous permeable adsorptive barrier (PAB-C) was added. In particular, this showed a 40% reduction of the total remediation costs by using the optimised PAB-D.

Genetic algorithm-based improved DOA estimation using fourth-order cumulants

NASA Astrophysics Data System (ADS)

Ahmed, Ammar; Tufail, Muhammad

2017-05-01

Genetic algorithm (GA)-based direction of arrival (DOA) estimation is proposed using fourth-order cumulants (FOC) and ESPRIT principle which results in Multiple Invariance Cumulant ESPRIT algorithm. In the existing FOC ESPRIT formulations, only one invariance is utilised to estimate DOAs. The unused multiple invariances (MIs) must be exploited simultaneously in order to improve the estimation accuracy. In this paper, a fitness function based on a carefully designed cumulant matrix is developed which incorporates MIs present in the sensor array. Better DOA estimation can be achieved by minimising this fitness function. Moreover, the effectiveness of Newton's method as well as GA for this optimisation problem has been illustrated. Simulation results show that the proposed algorithm provides improved estimation accuracy compared to existing algorithms, especially in the case of low SNR, less number of snapshots, closely spaced sources and high signal and noise correlation. Moreover, it is observed that the optimisation using Newton's method is more likely to converge to false local optima resulting in erroneous results. However, GA-based optimisation has been found attractive due to its global optimisation capability.

A multi-objective programming model for assessment the GHG emissions in MSW management

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mavrotas, George, E-mail: mavrotas@chemeng.ntua.gr; Skoulaxinou, Sotiria; Gakis, Nikos

2013-09-15

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty yearsmore » they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region.« less

Effects of efforts to optimise morbidity and mortality rounds to serve contemporary quality improvement and educational goals: a systematic review.

PubMed

Smaggus, Andrew; Mrkobrada, Marko; Marson, Alanna; Appleton, Andrew

2018-01-01

The quality and safety movement has reinvigorated interest in optimising morbidity and mortality (M&M) rounds. We performed a systematic review to identify effective means of updating M&M rounds to (1) identify and address quality and safety issues, and (2) address contemporary educational goals. Relevant databases (Medline, Embase, PubMed, Education Resource Information Centre, Cumulative Index to Nursing and Allied Health Literature, Healthstar, and Global Health) were searched to identify primary sources. Studies were included if they (1) investigated an intervention applied to M&M rounds, (2) reported outcomes relevant to the identification of quality and safety issues, or educational outcomes relevant to quality improvement (QI), patient safety or general medical education and (3) included a control group. Study quality was assessed using the Medical Education Research Study Quality Instrument and Newcastle-Ottawa Scale-Education instruments. Given the heterogeneity of interventions and outcome measures, results were analysed thematically. The final analysis included 19 studies. We identified multiple effective strategies (updating objectives, standardising elements of rounds and attaching rounds to a formal quality committee) to optimise M&M rounds for a QI/safety purpose. These efforts were associated with successful integration of quality and safety content into rounds, and increased implementation of QI interventions. Consistent effects on educational outcomes were difficult to identify, likely due to the use of methodologies ill-fitted for educational research. These results are encouraging for those seeking to optimise the quality and safety mission of M&M rounds. However, the inability to identify consistent educational effects suggests the investigation of M&M rounds could benefit from additional methodologies (qualitative, mixed methods) in order to understand the complex mechanisms driving learning at M&M rounds. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

An approach for aerodynamic optimization of transonic fan blades

NASA Astrophysics Data System (ADS)

Khelghatibana, Maryam

Aerodynamic design optimization of transonic fan blades is a highly challenging problem due to the complexity of flow field inside the fan, the conflicting design requirements and the high-dimensional design space. In order to address all these challenges, an aerodynamic design optimization method is developed in this study. This method automates the design process by integrating a geometrical parameterization method, a CFD solver and numerical optimization methods that can be applied to both single and multi-point optimization design problems. A multi-level blade parameterization is employed to modify the blade geometry. Numerical analyses are performed by solving 3D RANS equations combined with SST turbulence model. Genetic algorithms and hybrid optimization methods are applied to solve the optimization problem. In order to verify the effectiveness and feasibility of the optimization method, a singlepoint optimization problem aiming to maximize design efficiency is formulated and applied to redesign a test case. However, transonic fan blade design is inherently a multi-faceted problem that deals with several objectives such as efficiency, stall margin, and choke margin. The proposed multi-point optimization method in the current study is formulated as a bi-objective problem to maximize design and near-stall efficiencies while maintaining the required design pressure ratio. Enhancing these objectives significantly deteriorate the choke margin, specifically at high rotational speeds. Therefore, another constraint is embedded in the optimization problem in order to prevent the reduction of choke margin at high speeds. Since capturing stall inception is numerically very expensive, stall margin has not been considered as an objective in the problem statement. However, improving near-stall efficiency results in a better performance at stall condition, which could enhance the stall margin. An investigation is therefore performed on the Pareto-optimal solutions to demonstrate the relation between near-stall efficiency and stall margin. The proposed method is applied to redesign NASA rotor 67 for single and multiple operating conditions. The single-point design optimization showed +0.28 points improvement of isentropic efficiency at design point, while the design pressure ratio and mass flow are, respectively, within 0.12% and 0.11% of the reference blade. Two cases of multi-point optimization are performed: First, the proposed multi-point optimization problem is relaxed by removing the choke margin constraint in order to demonstrate the relation between near-stall efficiency and stall margin. An investigation on the Pareto-optimal solutions of this optimization shows that the stall margin has been increased with improving near-stall efficiency. The second multi-point optimization case is performed with considering all the objectives and constraints. One selected optimized design on the Pareto front presents +0.41, +0.56 and +0.9 points improvement in near-peak efficiency, near-stall efficiency and stall margin, respectively. The design pressure ratio and mass flow are, respectively, within 0.3% and 0.26% of the reference blade. Moreover the optimized design maintains the required choking margin. Detailed aerodynamic analyses are performed to investigate the effect of shape optimization on shock occurrence, secondary flows, tip leakage and shock/tip-leakage interactions in both single and multi-point optimizations.

Advanced treatment planning using direct 4D optimisation for pencil-beam scanned particle therapy

NASA Astrophysics Data System (ADS)

Bernatowicz, Kinga; Zhang, Ye; Perrin, Rosalind; Weber, Damien C.; Lomax, Antony J.

2017-08-01

We report on development of a new four-dimensional (4D) optimisation approach for scanned proton beams, which incorporates both irregular motion patterns and the delivery dynamics of the treatment machine into the plan optimiser. Furthermore, we assess the effectiveness of this technique to reduce dose to critical structures in proximity to moving targets, while maintaining effective target dose homogeneity and coverage. The proposed approach has been tested using both a simulated phantom and a clinical liver cancer case, and allows for realistic 4D calculations and optimisation using irregular breathing patterns extracted from e.g. 4DCT-MRI (4D computed tomography-magnetic resonance imaging). 4D dose distributions resulting from our 4D optimisation can achieve almost the same quality as static plans, independent of the studied geometry/anatomy or selected motion (regular and irregular). Additionally, current implementation of the 4D optimisation approach requires less than 3 min to find the solution for a single field planned on 4DCT of a liver cancer patient. Although 4D optimisation allows for realistic calculations using irregular breathing patterns, it is very sensitive to variations from the planned motion. Based on a sensitivity analysis, target dose homogeneity comparable to static plans (D5-D95  <5%) has been found only for differences in amplitude of up to 1 mm, for changes in respiratory phase  <200 ms and for changes in the breathing period of  <20 ms in comparison to the motions used during optimisation. As such, methods to robustly deliver 4D optimised plans employing 4D intensity-modulated delivery are discussed.

Fuzzy Multi-Objective Vendor Selection Problem with Modified S-CURVE Membership Function

NASA Astrophysics Data System (ADS)

Díaz-Madroñero, Manuel; Peidro, David; Vasant, Pandian

2010-06-01

In this paper, the S-Curve membership function methodology is used in a vendor selection (VS) problem. An interactive method for solving multi-objective VS problems with fuzzy goals is developed. The proposed method attempts simultaneously to minimize the total order costs, the number of rejected items and the number of late delivered items with reference to several constraints such as meeting buyers' demand, vendors' capacity, vendors' quota flexibility, vendors' allocated budget, etc. We compare in an industrial case the performance of S-curve membership functions, representing uncertainty goals and constraints in VS problems, with linear membership functions.

Feature point based 3D tracking of multiple fish from multi-view images

PubMed Central

Qian, Zhi-Ming

2017-01-01

A feature point based method is proposed for tracking multiple fish in 3D space. First, a simplified representation of the object is realized through construction of two feature point models based on its appearance characteristics. After feature points are classified into occluded and non-occluded types, matching and association are performed, respectively. Finally, the object's motion trajectory in 3D space is obtained through integrating multi-view tracking results. Experimental results show that the proposed method can simultaneously track 3D motion trajectories for up to 10 fish accurately and robustly. PMID:28665966

Feature point based 3D tracking of multiple fish from multi-view images.

PubMed

Qian, Zhi-Ming; Chen, Yan Qiu

2017-01-01

A feature point based method is proposed for tracking multiple fish in 3D space. First, a simplified representation of the object is realized through construction of two feature point models based on its appearance characteristics. After feature points are classified into occluded and non-occluded types, matching and association are performed, respectively. Finally, the object's motion trajectory in 3D space is obtained through integrating multi-view tracking results. Experimental results show that the proposed method can simultaneously track 3D motion trajectories for up to 10 fish accurately and robustly.

Optimisation of Combined Cycle Gas Turbine Power Plant in Intraday Market: Riga CHP-2 Example

NASA Astrophysics Data System (ADS)

Ivanova, P.; Grebesh, E.; Linkevics, O.

2018-02-01

In the research, the influence of optimised combined cycle gas turbine unit - according to the previously developed EM & OM approach with its use in the intraday market - is evaluated on the generation portfolio. It consists of the two combined cycle gas turbine units. The introduced evaluation algorithm saves the power and heat balance before and after the performance of EM & OM approach by making changes in the generation profile of units. The aim of this algorithm is profit maximisation of the generation portfolio. The evaluation algorithm is implemented in multi-paradigm numerical computing environment MATLab on the example of Riga CHP-2. The results show that the use of EM & OM approach in the intraday market can be profitable or unprofitable. It depends on the initial state of generation units in the intraday market and on the content of the generation portfolio.

Design and analysis of magneto rheological fluid brake for an all terrain vehicle

NASA Astrophysics Data System (ADS)

George, Luckachan K.; Tamilarasan, N.; Thirumalini, S.

2018-02-01

This work presents an optimised design for a magneto rheological fluid brake for all terrain vehicles. The actuator consists of a disk which is immersed in the magneto rheological fluid surrounded by an electromagnet. The braking torque is controlled by varying the DC current applied to the electromagnet. In the presence of a magnetic field, the magneto rheological fluid particle aligns in a chain like structure, thus increasing the viscosity. The shear stress generated causes friction in the surfaces of the rotating disk. Electromagnetic analysis of the proposed system is carried out using finite element based COMSOL multi-physics software and the amount of magnetic field generated is calculated with the help of COMSOL. The geometry is optimised and performance of the system in terms of braking torque is carried out. Proposed design reveals better performance in terms of braking torque from the existing literature.

Use of falling weight deflectometer multi-load data for pavement strength estimation

DOT National Transportation Integrated Search

2002-06-01

The objective of this study is to develop a mechanistic-empirical method for assessing pavement layer conditions and : estimating the remaining life of flexible pavements using multi-load level Falling Weight Deflectometer (FWD) deflections. A : dyna...

Virtual Engine a Tool for Truck Reliability Increase

NASA Astrophysics Data System (ADS)

Stodola, Jiri; Novotny, Pavel

2017-06-01

The internal combustion engine development process requires CAD models which deliver results for the concept phase at a very early stage and which can be further detailed on the same program platform as the development process progresses. The vibratory and acoustic behaviour of the powertrain is highly complex, consisting of many components that are subject to loads that vary greatly in magnitude and which operate at a wide range of speeds. The interaction of the crank and crankcase is a major problem for powertrain designers when optimising the vibration and noise characteristics of the powertrain. The Finite Element Method (FEM) and Multi-Body Systems (MBS) are suitable for the creation of 3-D calculation models. Non-contact measurements make it possible to verify complex calculation models. All numerical simulations and measurements are performed on a Diesel six-cylinder in-line engine.

An adaptive critic-based scheme for consensus control of nonlinear multi-agent systems

NASA Astrophysics Data System (ADS)

Heydari, Ali; Balakrishnan, S. N.

2014-12-01

The problem of decentralised consensus control of a network of heterogeneous nonlinear systems is formulated as an optimal tracking problem and a solution is proposed using an approximate dynamic programming based neurocontroller. The neurocontroller training comprises an initial offline training phase and an online re-optimisation phase to account for the fact that the reference signal subject to tracking is not fully known and available ahead of time, i.e., during the offline training phase. As long as the dynamics of the agents are controllable, and the communication graph has a directed spanning tree, this scheme guarantees the synchronisation/consensus even under switching communication topology and directed communication graph. Finally, an aerospace application is selected for the evaluation of the performance of the method. Simulation results demonstrate the potential of the scheme.

Comparison of Evolutionary (Genetic) Algorithm and Adjoint Methods for Multi-Objective Viscous Airfoil Optimizations

NASA Technical Reports Server (NTRS)

Pulliam, T. H.; Nemec, M.; Holst, T.; Zingg, D. W.; Kwak, Dochan (Technical Monitor)

2002-01-01

A comparison between an Evolutionary Algorithm (EA) and an Adjoint-Gradient (AG) Method applied to a two-dimensional Navier-Stokes code for airfoil design is presented. Both approaches use a common function evaluation code, the steady-state explicit part of the code,ARC2D. The parameterization of the design space is a common B-spline approach for an airfoil surface, which together with a common griding approach, restricts the AG and EA to the same design space. Results are presented for a class of viscous transonic airfoils in which the optimization tradeoff between drag minimization as one objective and lift maximization as another, produces the multi-objective design space. Comparisons are made for efficiency, accuracy and design consistency.

VizieR Online Data Catalog: Variability-selected AGN in Chandra DFS (Trevese+, 2008)

NASA Astrophysics Data System (ADS)

Trevese, D.; Boutsia, K.; Vagnetti, F.; Cappellaro, E.; Puccetti, S.

2008-11-01

Variability is a property shared by virtually all active galactic nuclei (AGNs), and was adopted as a criterion for their selection using data from multi epoch surveys. Low Luminosity AGNs (LLAGNs) are contaminated by the light of their host galaxies, and cannot therefore be detected by the usual colour techniques. For this reason, their evolution in cosmic time is poorly known. Consistency with the evolution derived from X-ray detected samples has not been clearly established so far, also because the low luminosity population consists of a mixture of different object types. LLAGNs can be detected by the nuclear optical variability of extended objects. Several variability surveys have been, or are being, conducted for the detection of supernovae (SNe). We propose to re-analyse these SNe data using a variability criterion optimised for AGN detection, to select a new AGN sample and study its properties. We analysed images acquired with the wide field imager at the 2.2m ESO/MPI telescope, in the framework of the STRESS supernova survey. We selected the AXAF field centred on the Chandra Deep Field South where, besides the deep X-ray survey, various optical data exist, originating in the EIS and COMBO-17 photometric surveys and the spectroscopic database of GOODS. (1 data file).

Real-time high-level video understanding using data warehouse

NASA Astrophysics Data System (ADS)

Lienard, Bruno; Desurmont, Xavier; Barrie, Bertrand; Delaigle, Jean-Francois

2006-02-01

High-level Video content analysis such as video-surveillance is often limited by computational aspects of automatic image understanding, i.e. it requires huge computing resources for reasoning processes like categorization and huge amount of data to represent knowledge of objects, scenarios and other models. This article explains how to design and develop a "near real-time adaptive image datamart", used, as a decisional support system for vision algorithms, and then as a mass storage system. Using RDF specification as storing format of vision algorithms meta-data, we can optimise the data warehouse concepts for video analysis, add some processes able to adapt the current model and pre-process data to speed-up queries. In this way, when new data is sent from a sensor to the data warehouse for long term storage, using remote procedure call embedded in object-oriented interfaces to simplified queries, they are processed and in memory data-model is updated. After some processing, possible interpretations of this data can be returned back to the sensor. To demonstrate this new approach, we will present typical scenarios applied to this architecture such as people tracking and events detection in a multi-camera network. Finally we will show how this system becomes a high-semantic data container for external data-mining.

Buildings Change Detection Based on Shape Matching for Multi-Resolution Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Abdessetar, M.; Zhong, Y.

2017-09-01

Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object's shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T0 to shape T1 and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).

Optimisation of novel method for the extraction of steviosides from Stevia rebaudiana leaves.

PubMed

Puri, Munish; Sharma, Deepika; Barrow, Colin J; Tiwary, A K

2012-06-01

Stevioside, a diterpene glycoside, is well known for its intense sweetness and is used as a non-caloric sweetener. Its potential widespread use requires an easy and effective extraction method. Enzymatic extraction of stevioside from Stevia rebaudiana leaves with cellulase, pectinase and hemicellulase, using various parameters, such as concentration of enzyme, incubation time and temperature, was optimised. Hemicellulase was observed to give the highest stevioside yield (369.23±0.11μg) in 1h in comparison to cellulase (359±0.30μg) and pectinases (333±0.55μg). Extraction from leaves under optimised conditions showed a remarkable increase in the yield (35 times) compared with a control experiment. The extraction conditions were further optimised using response surface methodology (RSM). A central composite design (CCD) was used for experimental design and analysis of the results to obtain optimal extraction conditions. Based on RSM analysis, temperature of 51-54°C, time of 36-45min and the cocktail of pectinase, cellulase and hemicellulase, set at 2% each, gave the best results. Under the optimised conditions, the experimental values were in close agreement with the prediction model and resulted in a three times yield enhancement of stevioside. The isolated stevioside was characterised through 1 H-NMR spectroscopy, by comparison with a stevioside standard. Copyright © 2011 Elsevier Ltd. All rights reserved.

Process Simulation of Aluminium Sheet Metal Deep Drawing at Elevated Temperatures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winklhofer, Johannes; Trattnig, Gernot; Lind, Christoph

Lightweight design is essential for an economic and environmentally friendly vehicle. Aluminium sheet metal is well known for its ability to improve the strength to weight ratio of lightweight structures. One disadvantage of aluminium is that it is less formable than steel. Therefore complex part geometries can only be realized by expensive multi-step production processes. One method for overcoming this disadvantage is deep drawing at elevated temperatures. In this way the formability of aluminium sheet metal can be improved significantly, and the number of necessary production steps can thereby be reduced. This paper introduces deep drawing of aluminium sheet metalmore » at elevated temperatures, a corresponding simulation method, a characteristic process and its optimization. The temperature and strain rate dependent material properties of a 5xxx series alloy and their modelling are discussed. A three dimensional thermomechanically coupled finite element deep drawing simulation model and its validation are presented. Based on the validated simulation model an optimised process strategy regarding formability, time and cost is introduced.« less

Evaluation of a High Throughput Starch Analysis Optimised for Wood

PubMed Central

Bellasio, Chandra; Fini, Alessio; Ferrini, Francesco

2014-01-01

Starch is the most important long-term reserve in trees, and the analysis of starch is therefore useful source of physiological information. Currently published protocols for wood starch analysis impose several limitations, such as long procedures and a neutralization step. The high-throughput standard protocols for starch analysis in food and feed represent a valuable alternative. However, they have not been optimised or tested with woody samples. These have particular chemical and structural characteristics, including the presence of interfering secondary metabolites, low reactivity of starch, and low starch content. In this study, a standard method for starch analysis used for food and feed (AOAC standard method 996.11) was optimised to improve precision and accuracy for the analysis of starch in wood. Key modifications were introduced in the digestion conditions and in the glucose assay. The optimised protocol was then evaluated through 430 starch analyses of standards at known starch content, matrix polysaccharides, and wood collected from three organs (roots, twigs, mature wood) of four species (coniferous and flowering plants). The optimised protocol proved to be remarkably precise and accurate (3%), suitable for a high throughput routine analysis (35 samples a day) of specimens with a starch content between 40 mg and 21 µg. Samples may include lignified organs of coniferous and flowering plants and non-lignified organs, such as leaves, fruits and rhizomes. PMID:24523863

Extracellular oxygen concentration mapping with a confocal multiphoton laser scanning microscope and TCSPC card

NASA Astrophysics Data System (ADS)

Hosny, Neveen A.; Lee, David A.; Knight, Martin M.

2010-02-01

Extracellular oxygen concentrations influence cell metabolism and tissue function. Fluorescence Lifetime Imaging Microscopy (FLIM) offers a non-invasive method for quantifying local oxygen concentrations. However, existing methods show limited spatial resolution and/or require custom made systems. This study describes a new optimised approach for quantitative extracellular oxygen detection, providing an off-the-shelf system with high spatial resolution and an improved lifetime determination over previous techniques, while avoiding systematic photon pile-up. Fluorescence lifetime detection of an oxygen sensitive fluorescent dye, tris(2,2'-bipyridyl)ruthenium(II) chloride hexahydrate [Ru(bipy)3]2+, was measured using a Becker&Hickl time-correlated single photon counting (TCSPC) card with excitation provided by a multi-photon laser. This technique was able to identify a subpopulation of isolated chondrocyte cells, seeded in three-dimensional agarose gel, displaying a significant spatial oxygen gradient. Thus this technique provides a powerful tool for quantifying spatial oxygen gradients within three-dimensional cellular models.

Fuzzy physical programming for Space Manoeuvre Vehicles trajectory optimization based on hp-adaptive pseudospectral method

NASA Astrophysics Data System (ADS)

Chai, Runqi; Savvaris, Al; Tsourdos, Antonios

2016-06-01

In this paper, a fuzzy physical programming (FPP) method has been introduced for solving multi-objective Space Manoeuvre Vehicles (SMV) skip trajectory optimization problem based on hp-adaptive pseudospectral methods. The dynamic model of SMV is elaborated and then, by employing hp-adaptive pseudospectral methods, the problem has been transformed to nonlinear programming (NLP) problem. According to the mission requirements, the solutions were calculated for each single-objective scenario. To get a compromised solution for each target, the fuzzy physical programming (FPP) model is proposed. The preference function is established with considering the fuzzy factor of the system such that a proper compromised trajectory can be acquired. In addition, the NSGA-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the FPP solution. Simulation results indicate that the proposed method is effective and feasible in terms of dealing with the multi-objective skip trajectory optimization for the SMV.

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

PubMed Central

2010-01-01

Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters. PMID:21034451

Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study.

PubMed

Richard, Vincent; Lamberto, Giuliano; Lu, Tung-Wu; Cappozzo, Aurelio; Dumas, Raphaël

2016-01-01

The use of multi-body optimisation (MBO) to estimate joint kinematics from stereophotogrammetric data while compensating for soft tissue artefact is still open to debate. Presently used joint models embedded in MBO, such as mechanical linkages, constitute a considerable simplification of joint function, preventing a detailed understanding of it. The present study proposes a knee joint model where femur and tibia are represented as rigid bodies connected through an elastic element the behaviour of which is described by a single stiffness matrix. The deformation energy, computed from the stiffness matrix and joint angles and displacements, is minimised within the MBO. Implemented as a "soft" constraint using a penalty-based method, this elastic joint description challenges the strictness of "hard" constraints. In this study, estimates of knee kinematics obtained using MBO embedding four different knee joint models (i.e., no constraints, spherical joint, parallel mechanism, and elastic joint) were compared against reference kinematics measured using bi-planar fluoroscopy on two healthy subjects ascending stairs. Bland-Altman analysis and sensitivity analysis investigating the influence of variations in the stiffness matrix terms on the estimated kinematics substantiate the conclusions. The difference between the reference knee joint angles and displacements and the corresponding estimates obtained using MBO embedding the stiffness matrix showed an average bias and standard deviation for kinematics of 0.9±3.2° and 1.6±2.3 mm. These values were lower than when no joint constraints (1.1±3.8°, 2.4±4.1 mm) or a parallel mechanism (7.7±3.6°, 1.6±1.7 mm) were used and were comparable to the values obtained with a spherical joint (1.0±3.2°, 1.3±1.9 mm). The study demonstrated the feasibility of substituting an elastic joint for more classic joint constraints in MBO.

Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study

PubMed Central

Richard, Vincent; Lamberto, Giuliano; Lu, Tung-Wu; Cappozzo, Aurelio; Dumas, Raphaël

2016-01-01

The use of multi-body optimisation (MBO) to estimate joint kinematics from stereophotogrammetric data while compensating for soft tissue artefact is still open to debate. Presently used joint models embedded in MBO, such as mechanical linkages, constitute a considerable simplification of joint function, preventing a detailed understanding of it. The present study proposes a knee joint model where femur and tibia are represented as rigid bodies connected through an elastic element the behaviour of which is described by a single stiffness matrix. The deformation energy, computed from the stiffness matrix and joint angles and displacements, is minimised within the MBO. Implemented as a “soft” constraint using a penalty-based method, this elastic joint description challenges the strictness of “hard” constraints. In this study, estimates of knee kinematics obtained using MBO embedding four different knee joint models (i.e., no constraints, spherical joint, parallel mechanism, and elastic joint) were compared against reference kinematics measured using bi-planar fluoroscopy on two healthy subjects ascending stairs. Bland-Altman analysis and sensitivity analysis investigating the influence of variations in the stiffness matrix terms on the estimated kinematics substantiate the conclusions. The difference between the reference knee joint angles and displacements and the corresponding estimates obtained using MBO embedding the stiffness matrix showed an average bias and standard deviation for kinematics of 0.9±3.2° and 1.6±2.3 mm. These values were lower than when no joint constraints (1.1±3.8°, 2.4±4.1 mm) or a parallel mechanism (7.7±3.6°, 1.6±1.7 mm) were used and were comparable to the values obtained with a spherical joint (1.0±3.2°, 1.3±1.9 mm). The study demonstrated the feasibility of substituting an elastic joint for more classic joint constraints in MBO. PMID:27314586

Identification of the contribution of contact and aerial biomechanical parameters in acrobatic performance

PubMed Central

Haering, Diane; Huchez, Aurore; Barbier, Franck; Holvoët, Patrice; Begon, Mickaël

2017-01-01

Introduction Teaching acrobatic skills with a minimal amount of repetition is a major challenge for coaches. Biomechanical, statistical or computer simulation tools can help them identify the most determinant factors of performance. Release parameters, change in moment of inertia and segmental momentum transfers were identified in the prediction of acrobatics success. The purpose of the present study was to evaluate the relative contribution of these parameters in performance throughout expertise or optimisation based improvements. The counter movement forward in flight (CMFIF) was chosen for its intrinsic dichotomy between the accessibility of its attempt and complexity of its mastery. Methods Three repetitions of the CMFIF performed by eight novice and eight advanced female gymnasts were recorded using a motion capture system. Optimal aerial techniques that maximise rotation potential at regrasp were also computed. A 14-segment-multibody-model defined through the Rigid Body Dynamics Library was used to compute recorded and optimal kinematics, and biomechanical parameters. A stepwise multiple linear regression was used to determine the relative contribution of these parameters in novice recorded, novice optimised, advanced recorded and advanced optimised trials. Finally, fixed effects of expertise and optimisation were tested through a mixed-effects analysis. Results and discussion Variation in release state only contributed to performances in novice recorded trials. Moment of inertia contribution to performance increased from novice recorded, to novice optimised, advanced recorded, and advanced optimised trials. Contribution to performance of momentum transfer to the trunk during the flight prevailed in all recorded trials. Although optimisation decreased transfer contribution, momentum transfer to the arms appeared. Conclusion Findings suggest that novices should be coached on both contact and aerial technique. Inversely, mainly improved aerial technique helped advanced gymnasts increase their performance. For both, reduction of the moment of inertia should be focused on. The method proposed in this article could be generalized to any aerial skill learning investigation. PMID:28422954

ATLAS software configuration and build tool optimisation

NASA Astrophysics Data System (ADS)

Rybkin, Grigory; Atlas Collaboration

2014-06-01

ATLAS software code base is over 6 million lines organised in about 2000 packages. It makes use of some 100 external software packages, is developed by more than 400 developers and used by more than 2500 physicists from over 200 universities and laboratories in 6 continents. To meet the challenge of configuration and building of this software, the Configuration Management Tool (CMT) is used. CMT expects each package to describe its build targets, build and environment setup parameters, dependencies on other packages in a text file called requirements, and each project (group of packages) to describe its policies and dependencies on other projects in a text project file. Based on the effective set of configuration parameters read from the requirements files of dependent packages and project files, CMT commands build the packages, generate the environment for their use, or query the packages. The main focus was on build time performance that was optimised within several approaches: reduction of the number of reads of requirements files that are now read once per package by a CMT build command that generates cached requirements files for subsequent CMT build commands; introduction of more fine-grained build parallelism at package task level, i.e., dependent applications and libraries are compiled in parallel; code optimisation of CMT commands used for build; introduction of package level build parallelism, i. e., parallelise the build of independent packages. By default, CMT launches NUMBER-OF-PROCESSORS build commands in parallel. The other focus was on CMT commands optimisation in general that made them approximately 2 times faster. CMT can generate a cached requirements file for the environment setup command, which is especially useful for deployment on distributed file systems like AFS or CERN VMFS. The use of parallelism, caching and code optimisation significantly-by several times-reduced software build time, environment setup time, increased the efficiency of multi-core computing resources utilisation, and considerably improved software developer and user experience.

Exchanging large data object in multi-agent systems

NASA Astrophysics Data System (ADS)

Al-Yaseen, Wathiq Laftah; Othman, Zulaiha Ali; Nazri, Mohd Zakree Ahmad

2016-08-01

One of the Business Intelligent solutions that is currently in use is the Multi-Agent System (MAS). Communication is one of the most important elements in MAS, especially for exchanging large low level data between distributed agents (physically). The Agent Communication Language in JADE has been offered as a secure method for sending data, whereby the data is defined as an object. However, the object cannot be used to send data to another agent in a different location. Therefore, the aim of this paper was to propose a method for the exchange of large low level data as an object by creating a proxy agent known as a Delivery Agent, which temporarily imitates the Receiver Agent. The results showed that the proposed method is able to send large-sized data. The experiments were conducted using 16 datasets ranging from 100,000 to 7 million instances. However, for the proposed method, the RAM and the CPU machine had to be slightly increased for the Receiver Agent, but the latency time was not significantly different compared to the use of the Java Socket method (non-agent and less secure). With such results, it was concluded that the proposed method can be used to securely send large data between agents.

Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

NASA Astrophysics Data System (ADS)

Baraldi, P.; Bonfanti, G.; Zio, E.

2018-03-01

The identification of the current degradation state of an industrial component and the prediction of its future evolution is a fundamental step for the development of condition-based and predictive maintenance approaches. The objective of the present work is to propose a general method for extracting a health indicator to measure the amount of component degradation from a set of signals measured during operation. The proposed method is based on the combined use of feature extraction techniques, such as Empirical Mode Decomposition and Auto-Associative Kernel Regression, and a multi-objective Binary Differential Evolution (BDE) algorithm for selecting the subset of features optimal for the definition of the health indicator. The objectives of the optimization are desired characteristics of the health indicator, such as monotonicity, trendability and prognosability. A case study is considered, concerning the prediction of the remaining useful life of turbofan engines. The obtained results confirm that the method is capable of extracting health indicators suitable for accurate prognostics.

Optimising Service Delivery of AAC AT Devices and Compensating AT for Dyslexia.

PubMed

Roentgen, Uta R; Hagedoren, Edith A V; Horions, Katrien D L; Dalemans, Ruth J P

2017-01-01

To promote successful use of Assistive Technology (AT) supporting Augmentative and Alternative Communication (AAC) and compensating for dyslexia, the last steps of their provision, delivery and instruction, use, maintenance and evaluation, were optimised. In co-creation with all stakeholders based on a list of requirements an integral method and tools were developed.

Structural optimisation of cage induction motors using finite element analysis

NASA Astrophysics Data System (ADS)

Palko, S.

The current trend in motor design is to have highly efficient, low noise, low cost, and modular motors with a high power factor. High torque motors are useful in applications like servo motors, lifts, cranes, and rolling mills. This report contains a detailed review of different optimization methods applicable in various design problems. Special attention is given to the performance of different methods, when they are used with finite element analysis (FEA) as an objective function, and accuracy problems arising from the numerical simulations. Also an effective method for designing high starting torque and high efficiency motors is presented. The method described in this work utilizes FEA combined with algorithms for the optimization of the slot geometry. The optimization algorithm modifies the position of the nodal points in the element mesh. The number of independent variables ranges from 14 to 140 in this work.

Development of a method for the analysis of hormones and pharmaceuticals in earthworms by quick, easy, cheap, effective, rugged and safe (QuEChERS) extraction followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS).

PubMed

Bergé, Alexandre; Vulliet, Emmanuelle

2015-10-01

The earthworm represents a kind of creature in contact with the soil surface and usually exposed to a variety of organic pollutants from human activities. Therefore, it can be considered as an organism of choice for identifying pollution or better understanding the input of contaminants in food chains in particular through the contributions of sludge. Moreover, the use of organisms such as soil invertebrates is to be developed for ecotoxicological risk assessment of pollutants. In this context, a simple, rapid and effective multi-residue method was developed for the determination of 31 compounds including 11 steroids, 14 veterinary antibiotics and 6 human contaminants (paracetamol, sulfamethoxazole, fluvoxamine, carbamazepine, ibuprofen, bisphenol A) in earthworm. The sample preparation procedure was based on a salting-out extraction with acetonitrile (QuEChERS approach) that was optimised with regard to the acetonitrile/water ratio used in the extraction step, the choice of the clean-up and the quantity of the matrix. The optimised extraction method exhibited recoveries that comprised between 44 and 98 % for all the tested compounds. The limits of detection of all compounds were below 14 ng g(-1) and the limits of quantification (LOQ) comprised between 1.6 and 40 ng g(-1) (wet weight). The method was therefore applied to determine the levels of pharmaceuticals and hormones in six earthworm samples collected in various soils. Concentrations up to 195 ng g(-1) for bisphenol A were determined, between a few nanograms per gram and 43.1 ng g(-1) (estriol) for hormones and between a few nanograms per gram and 73.5 ng g(-1) (florfenicol) for pharmaceuticals. Experiments were also conducted in laboratory conditions to evaluate the accumulation of the target substances by earthworm.

Characterisation and optimisation of a method for the detection and quantification of atmospherically relevant carbonyl compounds in aqueous medium

NASA Astrophysics Data System (ADS)

Rodigast, M.; Mutzel, A.; Iinuma, Y.; Haferkorn, S.; Herrmann, H.

2015-01-01

Carbonyl compounds are ubiquitous in the atmosphere and either emitted primarily from anthropogenic and biogenic sources or they are produced secondarily from the oxidation of volatile organic compounds (VOC). Despite a number of studies about the quantification of carbonyl compounds a comprehensive description of optimised methods is scarce for the quantification of atmospherically relevant carbonyl compounds. Thus a method was systematically characterised and improved to quantify carbonyl compounds. Quantification with the present method can be carried out for each carbonyl compound sampled in the aqueous phase regardless of their source. The method optimisation was conducted for seven atmospherically relevant carbonyl compounds including acrolein, benzaldehyde, glyoxal, methyl glyoxal, methacrolein, methyl vinyl ketone and 2,3-butanedione. O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride (PFBHA) was used as derivatisation reagent and the formed oximes were detected by gas chromatography/mass spectrometry (GC/MS). The main advantage of the improved method presented in this study is the low detection limit in the range of 0.01 and 0.17 μmol L-1 depending on carbonyl compounds. Furthermore best results were found for extraction with dichloromethane for 30 min followed by derivatisation with PFBHA for 24 h with 0.43 mg mL-1 PFBHA at a pH value of 3. The optimised method was evaluated in the present study by the OH radical initiated oxidation of 3-methylbutanone in the aqueous phase. Methyl glyoxal and 2,3-butanedione were found to be oxidation products in the samples with a yield of 2% for methyl glyoxal and 14% for 2,3-butanedione.

Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration

PubMed Central

Budi, Setia; de Souza, Paulo; Timms, Greg; Malhotra, Vishv; Turner, Paul

2015-01-01

This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN. Spatial Regression Test (SRT) is also utilised to promote robustness in data quality of the designed ESN. The proposed method provides high network representativeness (fit for purpose) with minimum sensor redundancy (cost), and ensures robustness by enabling the network to continue to achieve its objectives when some sensors fail. PMID:26633392

Learning by Doing--Teaching Systematic Review Methods in 8 Weeks

ERIC Educational Resources Information Center

Li, Tianjing; Saldanha, Ian J.; Vedula, S. Swaroop; Yu, Tsung; Rosman, Lori; Twose, Claire; Goodman, Steven N.; Dickersin, Kay

2014-01-01

Objective: The objective of this paper is to describe the course "Systematic Reviews and Meta-analysis" at the Johns Hopkins Bloomberg School of Public Health. Methods: A distinct feature of our course is a group project in which students, assigned to multi-disciplinary groups, conduct a systematic review. In-class sessions comprise…

Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

PubMed

Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

2016-01-01

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants

PubMed Central

Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

2016-01-01

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996–2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated. PMID:27010658

Optimising parameters for the differentiation of SH-SY5Y cells to study cell adhesion and cell migration

PubMed Central

2013-01-01

Background Cell migration is a fundamental biological process and has an important role in the developing brain by regulating a highly specific pattern of connections between nerve cells. Cell migration is required for axonal guidance and neurite outgrowth and involves a series of highly co-ordinated and overlapping signalling pathways. The non-receptor tyrosine kinase, Focal Adhesion Kinase (FAK) has an essential role in development and is the most highly expressed kinase in the developing CNS. FAK activity is essential for neuronal cell adhesion and migration. Results The objective of this study was to optimise a protocol for the differentiation of the neuroblastoma cell line, SH-SY5Y. We determined the optimal extracellular matrix proteins and growth factor combinations required for the optimal differentiation of SH-SY5Y cells into neuronal-like cells and determined those conditions that induce the expression of FAK. It was confirmed that the cells were morphologically and biochemically differentiated when compared to undifferentiated cells. This is in direct contrast to commonly used differentiation methods that induce morphological differentiation but not biochemical differentiation. Conclusions We conclude that we have optimised a protocol for the differentiation of SH-SY5Y cells that results in a cell population that is both morphologically and biochemically distinct from undifferentiated SH-SY5Y cells and has a distinct adhesion and spreading pattern and display extensive neurite outgrowth. This protocol will provide a neuronal model system for studying FAK activity during cell adhesion and migration events. PMID:24025096

Detection of Legionella pneumophila on clinical samples and susceptibility assessment by flow cytometry.

PubMed

Faria-Ramos, I; Costa-de-Oliveira, S; Barbosa, J; Cardoso, A; Santos-Antunes, J; Rodrigues, A G; Pina-Vaz, C

2012-12-01

Culture in selective media represents the standard diagnostic method to confirm Legionella pneumophila infection, despite requiring a prolonged incubation period; antigen detection by immunofluorescence (IFS) and molecular techniques are also available, but they do not allow antimicrobial susceptibility evaluation. Our objective was to optimise flow cytometry (FC) protocols for the detection of L. pneumophila in respiratory samples and for susceptibility evaluation to first-line drugs. In order to optimise the FC protocol, a specific monoclonal antibody, conjugated with fluorescein isothiocyanate (FITC), was incubated with type strain L. pneumophila ATCC 33152. The limit of detection was established by analysing serial dilutions of bacterial suspension; specificity was assayed using mixtures of prokaryotic and eukaryotic microorganisms. The optimised FC protocol was used to assess 50 respiratory samples and compared with IFS evaluation. The susceptibility profile to erythromycin, ciprofloxacin and levofloxacin was evaluated by FC using propidium iodide and SYBR Green fluorescent dyes; the results were compared with the Etest afterwards. The optimal specific antibody concentration was 20 μg/ml; 10(2)/ml Legionella organisms were detected by this protocol and no cross-reactions with other microorganisms were detected. The five positive respiratory samples (10 %) determined by IFS were also detected by FC, showing 100 % correlation. After 1 h of incubation at 37 °C with different antimicrobials, SYBR Green staining could discriminate between treated and non-treated cells. A novel flow cytometric approach for the detection of L. pneumophila from clinical samples and susceptibility evaluation is now available, representing an important step forward for the diagnosis of this very relevant agent.

Radiological Protection and Nuclear Engineering Studies in Multi-MW Target Systems

NASA Astrophysics Data System (ADS)

Luis, Raul Fernandes

Several innovative projects involving nuclear technology have emerged around the world in recent years, for applications such as spallation neutron sources, accelerator-driven systems for the transmutation of nuclear waste and radioactive ion beam (RIB) production. While the available neutron Wuxes from nuclear reactors did not increase substantially in intensity over the past three decades, the intensities of neutron sources produced in spallation targets have increased steadily, and should continue to do so during the 21st century. Innovative projects like ESS, MYRRHA and EURISOL lie at the forefront of the ongoing pursuit for increasingly bright neutron sources; driven by proton beams with energies up to 2 GeV and intensities up to several mA, the construction of their proposed facilities involves complex Nuclear Technology and Radiological Protection design studies executed by multidisciplinary teams of scientists and engineers from diUerent branches of Science. The intense neutron Wuxes foreseen for those facilities can be used in several scientiVc research Velds, such as Nuclear Physics and Astrophysics, Medicine and Materials Science. In this work, the target systems of two facilitites for the production of RIBs using the Isotope Separation On-Line (ISOL) method were studied in detail: ISOLDE, operating at CERN since 1967, and EURISOL, the next-generation ISOL facility to be built in Europe. For the EURISOL multi-MW target station, a detailed study of Radiological Protection was carried out using the Monte Carlo code FLUKA. Simulations were done to assess neutron Wuences, Vssion rates, ambient dose equivalent rates during operation and after shutdown and the production of radioactive nuclei in the targets and surrounding materials. DiUerent materials were discussed for diUerent components of the target system, aiming at improving its neutronics performance while keeping the residual activities resulting from material activation as low as possible. The second goal of this work was to perform an optimisation study for the ISOLDE neutron converter and Vssion target system. The target system was simulated using FLUKA and the cross section codes TALYS and ABRABLA, with the objective of maximising the performance of the system for the production of pure beams of neutron-rich isotopes, suppressing the contaminations by undesired neutron-deficient isobars. Two alternative target systems were proposed in the optimisation studies; the simplest of the two, with some modiVcations, was built as a prototype and tested at ISOLDE. The experimental results clearly show that it is possible, with simple changes in the layouts of the target systems, to produce purer beams of neutron-rich isotopes around the doubly magic nuclei 78Ni and 132Sn. A study of Radiological Protection was also performed, comparing the performances of the prototype target system and the standard ISOLDE target system. None

Optical sensing in laser machining

NASA Astrophysics Data System (ADS)

Smurov, Igor; Doubenskaia, Maria

2009-05-01

Optical monitoring of temperature evolution and temperature distribution in laser machining provides important information to optimise and to control technological process under study. The multi-wavelength pyrometer is used to measure brightness temperature under the pulsed action of Nd:YAG laser on stainless steel substrates. Specially developed "notch" filters (10-6 transparency at 1.06 μm wavelength) are applied to avoid the influence of laser radiation on temperature measurements. The true temperature is restored based on the method of multi-colour pyrometry. Temperature monitoring of the thin-walled gilded kovar boxes is applied to detect deviation of the welding seam from its optimum position. The pyrometers are used to control CO2-laser welding of steel and Ti plates: misalignment of the welded plates, variation of the welding geometry, internal defects, deviation of the laser beam trajectory from the junction, etc. The temperature profiles along and across the welding axis are measured by the 2D pyrometer. When using multi-component powder blends in laser cladding, for example metal matrix composite with ceramic reinforcement, one needs to control temperature of the melt to avoid thermal decomposition of certain compounds (as WC) and to assure melting of the base metal (as Co). Infra-red camera FLIR Phoenix RDAS provides detailed information on distribution of brightness temperature in laser cladding zone. CCD-camera based diagnostic system is used to measure particles-in-flight velocity and size distribution.

Connected Component Model for Multi-Object Tracking.

PubMed

He, Zhenyu; Li, Xin; You, Xinge; Tao, Dacheng; Tang, Yuan Yan

2016-08-01

In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches.

Multi-classification of cell deformation based on object alignment and run length statistic.

PubMed

Li, Heng; Liu, Zhiwen; An, Xing; Shi, Yonggang

2014-01-01

Cellular morphology is widely applied in digital pathology and is essential for improving our understanding of the basic physiological processes of organisms. One of the main issues of application is to develop efficient methods for cell deformation measurement. We propose an innovative indirect approach to analyze dynamic cell morphology in image sequences. The proposed approach considers both the cellular shape change and cytoplasm variation, and takes each frame in the image sequence into account. The cell deformation is measured by the minimum energy function of object alignment, which is invariant to object pose. Then an indirect analysis strategy is employed to overcome the limitation of gradual deformation by run length statistic. We demonstrate the power of the proposed approach with one application: multi-classification of cell deformation. Experimental results show that the proposed method is sensitive to the morphology variation and performs better than standard shape representation methods.

Participatory Planning, Monitoring and Evaluation of Multi-Stakeholder Platforms in Integrated Landscape Initiatives.

PubMed

Kusters, Koen; Buck, Louise; de Graaf, Maartje; Minang, Peter; van Oosten, Cora; Zagt, Roderick

2018-07-01

Integrated landscape initiatives typically aim to strengthen landscape governance by developing and facilitating multi-stakeholder platforms. These are institutional coordination mechanisms that enable discussions, negotiations, and joint planning between stakeholders from various sectors in a given landscape. Multi-stakeholder platforms tend to involve complex processes with diverse actors, whose objectives and focus may be subjected to periodic re-evaluation, revision or reform. In this article we propose a participatory method to aid planning, monitoring, and evaluation of such platforms, and we report on experiences from piloting the method in Ghana and Indonesia. The method is comprised of three components. The first can be used to look ahead, identifying priorities for future multi-stakeholder collaboration in the landscape. It is based on the identification of four aspirations that are common across multi-stakeholder platforms in integrated landscape initiatives. The second can be used to look inward. It focuses on the processes within an existing multi-stakeholder platform in order to identify areas for possible improvement. The third can be used to look back, identifying the main outcomes of an existing platform and comparing them to the original objectives. The three components can be implemented together or separately. They can be used to inform planning and adaptive management of the platform, as well as to demonstrate performance and inform the design of new interventions.

Near infrared spectrometric technique for testing fruit quality: optimisation of regression models using genetic algorithms

NASA Astrophysics Data System (ADS)

Isingizwe Nturambirwe, J. Frédéric; Perold, Willem J.; Opara, Umezuruike L.

2016-02-01

Near infrared (NIR) spectroscopy has gained extensive use in quality evaluation. It is arguably one of the most advanced spectroscopic tools in non-destructive quality testing of food stuff, from measurement to data analysis and interpretation. NIR spectral data are interpreted through means often involving multivariate statistical analysis, sometimes associated with optimisation techniques for model improvement. The objective of this research was to explore the extent to which genetic algorithms (GA) can be used to enhance model development, for predicting fruit quality. Apple fruits were used, and NIR spectra in the range from 12000 to 4000 cm-1 were acquired on both bruised and healthy tissues, with different degrees of mechanical damage. GAs were used in combination with partial least squares regression methods to develop bruise severity prediction models, and compared to PLS models developed using the full NIR spectrum. A classification model was developed, which clearly separated bruised from unbruised apple tissue. GAs helped improve prediction models by over 10%, in comparison with full spectrum-based models, as evaluated in terms of error of prediction (Root Mean Square Error of Cross-validation). PLS models to predict internal quality, such as sugar content and acidity were developed and compared to the versions optimized by genetic algorithm. Overall, the results highlighted the potential use of GA method to improve speed and accuracy of fruit quality prediction.

Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

PubMed

Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

2015-06-18

Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks

PubMed Central

Li, Ming; Miao, Chunyan; Leung, Cyril

2015-01-01

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches. PMID:26690162

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks.

PubMed

Li, Ming; Miao, Chunyan; Leung, Cyril

2015-12-04

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

PubMed

Ma, Changxi; Hao, Wei; Pan, Fuquan; Xiang, Wang

2018-01-01

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

Characterisation and optimisation of a sample preparation method for the detection and quantification of atmospherically relevant carbonyl compounds in aqueous medium

NASA Astrophysics Data System (ADS)

Rodigast, M.; Mutzel, A.; Iinuma, Y.; Haferkorn, S.; Herrmann, H.

2015-06-01

Carbonyl compounds are ubiquitous in the atmosphere and either emitted primarily from anthropogenic and biogenic sources or they are produced secondarily from the oxidation of volatile organic compounds. Despite a number of studies about the quantification of carbonyl compounds a comprehensive description of optimised methods is scarce for the quantification of atmospherically relevant carbonyl compounds. The method optimisation was conducted for seven atmospherically relevant carbonyl compounds including acrolein, benzaldehyde, glyoxal, methyl glyoxal, methacrolein, methyl vinyl ketone and 2,3-butanedione. O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride (PFBHA) was used as derivatisation reagent and the formed oximes were detected by gas chromatography/mass spectrometry (GC/MS). With the present method quantification can be carried out for each carbonyl compound originating from fog, cloud and rain or sampled from the gas- and particle phase in water. Detection limits between 0.01 and 0.17 μmol L-1 were found, depending on carbonyl compounds. Furthermore, best results were found for the derivatisation with a PFBHA concentration of 0.43 mg mL-1 for 24 h followed by a subsequent extraction with dichloromethane for 30 min at pH = 1. The optimised method was evaluated in the present study by the OH radical initiated oxidation of 3-methylbutanone in the aqueous phase. Methyl glyoxal and 2,3-butanedione were found to be oxidation products in the samples with a yield of 2% for methyl glyoxal and 14% for 2,3-butanedione after a reaction time of 5 h.

Sequential projection pursuit for optimised vibration-based damage detection in an experimental wind turbine blade

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2018-02-01

To advance the concept of smart structures in large systems, such as wind turbines (WTs), it is desirable to be able to detect structural damage early while using minimal instrumentation. Data-driven vibration-based damage detection methods can be competitive in that respect because global vibrational responses encompass the entire structure. Multivariate damage sensitive features (DSFs) extracted from acceleration responses enable to detect changes in a structure via statistical methods. However, even though such DSFs contain information about the structural state, they may not be optimised for the damage detection task. This paper addresses the shortcoming by exploring a DSF projection technique specialised for statistical structural damage detection. High dimensional initial DSFs are projected onto a low-dimensional space for improved damage detection performance and simultaneous computational burden reduction. The technique is based on sequential projection pursuit where the projection vectors are optimised one by one using an advanced evolutionary strategy. The approach is applied to laboratory experiments with a small-scale WT blade under wind-like excitations. Autocorrelation function coefficients calculated from acceleration signals are employed as DSFs. The optimal numbers of projection vectors are identified with the help of a fast forward selection procedure. To benchmark the proposed method, selections of original DSFs as well as principal component analysis scores from these features are additionally investigated. The optimised DSFs are tested for damage detection on previously unseen data from the healthy state and a wide range of damage scenarios. It is demonstrated that using selected subsets of the initial and transformed DSFs improves damage detectability compared to the full set of features. Furthermore, superior results can be achieved by projecting autocorrelation coefficients onto just a single optimised projection vector.

Test of multi-object exoplanet search spectral interferometer

NASA Astrophysics Data System (ADS)

Zhang, Kai; Wang, Liang; Jiang, Haijiao; Zhu, Yongtian; Hou, Yonghui; Dai, Songxin; Tang, Jin; Tang, Zhen; Zeng, Yizhong; Chen, Yi; Wang, Lei; Hu, Zhongwen

2014-07-01

Exoplanet detection, a highlight in the current astronomy, will be part of puzzle in astronomical and astrophysical future, which contains dark energy, dark matter, early universe, black hole, galactic evolution and so on. At present, most of the detected Exoplanets are confirmed through methods of radial velocity and transit. Guo shoujing Telescope well known as LAMOST is an advanced multi-object spectral survey telescope equipped with 4000 fibers and 16 low resolution fiber spectrographs. To explore its potential in different astronomical activities, a new radial velocity method named Externally Dispersed Interferometry (EDI) is applied to serve Exoplanet detection through combining a fixed-delay interferometer with the existing spectrograph in medium spectral resolution mode (R=5,000-10,000). This new technology has an impressive feature to enhance radial velocity measuring accuracy of the existing spectrograph through installing a fixed-delay interferometer in front of spectrograph. This way produces an interference spectrum with higher sensitivity to Doppler Effect by interference phase and fixed delay. This relative system named Multi-object Exoplanet Search Spectral Interferometer (MESSI) is composed of a few parts, including a pair of multi-fiber coupling sockets, a remote control iodine subsystem, a multi-object fixed delay interferometer and the existing spectrograph. It covers from 500 to 550 nm and simultaneously observes up to 21 stars. Even if it's an experimental instrument at present, it's still well demonstrated in paper that how MESSI does explore an effective way to build its own system under the existing condition of LAMOST and get its expected performance for multi-object Exoplanet detection, especially instrument stability and its special data reduction. As a result of test at lab, inside temperature of its instrumental chamber is stable in a range of +/-0.5degree Celsius within 12 hours, and the direct instrumental stability without further observation correction is equivalent to be +/-50m/s every 20mins.

Fuzzy Multi-Objective Transportation Planning with Modified S-Curve Membership Function

NASA Astrophysics Data System (ADS)

Peidro, D.; Vasant, P.

2009-08-01

In this paper, the S-Curve membership function methodology is used in a transportation planning decision (TPD) problem. An interactive method for solving multi-objective TPD problems with fuzzy goals, available supply and forecast demand is developed. The proposed method attempts simultaneously to minimize the total production and transportation costs and the total delivery time with reference to budget constraints and available supply, machine capacities at each source, as well as forecast demand and warehouse space constraints at each destination. We compare in an industrial case the performance of S-curve membership functions, representing uncertainty goals and constraints in TPD problems, with linear membership functions.

Anti-buckling design of variable stiffness composite cylinder under combined loading based on the multi-objective optimization method

NASA Astrophysics Data System (ADS)

Zheng, Y.; Chen, J.

2018-06-01

Variable stiffness composite structures take full advantages of composite’s design ability. An enlarged design space will make the structure’s performance more excellent. Through an optimal design of a variable stiffness cylinder, the buckling capacity of the cylinder will be increased as compared with its constant stiffness counterpart. In this paper, variable stiffness composite cylinders sustaining combined loadings are considered, and the optimization is conducted based on the multi-objective optimization method. The results indicate that variable stiffness cylinder’s loading capacity is increased significantly as compared with the constant stiffness, especially when an inhomogeneous loading is considered.

Implementation and comparative analysis of the optimisations produced by evolutionary algorithms for the parameter extraction of PSP MOSFET model

NASA Astrophysics Data System (ADS)

Hadia, Sarman K.; Thakker, R. A.; Bhatt, Kirit R.

2016-05-01

The study proposes an application of evolutionary algorithms, specifically an artificial bee colony (ABC), variant ABC and particle swarm optimisation (PSO), to extract the parameters of metal oxide semiconductor field effect transistor (MOSFET) model. These algorithms are applied for the MOSFET parameter extraction problem using a Pennsylvania surface potential model. MOSFET parameter extraction procedures involve reducing the error between measured and modelled data. This study shows that ABC algorithm optimises the parameter values based on intelligent activities of honey bee swarms. Some modifications have also been applied to the basic ABC algorithm. Particle swarm optimisation is a population-based stochastic optimisation method that is based on bird flocking activities. The performances of these algorithms are compared with respect to the quality of the solutions. The simulation results of this study show that the PSO algorithm performs better than the variant ABC and basic ABC algorithm for the parameter extraction of the MOSFET model; also the implementation of the ABC algorithm is shown to be simpler than that of the PSO algorithm.

Optimal Earth's reentry disposal of the Galileo constellation

NASA Astrophysics Data System (ADS)

Armellin, Roberto; San-Juan, Juan F.

2018-02-01

Nowadays there is international consensus that space activities must be managed to minimize debris generation and risk. The paper presents a method for the end-of-life (EoL) disposal of spacecraft in Medium Earth Orbit (MEO). The problem is formulated as a multiobjective optimisation one, which is solved with an evolutionary algorithm. An impulsive manoeuvre is optimised to reenter the spacecraft in Earth's atmosphere within 100 years. Pareto optimal solutions are obtained using the manoeuvre Δv and the time-to-reentry as objective functions to be minimised. To explore at the best the search space a semi-analytical orbit propagator, which can propagate an orbit for 100 years in few seconds, is adopted. An in-depth analysis of the results is carried out to understand the conditions leading to a fast reentry with minimum propellant. For this aim a new way of representing the disposal solutions is introduced. With a single 2D plot we are able to fully describe the time evolution of all the relevant orbital parameters as well as identify the conditions that enables the eccentricity build-up. The EoL disposal of the Galileo constellation is used as test case.

Sybil--efficient constraint-based modelling in R.

PubMed

Gelius-Dietrich, Gabriel; Desouki, Abdelmoneim Amer; Fritzemeier, Claus Jonathan; Lercher, Martin J

2013-11-13

Constraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automated search for model improvements. Furthermore, available implementations cannot easily be extended or adapted by users. Here, we present sybil, an open source software library for constraint-based analyses in R; R is a free, platform-independent environment for statistical computing and graphics that is widely used in bioinformatics. Among other functions, sybil currently provides efficient methods for flux-balance analysis (FBA), MOMA, and ROOM that are about ten times faster than previous implementations when calculating the effect of whole-genome single gene deletions in silico on a complete E. coli metabolic model. Due to the object-oriented architecture of sybil, users can easily build analysis pipelines in R or even implement their own constraint-based algorithms. Based on its highly efficient communication with different mathematical optimisation programs, sybil facilitates the exploration of high-dimensional optimisation problems on small time scales. Sybil and all its dependencies are open source. Sybil and its documentation are available for download from the comprehensive R archive network (CRAN).

Improved packing of protein side chains with parallel ant colonies.

PubMed

Quan, Lijun; Lü, Qiang; Li, Haiou; Xia, Xiaoyan; Wu, Hongjie

2014-01-01

The accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains. We present a side-chain modelling method, pacoPacker, which uses a parallel ant colony optimisation strategy based on sharing a single pheromone matrix. This parallel approach combines different sources of energy functions and generates protein side-chain conformations with the lowest energies jointly determined by the various energy functions. We further optimised the selected rotamers to construct subrotamer by rotamer minimisation, which reasonably improved the discreteness of the rotamer library. We focused on improving the accuracy of side-chain conformation prediction. For a testing set of 442 proteins, 87.19% of X1 and 77.11% of X12 angles were predicted correctly within 40° of the X-ray positions. We compared the accuracy of pacoPacker with state-of-the-art methods, such as CIS-RR and SCWRL4. We analysed the results from different perspectives, in terms of protein chain and individual residues. In this comprehensive benchmark testing, 51.5% of proteins within a length of 400 amino acids predicted by pacoPacker were superior to the results of CIS-RR and SCWRL4 simultaneously. Finally, we also showed the advantage of using the subrotamers strategy. All results confirmed that our parallel approach is competitive to state-of-the-art solutions for packing side chains. This parallel approach combines various sources of searching intelligence and energy functions to pack protein side chains. It provides a frame-work for combining different inaccuracy/usefulness objective functions by designing parallel heuristic search algorithms.

Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

NASA Astrophysics Data System (ADS)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

Enabling Data-as- a-Service (DaaS) - Biggest Challenge of Geoscience Australia

NASA Astrophysics Data System (ADS)

Bastrakova, I.; Kemp, C.; Car, N. J.

2016-12-01

Geoscience Australia (GA) is recognised and respected as the national repository and steward of multiple national significance data collections that provides geoscience information, services and capability to the Australian Government, industry and stakeholders. Provision of Data-as-a-Service is both GA's key responsibility and core business. Through the Science First Transformation Program GA is undergoing a significant rethinking of its data architecture, curation and access to support the Digital Science capability for which DaaS forms both a dependency and underpins its implementation. DaaS, being a service, means we can deliver its outputs in multiple ways thus providing users with data on demand in ready-for-consumption forms. We can then to reuse prebuilt data constructions to allow self-serviced integration of data underpinned by dynamic query tools. In GA's context examples of DaaS are the Australian Geoscience Data Cube, the Foundation Spatial Data Framework and data served through several Virtual Laboratories. We have implemented a three-layered architecture for DaaS in order to store and manage the data while honouring the semantics of Scientific Data Models defined by subject matter experts and GA's Enterprise Data Architecture as well as retain that delivery flexibility. The foundation layer of DaaS is Canonical Datasets, which are optimised for a long-term data stewardship and curation. Data is well structured, standardised, described and audited. All data creation and editing happen within this layer. The middle Data Transformation layer assists with transformation of data from Canonical Datasets to data integration layer. It provides mechanisms for multi-format and multi-technology data transformation. The top Data Integration layer is optimised for data access. Data can be easily reused and repurposed; data formats made available are optimised for scientific computing and adjusted for access by multiple applications, tools and libraries. Moving to DaaS enables GA to increase data alertness, generate new capabilities and be prepared for emerging technological challengers.

Evaluation and optimisation of phenomenological multi-step soot model for spray combustion under diesel engine-like operating conditions

NASA Astrophysics Data System (ADS)

Pang, Kar Mun; Jangi, Mehdi; Bai, Xue-Song; Schramm, Jesper

2015-05-01

In this work, a two-dimensional computational fluid dynamics study is reported of an n-heptane combustion event and the associated soot formation process in a constant volume combustion chamber. The key interest here is to evaluate the sensitivity of the chemical kinetics and submodels of a semi-empirical soot model in predicting the associated events. Numerical computation is performed using an open-source code and a chemistry coordinate mapping approach is used to expedite the calculation. A library consisting of various phenomenological multi-step soot models is constructed and integrated with the spray combustion solver. Prior to the soot modelling, combustion simulations are carried out. Numerical results show that the ignition delay times and lift-off lengths exhibit good agreement with the experimental measurements across a wide range of operating conditions, apart from those in the cases with ambient temperature lower than 850 K. The variation of the soot precursor production with respect to the change of ambient oxygen levels qualitatively agrees with that of the conceptual models when the skeletal n-heptane mechanism is integrated with a reduced pyrene chemistry. Subsequently, a comprehensive sensitivity analysis is carried out to appraise the existing soot formation and oxidation submodels. It is revealed that the soot formation is captured when the surface growth rate is calculated using a square root function of the soot specific surface area and when a pressure-dependent model constant is considered. An optimised soot model is then proposed based on the knowledge gained through this exercise. With the implementation of optimised model, the simulated soot onset and transport phenomena before reaching quasi-steady state agree reasonably well with the experimental observation. Also, variation of spatial soot distribution and soot mass produced at oxygen molar fractions ranging from 10.0 to 21.0% for both low and high density conditions are reproduced.

Fox-7 for Insensitive Boosters

DTIC Science & Technology

2010-08-01

cavitation , and therefore nucleation, to occur at each frequency. As well as producing ultrasound at different frequencies, the method of delivery of...processing techniques using ultrasound , designed to optimise FOX-7 crystal size and morphology to improve booster formulations, and results from these...7 booster formulations. Also included are particle processing techniques using ultrasound , designed to optimise FOX-7 crystal size and morphology

Design of distributed PID-type dynamic matrix controller for fractional-order systems

NASA Astrophysics Data System (ADS)

Wang, Dawei; Zhang, Ridong

2018-01-01

With the continuous requirements for product quality and safety operation in industrial production, it is difficult to describe the complex large-scale processes with integer-order differential equations. However, the fractional differential equations may precisely represent the intrinsic characteristics of such systems. In this paper, a distributed PID-type dynamic matrix control method based on fractional-order systems is proposed. First, the high-order approximate model of integer order is obtained by utilising the Oustaloup method. Then, the step response model vectors of the plant is obtained on the basis of the high-order model, and the online optimisation for multivariable processes is transformed into the optimisation of each small-scale subsystem that is regarded as a sub-plant controlled in the distributed framework. Furthermore, the PID operator is introduced into the performance index of each subsystem and the fractional-order PID-type dynamic matrix controller is designed based on Nash optimisation strategy. The information exchange among the subsystems is realised through the distributed control structure so as to complete the optimisation task of the whole large-scale system. Finally, the control performance of the designed controller in this paper is verified by an example.

Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression.

PubMed

Ashrafi, Parivash; Sun, Yi; Davey, Neil; Adams, Roderick G; Wilkinson, Simon C; Moss, Gary Patrick

2018-03-01

The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters. Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or 'chemical space' of the key descriptors to assess the effect of the data range on model quality. The Hyper-prior Smoothbox kernel results in the best models for the majority of data sets, and they exhibited significantly better performance than benchmark quantitative structure-permeability relationship (QSPR) models. When the data sets were systematically reduced in size, the different optimisation methods generally retained their statistical quality, whereas benchmark QSPR models performed poorly. The design of the data set, and possibly also the approach to validation of the model, is critical in the development of improved models. The size of the data set, if carefully controlled, was not generally a significant factor for these models and that models of excellent statistical quality could be produced from substantially smaller data sets. © 2018 Royal Pharmaceutical Society.

On the use of PGD for optimal control applied to automated fibre placement

NASA Astrophysics Data System (ADS)

Bur, N.; Joyot, P.

2017-10-01

Automated Fibre Placement (AFP) is an incipient manufacturing process for composite structures. Despite its concep-tual simplicity it involves many complexities related to the necessity of melting the thermoplastic at the interface tape-substrate, ensuring the consolidation that needs the diffusion of molecules and control the residual stresses installation responsible of the residual deformations of the formed parts. The optimisation of the process and the determination of the process window cannot be achieved in a traditional way since it requires a plethora of trials/errors or numerical simulations, because there are many parameters involved in the characterisation of the material and the process. Using reduced order modelling such as the so called Proper Generalised Decomposition method, allows the construction of multi-parametric solution taking into account many parameters. This leads to virtual charts that can be explored on-line in real time in order to perform process optimisation or on-line simulation-based control. Thus, for a given set of parameters, determining the power leading to an optimal temperature becomes easy. However, instead of controlling the power knowing the temperature field by particularizing an abacus, we propose here an approach based on optimal control: we solve by PGD a dual problem from heat equation and optimality criteria. To circumvent numerical issue due to ill-conditioned system, we propose an algorithm based on Uzawa's method. That way, we are able to solve the dual problem, setting the desired state as an extra-coordinate in the PGD framework. In a single computation, we get both the temperature field and the required heat flux to reach a parametric optimal temperature on a given zone.

Automatic 3D power line reconstruction of multi-angular imaging power line inspection system

NASA Astrophysics Data System (ADS)

Zhang, Wuming; Yan, Guangjian; Wang, Ning; Li, Qiaozhi; Zhao, Wei

2007-06-01

We develop a multi-angular imaging power line inspection system. Its main objective is to monitor the relative distance between high voltage power line and around objects, and alert if the warning threshold is exceeded. Our multi-angular imaging power line inspection system generates DSM of the power line passage, which comprises ground surface and ground objects, for example trees and houses, etc. For the purpose of revealing the dangerous regions, where ground objects are too close to the power line, 3D power line information should be extracted at the same time. In order to improve the automation level of extraction, reduce labour costs and human errors, an automatic 3D power line reconstruction method is proposed and implemented. It can be achieved by using epipolar constraint and prior knowledge of pole tower's height. After that, the proper 3D power line information can be obtained by space intersection using found homologous projections. The flight experiment result shows that the proposed method can successfully reconstruct 3D power line, and the measurement accuracy of the relative distance satisfies the user requirement of 0.5m.

Thermal-economic optimisation of a CHP gas turbine system by applying a fit-problem genetic algorithm

NASA Astrophysics Data System (ADS)

Ferreira, Ana C. M.; Teixeira, Senhorinha F. C. F.; Silva, Rui G.; Silva, Ângela M.

2018-04-01

Cogeneration allows the optimal use of the primary energy sources and significant reductions in carbon emissions. Its use has great potential for applications in the residential sector. This study aims to develop a methodology for thermal-economic optimisation of small-scale micro-gas turbine for cogeneration purposes, able to fulfil domestic energy needs with a thermal power out of 125 kW. A constrained non-linear optimisation model was built. The objective function is the maximisation of the annual worth from the combined heat and power, representing the balance between the annual incomes and the expenditures subject to physical and economic constraints. A genetic algorithm coded in the java programming language was developed. An optimal micro-gas turbine able to produce 103.5 kW of electrical power with a positive annual profit (i.e. 11,925 €/year) was disclosed. The investment can be recovered in 4 years and 9 months, which is less than half of system lifetime expectancy.

Vehicle trajectory linearisation to enable efficient optimisation of the constant speed racing line

NASA Astrophysics Data System (ADS)

Timings, Julian P.; Cole, David J.

2012-06-01

A driver model is presented capable of optimising the trajectory of a simple dynamic nonlinear vehicle, at constant forward speed, so that progression along a predefined track is maximised as a function of time. In doing so, the model is able to continually operate a vehicle at its lateral-handling limit, maximising vehicle performance. The technique used forms a part of the solution to the motor racing objective of minimising lap time. A new approach of formulating the minimum lap time problem is motivated by the need for a more computationally efficient and robust tool-set for understanding on-the-limit driving behaviour. This has been achieved through set point-dependent linearisation of the vehicle model and coupling the vehicle-track system using an intrinsic coordinate description. Through this, the geometric vehicle trajectory had been linearised relative to the track reference, leading to new path optimisation algorithm which can be formed as a computationally efficient convex quadratic programming problem.

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation.

PubMed

Ławryńczuk, Maciej

2017-03-01

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Extended depth of field integral imaging using multi-focus fusion

NASA Astrophysics Data System (ADS)

Piao, Yongri; Zhang, Miao; Wang, Xiaohui; Li, Peihua

2018-03-01

In this paper, we propose a new method for depth of field extension in integral imaging by realizing the image fusion method on the multi-focus elemental images. In the proposed method, a camera is translated on a 2D grid to take multi-focus elemental images by sweeping the focus plane across the scene. Simply applying an image fusion method on the elemental images holding rich parallax information does not work effectively because registration accuracy of images is the prerequisite for image fusion. To solve this problem an elemental image generalization method is proposed. The aim of this generalization process is to geometrically align the objects in all elemental images so that the correct regions of multi-focus elemental images can be exacted. The all-in focus elemental images are then generated by fusing the generalized elemental images using the block based fusion method. The experimental results demonstrate that the depth of field of synthetic aperture integral imaging system has been extended by realizing the generation method combined with the image fusion on multi-focus elemental images in synthetic aperture integral imaging system.

Achieving optimal SERS through enhanced experimental design

PubMed Central

Fisk, Heidi; Westley, Chloe; Turner, Nicholas J.

2016-01-01

One of the current limitations surrounding surface‐enhanced Raman scattering (SERS) is the perceived lack of reproducibility. SERS is indeed challenging, and for analyte detection, it is vital that the analyte interacts with the metal surface. However, as this is analyte dependent, there is not a single set of SERS conditions that are universal. This means that experimental optimisation for optimum SERS response is vital. Most researchers optimise one factor at a time, where a single parameter is altered first before going onto optimise the next. This is a very inefficient way of searching the experimental landscape. In this review, we explore the use of more powerful multivariate approaches to SERS experimental optimisation based on design of experiments and evolutionary computational methods. We particularly focus on colloidal‐based SERS rather than thin film preparations as a result of their popularity. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd. PMID:27587905

Topology Optimisation of Wideband Coaxial-to-Waveguide Transitions

NASA Astrophysics Data System (ADS)

Hassan, Emadeldeen; Noreland, Daniel; Wadbro, Eddie; Berggren, Martin

2017-03-01

To maximize the matching between a coaxial cable and rectangular waveguides, we present a computational topology optimisation approach that decides for each point in a given domain whether to hold a good conductor or a good dielectric. The conductivity is determined by a gradient-based optimisation method that relies on finite-difference time-domain solutions to the 3D Maxwell’s equations. Unlike previously reported results in the literature for this kind of problems, our design algorithm can efficiently handle tens of thousands of design variables that can allow novel conceptual waveguide designs. We demonstrate the effectiveness of the approach by presenting optimised transitions with reflection coefficients lower than -15 dB over more than a 60% bandwidth, both for right-angle and end-launcher configurations. The performance of the proposed transitions is cross-verified with a commercial software, and one design case is validated experimentally.

Topology Optimisation of Wideband Coaxial-to-Waveguide Transitions.

PubMed

Hassan, Emadeldeen; Noreland, Daniel; Wadbro, Eddie; Berggren, Martin

2017-03-23

To maximize the matching between a coaxial cable and rectangular waveguides, we present a computational topology optimisation approach that decides for each point in a given domain whether to hold a good conductor or a good dielectric. The conductivity is determined by a gradient-based optimisation method that relies on finite-difference time-domain solutions to the 3D Maxwell's equations. Unlike previously reported results in the literature for this kind of problems, our design algorithm can efficiently handle tens of thousands of design variables that can allow novel conceptual waveguide designs. We demonstrate the effectiveness of the approach by presenting optimised transitions with reflection coefficients lower than -15 dB over more than a 60% bandwidth, both for right-angle and end-launcher configurations. The performance of the proposed transitions is cross-verified with a commercial software, and one design case is validated experimentally.

Optimal design and operation of a photovoltaic-electrolyser system using particle swarm optimisation

NASA Astrophysics Data System (ADS)

Sayedin, Farid; Maroufmashat, Azadeh; Roshandel, Ramin; Khavas, Sourena Sattari

2016-07-01

In this study, hydrogen generation is maximised by optimising the size and the operating conditions of an electrolyser (EL) directly connected to a photovoltaic (PV) module at different irradiance. Due to the variations of maximum power points of the PV module during a year and the complexity of the system, a nonlinear approach is considered. A mathematical model has been developed to determine the performance of the PV/EL system. The optimisation methodology presented here is based on the particle swarm optimisation algorithm. By this method, for the given number of PV modules, the optimal sizeand operating condition of a PV/EL system areachieved. The approach can be applied for different sizes of PV systems, various ambient temperatures and different locations with various climaticconditions. The results show that for the given location and the PV system, the energy transfer efficiency of PV/EL system can reach up to 97.83%.

Assessment of grid optimisation measures for the German transmission grid using open source grid data

NASA Astrophysics Data System (ADS)

Böing, F.; Murmann, A.; Pellinger, C.; Bruckmeier, A.; Kern, T.; Mongin, T.

2018-02-01

The expansion of capacities in the German transmission grid is a necessity for further integration of renewable energy sources into the electricity sector. In this paper, the grid optimisation measures ‘Overhead Line Monitoring’, ‘Power-to-Heat’ and ‘Demand Response in the Industry’ are evaluated and compared against conventional grid expansion for the year 2030. Initially, the methodical approach of the simulation model is presented and detailed descriptions of the grid model and the used grid data, which partly originates from open-source platforms, are provided. Further, this paper explains how ‘Curtailment’ and ‘Redispatch’ can be reduced by implementing grid optimisation measures and how the depreciation of economic costs can be determined considering construction costs. The developed simulations show that the conventional grid expansion is more efficient and implies more grid relieving effects than the evaluated grid optimisation measures.

Topology Optimisation of Wideband Coaxial-to-Waveguide Transitions

PubMed Central

Hassan, Emadeldeen; Noreland, Daniel; Wadbro, Eddie; Berggren, Martin

2017-01-01

To maximize the matching between a coaxial cable and rectangular waveguides, we present a computational topology optimisation approach that decides for each point in a given domain whether to hold a good conductor or a good dielectric. The conductivity is determined by a gradient-based optimisation method that relies on finite-difference time-domain solutions to the 3D Maxwell’s equations. Unlike previously reported results in the literature for this kind of problems, our design algorithm can efficiently handle tens of thousands of design variables that can allow novel conceptual waveguide designs. We demonstrate the effectiveness of the approach by presenting optimised transitions with reflection coefficients lower than −15 dB over more than a 60% bandwidth, both for right-angle and end-launcher configurations. The performance of the proposed transitions is cross-verified with a commercial software, and one design case is validated experimentally. PMID:28332585

VLSI Technology for Cognitive Radio

NASA Astrophysics Data System (ADS)

VIJAYALAKSHMI, B.; SIDDAIAH, P.

2017-08-01

One of the most challenging tasks of cognitive radio is the efficiency in the spectrum sensing scheme to overcome the spectrum scarcity problem. The popular and widely used spectrum sensing technique is the energy detection scheme as it is very simple and doesn’t require any previous information related to the signal. We propose one such approach which is an optimised spectrum sensing scheme with reduced filter structure. The optimisation is done in terms of area and power performance of the spectrum. The simulations of the VLSI structure of the optimised flexible spectrum is done using verilog coding by using the XILINX ISE software. Our method produces performance with 13% reduction in area and 66% reduction in power consumption in comparison to the flexible spectrum sensing scheme. All the results are tabulated and comparisons are made. A new scheme for optimised and effective spectrum sensing opens up with our model.

Achieving optimal SERS through enhanced experimental design.

PubMed

Fisk, Heidi; Westley, Chloe; Turner, Nicholas J; Goodacre, Royston

2016-01-01

One of the current limitations surrounding surface-enhanced Raman scattering (SERS) is the perceived lack of reproducibility. SERS is indeed challenging, and for analyte detection, it is vital that the analyte interacts with the metal surface. However, as this is analyte dependent, there is not a single set of SERS conditions that are universal. This means that experimental optimisation for optimum SERS response is vital. Most researchers optimise one factor at a time, where a single parameter is altered first before going onto optimise the next. This is a very inefficient way of searching the experimental landscape. In this review, we explore the use of more powerful multivariate approaches to SERS experimental optimisation based on design of experiments and evolutionary computational methods. We particularly focus on colloidal-based SERS rather than thin film preparations as a result of their popularity. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.

Optical cryptography with biometrics for multi-depth objects.

PubMed

Yan, Aimin; Wei, Yang; Hu, Zhijuan; Zhang, Jingtao; Tsang, Peter Wai Ming; Poon, Ting-Chung

2017-10-11

We propose an optical cryptosystem for encrypting images of multi-depth objects based on the combination of optical heterodyne technique and fingerprint keys. Optical heterodyning requires two optical beams to be mixed. For encryption, each optical beam is modulated by an optical mask containing either the fingerprint of the person who is sending, or receiving the image. The pair of optical masks are taken as the encryption keys. Subsequently, the two beams are used to scan over a multi-depth 3-D object to obtain an encrypted hologram. During the decryption process, each sectional image of the 3-D object is recovered by convolving its encrypted hologram (through numerical computation) with the encrypted hologram of a pinhole image that is positioned at the same depth as the sectional image. Our proposed method has three major advantages. First, the lost-key situation can be avoided with the use of fingerprints as the encryption keys. Second, the method can be applied to encrypt 3-D images for subsequent decrypted sectional images. Third, since optical heterodyning scanning is employed to encrypt a 3-D object, the optical system is incoherent, resulting in negligible amount of speckle noise upon decryption. To the best of our knowledge, this is the first time optical cryptography of 3-D object images has been demonstrated in an incoherent optical system with biometric keys.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

PubMed

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

2017-01-01

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

Behavior analysis of video object in complicated background

NASA Astrophysics Data System (ADS)

Zhao, Wenting; Wang, Shigang; Liang, Chao; Wu, Wei; Lu, Yang

2016-10-01

This paper aims to achieve robust behavior recognition of video object in complicated background. Features of the video object are described and modeled according to the depth information of three-dimensional video. Multi-dimensional eigen vector are constructed and used to process high-dimensional data. Stable object tracing in complex scenes can be achieved with multi-feature based behavior analysis, so as to obtain the motion trail. Subsequently, effective behavior recognition of video object is obtained according to the decision criteria. What's more, the real-time of algorithms and accuracy of analysis are both improved greatly. The theory and method on the behavior analysis of video object in reality scenes put forward by this project have broad application prospect and important practical significance in the security, terrorism, military and many other fields.

Multi-objective decision-making under uncertainty: Fuzzy logic methods

NASA Technical Reports Server (NTRS)

Hardy, Terry L.

1994-01-01

Selecting the best option among alternatives is often a difficult process. This process becomes even more difficult when the evaluation criteria are vague or qualitative, and when the objectives vary in importance and scope. Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

Determination of volatile monophenols in beer using acetylation and headspace solid-phase microextraction in combination with gas chromatography and mass spectrometry.

PubMed

Sterckx, Femke L; Saison, Daan; Delvaux, Freddy R

2010-08-31

Monophenols are widely spread compounds contributing to the flavour of many foods and beverages. They are most likely present in beer, but so far, little is known about their influence on beer flavour. To quantify these monophenols in beer, we optimised a headspace solid-phase microextraction method coupled to gas chromatography-mass spectrometry. To improve their isolation from the beer matrix and their chromatographic properties, the monophenols were acetylated using acetic anhydride and KHCO(3) as derivatising agent and base catalyst, respectively. Derivatisation conditions were optimised with attention for the pH of the reaction medium. Additionally, different parameters affecting extraction efficiency were optimised, including fibre coating, extraction time and temperature and salt addition. Afterwards, we calibrated and validated the method successfully and applied it for the analysis of monophenols in beer samples. 2010 Elsevier B.V. All rights reserved.

Dwell time-based stabilisation of switched delay systems using free-weighting matrices

NASA Astrophysics Data System (ADS)

Koru, Ahmet Taha; Delibaşı, Akın; Özbay, Hitay

2018-01-01

In this paper, we present a quasi-convex optimisation method to minimise an upper bound of the dwell time for stability of switched delay systems. Piecewise Lyapunov-Krasovskii functionals are introduced and the upper bound for the derivative of Lyapunov functionals is estimated by free-weighting matrices method to investigate non-switching stability of each candidate subsystems. Then, a sufficient condition for the dwell time is derived to guarantee the asymptotic stability of the switched delay system. Once these conditions are represented by a set of linear matrix inequalities , dwell time optimisation problem can be formulated as a standard quasi-convex optimisation problem. Numerical examples are given to illustrate the improvements over previously obtained dwell time bounds. Using the results obtained in the stability case, we present a nonlinear minimisation algorithm to synthesise the dwell time minimiser controllers. The algorithm solves the problem with successive linearisation of nonlinear conditions.

Statistical optimisation of diclofenac sustained release pellets coated with polymethacrylic films.

PubMed

Kramar, A; Turk, S; Vrecer, F

2003-04-30

The objective of the present study was to evaluate three formulation parameters for the application of polymethacrylic films from aqueous dispersions in order to obtain multiparticulate sustained release of diclofenac sodium. Film coating of pellet cores was performed in a laboratory fluid bed apparatus. The chosen independent variables, i.e. the concentration of plasticizer (triethyl citrate), methacrylate polymers ratio (Eudragit RS:Eudragit RL) and the quantity of coating dispersion were optimised with a three-factor, three-level Box-Behnken design. The chosen dependent variables were cumulative percentage values of diclofenac dissolved in 3, 4 and 6 h. Based on the experimental design, different diclofenac release profiles were obtained. Response surface plots were used to relate the dependent and the independent variables. The optimisation procedure generated an optimum of 40% release in 3 h. The levels of plasticizer concentration, quantity of coating dispersion and polymer to polymer ratio (Eudragit RS:Eudragit RL) were 25% w/w, 400 g and 3/1, respectively. The optimised formulation prepared according to computer-determined levels provided a release profile, which was close to the predicted values. We also studied thermal and surface characteristics of the polymethacrylic films to understand the influence of plasticizer concentration on the drug release from the pellets.

Multi-Temporal Multi-Sensor Analysis of Urbanization and Environmental/Climate Impact in China for Sustainable Urban Development

NASA Astrophysics Data System (ADS)

Ban, Yifang; Gong, Peng; Gamba, Paolo; Taubenbock, Hannes; Du, Peijun

2016-08-01

The overall objective of this research is to investigate multi-temporal, multi-scale, multi-sensor satellite data for analysis of urbanization and environmental/climate impact in China to support sustainable planning. Multi- temporal multi-scale SAR and optical data have been evaluated for urban information extraction using innovative methods and algorithms, including KTH- Pavia Urban Extractor, Pavia UEXT, and an "exclusion- inclusion" framework for urban extent extraction, and KTH-SEG, a novel object-based classification method for detailed urban land cover mapping. Various pixel- based and object-based change detection algorithms were also developed to extract urban changes. Several Chinese cities including Beijing, Shanghai and Guangzhou are selected as study areas. Spatio-temporal urbanization patterns and environmental impact at regional, metropolitan and city core were evaluated through ecosystem service, landscape metrics, spatial indices, and/or their combinations. The relationship between land surface temperature and land-cover classes was also analyzed.The urban extraction results showed that urban areas and small towns could be well extracted using multitemporal SAR data with the KTH-Pavia Urban Extractor and UEXT. The fusion of SAR data at multiple scales from multiple sensors was proven to improve urban extraction. For urban land cover mapping, the results show that the fusion of multitemporal SAR and optical data could produce detailed land cover maps with improved accuracy than that of SAR or optical data alone. Pixel-based and object-based change detection algorithms developed with the project were effective to extract urban changes. Comparing the urban land cover results from mulitemporal multisensor data, the environmental impact analysis indicates major losses for food supply, noise reduction, runoff mitigation, waste treatment and global climate regulation services through landscape structural changes in terms of decreases in service area, edge contamination and fragmentation. In terms ofclimate impact, the results indicate that land surface temperature can be related to land use/land cover classes.

Improved packing of protein side chains with parallel ant colonies

PubMed Central

2014-01-01

Introduction The accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains. Methods We present a side-chain modelling method, pacoPacker, which uses a parallel ant colony optimisation strategy based on sharing a single pheromone matrix. This parallel approach combines different sources of energy functions and generates protein side-chain conformations with the lowest energies jointly determined by the various energy functions. We further optimised the selected rotamers to construct subrotamer by rotamer minimisation, which reasonably improved the discreteness of the rotamer library. Results We focused on improving the accuracy of side-chain conformation prediction. For a testing set of 442 proteins, 87.19% of X1 and 77.11% of X12 angles were predicted correctly within 40° of the X-ray positions. We compared the accuracy of pacoPacker with state-of-the-art methods, such as CIS-RR and SCWRL4. We analysed the results from different perspectives, in terms of protein chain and individual residues. In this comprehensive benchmark testing, 51.5% of proteins within a length of 400 amino acids predicted by pacoPacker were superior to the results of CIS-RR and SCWRL4 simultaneously. Finally, we also showed the advantage of using the subrotamers strategy. All results confirmed that our parallel approach is competitive to state-of-the-art solutions for packing side chains. Conclusions This parallel approach combines various sources of searching intelligence and energy functions to pack protein side chains. It provides a frame-work for combining different inaccuracy/usefulness objective functions by designing parallel heuristic search algorithms. PMID:25474164

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

PubMed

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

NASA Astrophysics Data System (ADS)

Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

2002-06-01

This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

PubMed Central

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality. PMID:26954783

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

Where to Settle—Settlement Preferences of Mytilus galloprovincialis and Choice of Habitat at a Micro Spatial Scale

PubMed Central

Carl, Christina; Poole, Andrew J.; Williams, Mike R.; de Nys, Rocky

2012-01-01

The global mussel aquaculture industry uses specialised spat catching and nursery culture ropes made of multi-filament synthetic and natural fibres to optimise settlement and retention of mussels for on-growing. However, the settlement ecology and preferences of mussels are poorly understood and only sparse information exists in a commercial context. This study quantified the settlement preferences of pediveligers and plantigrades of Mytilus galloprovincialis on increasingly complex surfaces and settlement locations at a micro spatial scale on and within ropes under commercial hatchery operating conditions using optical microscopy and X-ray micro-computed tomography (µCT). M. galloprovincialis has clear settlement preferences for more complex materials and high selectivity for settlement sites from the pediveliger through to the plantigrade stage. Pediveligers of M. galloprovincialis initially settle inside specialised culture ropes. Larger pediveligers were located close to the exterior of ropes as they increased in size over time. In contrast, smaller individuals were located deeper inside of the ropes over time. This study demonstrates that X-ray µCT is an excellent non-destructive technique for mapping settlement and attachment sites of individuals as early as one day post settlement, and quantifies the number and location of settled individuals on and within ropes as a tool to understand and optimise settlement in complex multi-dimensional materials and environments. PMID:23251710

Highlights from the 17th International Conference on Multi-Criteria Decision Making, Whistler, BC, August 6-11, 2004

DTIC Science & Technology

2005-04-01

related to one of the following areas: 1. Group Decision Support Methods; 2. Decision Support Methods; 3. AHP applications; 4. Multi...Objective Linear Programming (MOLP) algorithms; 5. Industrial engineering applications; 6. Behavioural considerations, and 7. Fuzzy MCDM. 3...making. This is especially important when using software like AHP or when constructing questionnaires for SME’s ( see [10] for many examples

Development and Applications of Technology for Sensing Zooplankton

DTIC Science & Technology

2003-09-30

zooplankton-like particles. WORK COMPLETED In support of our first objective, in prior years we occupied sites in both East and West Sound at Orcas ...Island in northern Puget Sound , WA. We have also made deployments at four sites on open linear coasts, including one just north of Oceanside, CA (Red...layers. Multi-static, multi-frequency methods Most active bioacoustical methods in oceanography exclusively utilize the sound that is scattered

Deterministic methods for multi-control fuel loading optimization

NASA Astrophysics Data System (ADS)

Rahman, Fariz B. Abdul

We have developed a multi-control fuel loading optimization code for pressurized water reactors based on deterministic methods. The objective is to flatten the fuel burnup profile, which maximizes overall energy production. The optimal control problem is formulated using the method of Lagrange multipliers and the direct adjoining approach for treatment of the inequality power peaking constraint. The optimality conditions are derived for a multi-dimensional multi-group optimal control problem via calculus of variations. Due to the Hamiltonian having a linear control, our optimal control problem is solved using the gradient method to minimize the Hamiltonian and a Newton step formulation to obtain the optimal control. We are able to satisfy the power peaking constraint during depletion with the control at beginning of cycle (BOC) by building the proper burnup path forward in time and utilizing the adjoint burnup to propagate the information back to the BOC. Our test results show that we are able to achieve our objective and satisfy the power peaking constraint during depletion using either the fissile enrichment or burnable poison as the control. Our fuel loading designs show an increase of 7.8 equivalent full power days (EFPDs) in cycle length compared with 517.4 EFPDs for the AP600 first cycle.

Organisation of services for managing ADHD.

PubMed

Coghill, D R

2017-10-01

There is considerable variation in practice, both between and with different countries in the management of attention deficit hyperactivity disorder (ADHD). Whilst there is no one optimal model of service organisation there are general principles of care that can be introduced to reduce this variability. There are frequent debates and discussions about which professional group is best placed to manage ADHD at different points in the life cycle. Who delivers care is however less important than ensuring that training schemes provide adequate exposure, training and experience to both the core and non-core skills required to provide a comprehensive package of care. Most evidence-based guidelines recommend a multi-modal, multi-professional and multi-agency approach. Many also promote the use of both stepped care and shared care approaches for the management of ADHD. As most of those with ADHD continue to have ADHD-related problems into adulthood, it is important to consider how best to transition care into adulthood and think about who should deliver care to adults with ADHD. Young people with ADHD should generally be transferred to adult mental health services if they continue to have significant symptoms of ADHD or other coexisting conditions that require treatment. Unfortunately services for adults with ADHD remain relatively scarce across much of the world and some adult psychiatrists remain unsure of the diagnosis and uncertain about the appropriate use of ADHD medications in adults, but there is a strong case for increased services for adults. ADHD is on the one hand easy to treat; it is much more difficult to treat well. Although optimised care for ADHD requires routine measurement of outcomes, this often does not happen in routine clinical practice. Focusing on optimising symptoms and minimising adverse effects can significantly improve both short- and long-term outcomes.

Time-resolved computed tomography of the liver: retrospective, multi-phase image reconstruction derived from volumetric perfusion imaging.

PubMed

Fischer, Michael A; Leidner, Bertil; Kartalis, Nikolaos; Svensson, Anders; Aspelin, Peter; Albiin, Nils; Brismar, Torkel B

2014-01-01

To assess feasibility and image quality (IQ) of a new post-processing algorithm for retrospective extraction of an optimised multi-phase CT (time-resolved CT) of the liver from volumetric perfusion imaging. Sixteen patients underwent clinically indicated perfusion CT using 4D spiral mode of dual-source 128-slice CT. Three image sets were reconstructed: motion-corrected and noise-reduced (MCNR) images derived from 4D raw data; maximum and average intensity projections (time MIP/AVG) of the arterial/portal/portal-venous phases and all phases (total MIP/ AVG) derived from retrospective fusion of dedicated MCNR split series. Two readers assessed the IQ, detection rate and evaluation time; one reader assessed image noise and lesion-to-liver contrast. Time-resolved CT was feasible in all patients. Each post-processing step yielded a significant reduction of image noise and evaluation time, maintaining lesion-to-liver contrast. Time MIPs/AVGs showed the highest overall IQ without relevant motion artefacts and best depiction of arterial and portal/portal-venous phases respectively. Time MIPs demonstrated a significantly higher detection rate for arterialised liver lesions than total MIPs/AVGs and the raw data series. Time-resolved CT allows data from volumetric perfusion imaging to be condensed into an optimised multi-phase liver CT, yielding a superior IQ and higher detection rate for arterialised liver lesions than the raw data series. • Four-dimensional computed tomography is limited by motion artefacts and poor image quality. • Time-resolved-CT facilitates 4D-CT data visualisation, segmentation and analysis by condensing raw data. • Time-resolved CT demonstrates better image quality than raw data images. • Time-resolved CT improves detection of arterialised liver lesions in cirrhotic patients.