[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

Optimal placement of unified power flow controllers to improve dynamic voltage stability using power system variable based voltage stability indices.

PubMed

Albatsh, Fadi M; Ahmad, Shameem; Mekhilef, Saad; Mokhlis, Hazlie; Hassan, M A

2015-01-01

This study examines a new approach to selecting the locations of unified power flow controllers (UPFCs) in power system networks based on a dynamic analysis of voltage stability. Power system voltage stability indices (VSIs) including the line stability index (LQP), the voltage collapse proximity indicator (VCPI), and the line stability index (Lmn) are employed to identify the most suitable locations in the system for UPFCs. In this study, the locations of the UPFCs are identified by dynamically varying the loads across all of the load buses to represent actual power system conditions. Simulations were conducted in a power system computer-aided design (PSCAD) software using the IEEE 14-bus and 39- bus benchmark power system models. The simulation results demonstrate the effectiveness of the proposed method. When the UPFCs are placed in the locations obtained with the new approach, the voltage stability improves. A comparison of the steady-state VSIs resulting from the UPFCs placed in the locations obtained with the new approach and with particle swarm optimization (PSO) and differential evolution (DE), which are static methods, is presented. In all cases, the UPFC locations given by the proposed approach result in better voltage stability than those obtained with the other approaches.

Optimal Placement of Unified Power Flow Controllers to Improve Dynamic Voltage Stability Using Power System Variable Based Voltage Stability Indices

PubMed Central

Albatsh, Fadi M.; Ahmad, Shameem; Mekhilef, Saad; Mokhlis, Hazlie; Hassan, M. A.

2015-01-01

This study examines a new approach to selecting the locations of unified power flow controllers (UPFCs) in power system networks based on a dynamic analysis of voltage stability. Power system voltage stability indices (VSIs) including the line stability index (LQP), the voltage collapse proximity indicator (VCPI), and the line stability index (Lmn) are employed to identify the most suitable locations in the system for UPFCs. In this study, the locations of the UPFCs are identified by dynamically varying the loads across all of the load buses to represent actual power system conditions. Simulations were conducted in a power system computer-aided design (PSCAD) software using the IEEE 14-bus and 39- bus benchmark power system models. The simulation results demonstrate the effectiveness of the proposed method. When the UPFCs are placed in the locations obtained with the new approach, the voltage stability improves. A comparison of the steady-state VSIs resulting from the UPFCs placed in the locations obtained with the new approach and with particle swarm optimization (PSO) and differential evolution (DE), which are static methods, is presented. In all cases, the UPFC locations given by the proposed approach result in better voltage stability than those obtained with the other approaches. PMID:25874560

Facial Emotion Recognition System – A Machine Learning Approach

NASA Astrophysics Data System (ADS)

Ramalingam, V. V.; Pandian, A.; Jayakumar, Lavanya

2018-04-01

Frown is a medium for people correlation and it could be exercised in multiple real systems. Single crucial stage for frown realizing is to exactly select hysterical aspects. This journal proposed a frown realization scheme applying transformative Particle Swarm Optimization (PSO) based aspect accumulation. This entity initially employs changed LVP, handles crisscross adjacent picture element contrast, for achieving the selective first frown portrayal. Then the PSO entity inserted with a concept of micro Genetic Algorithm (mGA) called mGA-embedded PSO designed for achieving aspect accumulation. This study, the technique subsumes no disposable memory, a little-populace insignificant flock, a latest acceleration that amends with the approach and a sub dimension-based in-depth local frown aspect examines. Assistance of provincial utilization and comprehensive inspection examine structure of alleviating of an immature concurrence complication of conventional PSO. Numerous identifiers are used to diagnose different frown expositions. Stationed on extensive study within and other-sphere pictures from the continued Cohn Kanade and MMI benchmark directory appropriately. Determination of the application exceeds most advanced level PSO variants, conventional PSO, classical GA and alternate relevant frown realization structures is described with powerful limit. Extending our accession to a motion based FER application for connecting patch-based Gabor aspects with continuous data in multi-frames.

A Review of Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Jain, N. K.; Nangia, Uma; Jain, Jyoti

2018-03-01

This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995-2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.

A new logistic dynamic particle swarm optimization algorithm based on random topology.

PubMed

Ni, Qingjian; Deng, Jianming

2013-01-01

Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.

Fuzzy PID control algorithm based on PSO and application in BLDC motor

NASA Astrophysics Data System (ADS)

Lin, Sen; Wang, Guanglong

2017-06-01

A fuzzy PID control algorithm is studied based on improved particle swarm optimization (PSO) to perform Brushless DC (BLDC) motor control which has high accuracy, good anti-jamming capability and steady state accuracy compared with traditional PID control. The mathematical and simulation model is established for BLDC motor by simulink software, and the speed loop of the fuzzy PID controller is designed. The simulation results show that the fuzzy PID control algorithm based on PSO has higher stability, high control precision and faster dynamic response speed.

PSO-based PID Speed Control of Traveling Wave Ultrasonic Motor under Temperature Disturbance

NASA Astrophysics Data System (ADS)

Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Azmi, Nur Iffah Mohamed; Romlay, Fadhlur Rahman Mohd

2018-03-01

Traveling wave ultrasonic motors (TWUSMs) have a time varying dynamics characteristics. Temperature rise in TWUSMs remains a problem particularly in sustaining optimum speed performance. In this study, a PID controller is used to control the speed of TWUSM under temperature disturbance. Prior to developing the controller, a linear approximation model which relates the speed to the temperature is developed based on the experimental data. Two tuning methods are used to determine PID parameters: conventional Ziegler-Nichols(ZN) and particle swarm optimization (PSO). The comparison of speed control performance between PSO-PID and ZN-PID is presented. Modelling, simulation and experimental work is carried out utilizing Fukoku-Shinsei USR60 as the chosen TWUSM. The results of the analyses and experimental work reveal that PID tuning using PSO-based optimization has the advantage over the conventional Ziegler-Nichols method.

Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

PubMed

Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

2014-01-01

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

Particle Swarm Optimization with Double Learning Patterns.

PubMed

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

PubMed Central

Zhang, Le; Wu, Jinnan

2014-01-01

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature. PMID:24672389

A PSO-based hybrid metaheuristic for permutation flowshop scheduling problems.

PubMed

Zhang, Le; Wu, Jinnan

2014-01-01

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.

Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

PubMed Central

Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi

2014-01-01

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

The Interprofessional Psychosocial Oncology Distance Education (IPODE) project: perceived outcomes of an approach to healthcare professional education.

PubMed

McLeod, Deborah; Curran, Janet; Dumont, Serge; White, Maureen; Charles, Grant

2014-05-01

The Interprofessional Psychosocial Oncology Distance Education (IPODE) project was designed as an approach to the problems of feasibility and accessibility in specialty health professional education, in this case, psychosocial oncology (PSO). In this article, we report the evaluation findings from the first three years of the project in relation to one IPODE course, which was offered as a graduate level university elective in nine Canadian universities and as a continuing education (CE) option to health professionals between January 2008 and May 2010. The evaluation included a pre and post questionnaire that explored how an interprofessional (IP), web-based, PSO course influenced participants' knowledge, attitudes and beliefs about IP, person-centered PSO care. It also examined what attributes of a web-based platform were most effective in delivering an IP PSO course. The study yielded two key findings. First, web-based learning in a pan-Canadian and cross-university collaboration is a viable alternative to providing specialty education and significantly improves knowledge, attitudes and beliefs about IP, person-centered PSO care. Second, a web-based platform with real-time seminars, discussion boards and multiple audio visual resources that privilege first person illness narratives were important elements in expanding knowledge and shifting attitudes about IP practice and person-centered care in regards to PSO. In their evaluation, course participants highlighted a variety of ways in which the course expanded their vision about what constitutes an IP team and increased their confidence in interacting with healthcare professionals from professions other than their own.

Particle Swarm Optimization with Double Learning Patterns

PubMed Central

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747

Particle Swarm Optimization algorithms for geophysical inversion, practical hints

NASA Astrophysics Data System (ADS)

Garcia Gonzalo, E.; Fernandez Martinez, J.; Fernandez Alvarez, J.; Kuzma, H.; Menendez Perez, C.

2008-12-01

PSO is a stochastic optimization technique that has been successfully used in many different engineering fields. PSO algorithm can be physically interpreted as a stochastic damped mass-spring system (Fernandez Martinez and Garcia Gonzalo 2008). Based on this analogy we present a whole family of PSO algorithms and their respective first order and second order stability regions. Their performance is also checked using synthetic functions (Rosenbrock and Griewank) showing a degree of ill-posedness similar to that found in many geophysical inverse problems. Finally, we present the application of these algorithms to the analysis of a Vertical Electrical Sounding inverse problem associated to a seawater intrusion in a coastal aquifer in South Spain. We analyze the role of PSO parameters (inertia, local and global accelerations and discretization step), both in convergence curves and in the a posteriori sampling of the depth of an intrusion. Comparison is made with binary genetic algorithms and simulated annealing. As result of this analysis, practical hints are given to select the correct algorithm and to tune the corresponding PSO parameters. Fernandez Martinez, J.L., Garcia Gonzalo, E., 2008a. The generalized PSO: a new door to PSO evolution. Journal of Artificial Evolution and Applications. DOI:10.1155/2008/861275.

PSO Algorithm for an Optimal Power Controller in a Microgrid

NASA Astrophysics Data System (ADS)

Al-Saedi, W.; Lachowicz, S.; Habibi, D.; Bass, O.

2017-07-01

This paper presents the Particle Swarm Optimization (PSO) algorithm to improve the quality of the power supply in a microgrid. This algorithm is proposed for a real-time selftuning method that used in a power controller for an inverter based Distributed Generation (DG) unit. In such system, the voltage and frequency are the main control objectives, particularly when the microgrid is islanded or during load change. In this work, the PSO algorithm is implemented to find the optimal controller parameters to satisfy the control objectives. The results show high performance of the applied PSO algorithm of regulating the microgrid voltage and frequency.

A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models

PubMed Central

Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung

2015-01-01

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237

Research of converter transformer fault diagnosis based on improved PSO-BP algorithm

NASA Astrophysics Data System (ADS)

Long, Qi; Guo, Shuyong; Li, Qing; Sun, Yong; Li, Yi; Fan, Youping

2017-09-01

To overcome those disadvantages that BP (Back Propagation) neural network and conventional Particle Swarm Optimization (PSO) converge at the global best particle repeatedly in early stage and is easy trapped in local optima and with low diagnosis accuracy when being applied in converter transformer fault diagnosis, we come up with the improved PSO-BP neural network to improve the accuracy rate. This algorithm improves the inertia weight Equation by using the attenuation strategy based on concave function to avoid the premature convergence of PSO algorithm and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. At last the simulation results prove that the proposed approach has a better ability in optimizing BP neural network in terms of network output error, global searching performance and diagnosis accuracy.

Composite Particle Swarm Optimizer With Historical Memory for Function Optimization.

PubMed

Li, Jie; Zhang, JunQi; Jiang, ChangJun; Zhou, MengChu

2015-10-01

Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms.

SU-E-T-500: Initial Implementation of GPU-Based Particle Swarm Optimization for 4D IMRT Planning in Lung SBRT

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

Modiri, A; Hagan, A; Gu, X

Purpose 4D-IMRT planning, combined with dynamic MLC tracking delivery, utilizes the temporal dimension as an additional degree of freedom to achieve improved OAR-sparing. The computational complexity for such optimization increases exponentially with increase in dimensionality. In order to accomplish this task in a clinically-feasible time frame, we present an initial implementation of GPU-based 4D-IMRT planning based on particle swarm optimization (PSO). Methods The target and normal structures were manually contoured on ten phases of a 4DCT scan of a NSCLC patient with a 54cm3 right-lower-lobe tumor (1.5cm motion). Corresponding ten 3D-IMRT plans were created in the Eclipse treatment planning systemmore » (Ver-13.6). A vendor-provided scripting interface was used to export 3D-dose matrices corresponding to each control point (10 phases × 9 beams × 166 control points = 14,940), which served as input to PSO. The optimization task was to iteratively adjust the weights of each control point and scale the corresponding dose matrices. In order to handle the large amount of data in GPU memory, dose matrices were sparsified and placed in contiguous memory blocks with the 14,940 weight-variables. PSO was implemented on CPU (dual-Xeon, 3.1GHz) and GPU (dual-K20 Tesla, 2496 cores, 3.52Tflops, each) platforms. NiftyReg, an open-source deformable image registration package, was used to calculate the summed dose. Results The 4D-PSO plan yielded PTV coverage comparable to the clinical ITV-based plan and significantly higher OAR-sparing, as follows: lung Dmean=33%; lung V20=27%; spinal cord Dmax=26%; esophagus Dmax=42%; heart Dmax=0%; heart Dmean=47%. The GPU-PSO processing time for 14940 variables and 7 PSO-particles was 41% that of CPU-PSO (199 vs. 488 minutes). Conclusion Truly 4D-IMRT planning can yield significant OAR dose-sparing while preserving PTV coverage. The corresponding optimization problem is large-scale, non-convex and computationally rigorous. Our initial results indicate that GPU-based PSO with further software optimization can make such planning clinically feasible. This work was supported through funding from the National Institutes of Health and Varian Medical Systems.« less

Handwritten digits recognition using HMM and PSO based on storks

NASA Astrophysics Data System (ADS)

Yan, Liao; Jia, Zhenhong; Yang, Jie; Pang, Shaoning

2010-07-01

A new method for handwritten digits recognition based on hidden markov model (HMM) and particle swarm optimization (PSO) is proposed. This method defined 24 strokes with the sense of directional, to make up for the shortage that is sensitive in choice of stating point in traditional methods, but also reduce the ambiguity caused by shakes. Make use of excellent global convergence of PSO; improving the probability of finding the optimum and avoiding local infinitesimal obviously. Experimental results demonstrate that compared with the traditional methods, the proposed method can make most of the recognition rate of handwritten digits improved.

[The practice of special observation in adults in the German-speaking part of Switzerland - a descriptive cross-sectional study].

PubMed

Lienhardt, Andrea; Rabenschlag, Franziska; Panfil, Eva-Maria

2018-06-08

The practice of special observation in adults in the German-speaking part of Switzerland - a descriptive cross-sectional study Abstract. Psychiatric Special Observation (PSO) is an intervention often used by nurses to prevent service users of harming themselves or to protect others. The intervention ranges between control and therapy and is resource intensive. Despite the widespread use of PSO, there is still no data on the practice of the intervention in Switzerland. What is the current practice of PSO in adults in psychiatric hospitals in the German-speaking part of Switzerland? Descriptive cross-sectional study. Nurses from inpatient psychiatric services in the German-speaking part of Switzerland completed a questionnaire based on a concept analysis of PSO. 538 questionnaires were analysed. PSO was more often conducted intermittent than as constant observation. In more than one out of four cases, suicidality reasoned as a cause for prescription. Nurses generally used standardized instruments to assess the risk of harming oneself or others. The duration of PSO lasted eight hours or more in three out of four cases. In every fifth case, there was no validation of the need of the intervention taking place during one shift. Nurses have a neutral attitude towards the intervention and are experiencing no or weak negative feelings during performance of PSO. The results suggest that there is an inconsistent performance of PSO in Switzerland as well as in other countries. The validation of the need of the intervention is insufficient. To facilitate PSO as a justified performance, the preparation of an interprofessional guideline is recommended.

Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

NASA Astrophysics Data System (ADS)

Guo, Weian; Li, Wuzhao; Zhang, Qun; Wang, Lei; Wu, Qidi; Ren, Hongliang

2014-11-01

In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.

Spatial-Temporal Reasoning Applications of Computational Intelligence in the Game of Go and Computer Networks

DTIC Science & Technology

2012-01-01

dimensionality, Tesauro used a backpropagation- based , three-layer neural network and implemented the outcome from a self-play game as the reinforcement signal...a school of fish, flock of birds, and colony of ants. Our literature review reveals that no one has used PSO to train the neural network ...trained with a variant of PSO called cellular PSO (CPSO). CSRN is a supervised learning neural network (SLNN). The proposed algorithm for the

Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

NASA Astrophysics Data System (ADS)

Zheng, Qin; Yang, Zubin; Sha, Jianxin; Yan, Jun

2017-02-01

In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the one by ADJ-CNOP with the forecast time increasing.

Synthesis of soybean oil-based polymeric surfactants in supercritical carbon dioxide and investigation of their surface properties

USDA-ARS?s Scientific Manuscript database

This paper reports the preparation of polymeric surfactants (HPSO) via a two-step synthetic procedure: polymerization of soybean oil (PSO) in supercritical carbon dioxide and followed by hydrolysis of PSO with a base. HPSO was characterized and identified by using a combination of FTIR, 1H NMR, 13C...

Rayleigh wave dispersion curve inversion by using particle swarm optimization and genetic algorithm

NASA Astrophysics Data System (ADS)

Buyuk, Ersin; Zor, Ekrem; Karaman, Abdullah

2017-04-01

Inversion of surface wave dispersion curves with its highly nonlinear nature has some difficulties using traditional linear inverse methods due to the need and strong dependence to the initial model, possibility of trapping in local minima and evaluation of partial derivatives. There are some modern global optimization methods to overcome of these difficulties in surface wave analysis such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). GA is based on biologic evolution consisting reproduction, crossover and mutation operations, while PSO algorithm developed after GA is inspired from the social behaviour of birds or fish of swarms. Utility of these methods require plausible convergence rate, acceptable relative error and optimum computation cost that are important for modelling studies. Even though PSO and GA processes are similar in appearence, the cross-over operation in GA is not used in PSO and the mutation operation is a stochastic process for changing the genes within chromosomes in GA. Unlike GA, the particles in PSO algorithm changes their position with logical velocities according to particle's own experience and swarm's experience. In this study, we applied PSO algorithm to estimate S wave velocities and thicknesses of the layered earth model by using Rayleigh wave dispersion curve and also compared these results with GA and we emphasize on the advantage of using PSO algorithm for geophysical modelling studies considering its rapid convergence, low misfit error and computation cost.

A particle swarm optimization variant with an inner variable learning strategy.

PubMed

Wu, Guohua; Pedrycz, Witold; Ma, Manhao; Qiu, Dishan; Li, Haifeng; Liu, Jin

2014-01-01

Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.

Computational intelligence-based optimization of maximally stable extremal region segmentation for object detection

NASA Astrophysics Data System (ADS)

Davis, Jeremy E.; Bednar, Amy E.; Goodin, Christopher T.; Durst, Phillip J.; Anderson, Derek T.; Bethel, Cindy L.

2017-05-01

Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational intelligence (CI) for search problems where a direct solution can not easily be obtained. One such problem is finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing MSER parameters to detect stop signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several additions to the GA and PSO are required to successfully detect stop signs in simulated images. These additions are a primary focus of this paper and include: the identification of an appropriate fitness function, the creation of a variable mutation operator for the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential velocity decay function to the PSO, the addition of an "execution best" omnipresent particle to the PSO, and the addition of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using various combinations of selection, crossover, and mutation operators and experimentation was also performed with the PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and drawbacks of each algorithm in terms of detection accuracy, execution speed, and additions required to generate successful problem specific parameter sets.

[The research of near-infrared blood glucose measurement using particle swarm optimization and artificial neural network].

PubMed

Dai, Juan; Ji, Zhong; Du, Yubao

2017-08-01

Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

PubMed

Su, Hongsheng

2017-12-18

Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.

A multipopulation PSO based memetic algorithm for permutation flow shop scheduling.

PubMed

Liu, Ruochen; Ma, Chenlin; Ma, Wenping; Li, Yangyang

2013-01-01

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.

PubMed

Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein

2016-01-01

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.

Set-Based Discrete Particle Swarm Optimization Based on Decomposition for Permutation-Based Multiobjective Combinatorial Optimization Problems.

PubMed

Yu, Xue; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Yuan, Huaqiang; Kwong, Sam; Zhang, Jun

2018-07-01

This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.

Machining fixture layout optimization using particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Dou, Jianping; Wang, Xingsong; Wang, Lei

2011-05-01

Optimization of fixture layout (locator and clamp locations) is critical to reduce geometric error of the workpiece during machining process. In this paper, the application of particle swarm optimization (PSO) algorithm is presented to minimize the workpiece deformation in the machining region. A PSO based approach is developed to optimize fixture layout through integrating ANSYS parametric design language (APDL) of finite element analysis to compute the objective function for a given fixture layout. Particle library approach is used to decrease the total computation time. The computational experiment of 2D case shows that the numbers of function evaluations are decreased about 96%. Case study illustrates the effectiveness and efficiency of the PSO based optimization approach.

Packets Distributing Evolutionary Algorithm Based on PSO for Ad Hoc Network

NASA Astrophysics Data System (ADS)

Xu, Xiao-Feng

2018-03-01

Wireless communication network has such features as limited bandwidth, changeful channel and dynamic topology, etc. Ad hoc network has lots of difficulties in accessing control, bandwidth distribution, resource assign and congestion control. Therefore, a wireless packets distributing Evolutionary algorithm based on PSO (DPSO)for Ad Hoc Network is proposed. Firstly, parameters impact on performance of network are analyzed and researched to obtain network performance effective function. Secondly, the improved PSO Evolutionary Algorithm is used to solve the optimization problem from local to global in the process of network packets distributing. The simulation results show that the algorithm can ensure fairness and timeliness of network transmission, as well as improve ad hoc network resource integrated utilization efficiency.

Solving the Container Stowage Problem (CSP) using Particle Swarm Optimization (PSO)

NASA Astrophysics Data System (ADS)

Matsaini; Santosa, Budi

2018-04-01

Container Stowage Problem (CSP) is a problem of containers arrangement into ships by considering rules such as: total weight, weight of one stack, destination, equilibrium, and placement of containers on vessel. Container stowage problem is combinatorial problem and hard to solve with enumeration technique. It is an NP-Hard Problem. Therefore, to find a solution, metaheuristics is preferred. The objective of solving the problem is to minimize the amount of shifting such that the unloading time is minimized. Particle Swarm Optimization (PSO) is proposed to solve the problem. The implementation of PSO is combined with some steps which are stack position change rules, stack changes based on destination, and stack changes based on the weight type of the stacks (light, medium, and heavy). The proposed method was applied on five different cases. The results were compared to Bee Swarm Optimization (BSO) and heuristics method. PSO provided mean of 0.87% gap and time gap of 60 second. While BSO provided mean of 2,98% gap and 459,6 second to the heuristcs.

A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization.

PubMed

Zhang, Yong-Feng; Chiang, Hsiao-Dong

2017-09-01

A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.

Swarm-wavelet based extreme learning machine for finger movement classification on transradial amputees.

PubMed

Anam, Khairul; Al-Jumaily, Adel

2014-01-01

The use of a small number of surface electromyography (EMG) channels on the transradial amputee in a myoelectric controller is a big challenge. This paper proposes a pattern recognition system using an extreme learning machine (ELM) optimized by particle swarm optimization (PSO). PSO is mutated by wavelet function to avoid trapped in a local minima. The proposed system is used to classify eleven imagined finger motions on five amputees by using only two EMG channels. The optimal performance of wavelet-PSO was compared to a grid-search method and standard PSO. The experimental results show that the proposed system is the most accurate classifier among other tested classifiers. It could classify 11 finger motions with the average accuracy of about 94 % across five amputees.

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.

FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds.

PubMed

Abbasi, Elham; Ghatee, Mehdi; Shiri, M E

2013-09-01

In this paper, an intelligent hyper framework is proposed to recognize protein folds from its amino acid sequence which is a fundamental problem in bioinformatics. This framework includes some statistical and intelligent algorithms for proteins classification. The main components of the proposed framework are the Fuzzy Resource-Allocating Network (FRAN) and the Radial Bases Function based on Particle Swarm Optimization (RBF-PSO). FRAN applies a dynamic method to tune up the RBF network parameters. Due to the patterns complexity captured in protein dataset, FRAN classifies the proteins under fuzzy conditions. Also, RBF-PSO applies PSO to tune up the RBF classifier. Experimental results demonstrate that FRAN improves prediction accuracy up to 51% and achieves acceptable multi-class results for protein fold prediction. Although RBF-PSO provides reasonable results for protein fold recognition up to 48%, it is weaker than FRAN in some cases. However the proposed hyper framework provides an opportunity to use a great range of intelligent methods and can learn from previous experiences. Thus it can avoid the weakness of some intelligent methods in terms of memory, computational time and static structure. Furthermore, the performance of this system can be enhanced throughout the system life-cycle. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effects of Different Levels of Pomegranate Seed Oil on Some Blood Parameters and Disease Resistance Against Yersinia ruckeri in Rainbow Trout

PubMed Central

Acar, Ümit; Parrino, Vincenzo; Kesbiç, Osman Sabri; Lo Paro, Giuseppe; Saoca, Concetta; Abbate, Francesco; Yılmaz, Sevdan; Fazio, Francesco

2018-01-01

This study is aimed to assess the effects of pomegranate seed oil (PSO) supplementation on growth performance, some hematological, biochemical and immunological parameters, and disease resistance against Yersinia ruckeri in cultured rainbow trout Oncorhynchus mykiss (Walbaum, 1792). 240 fish in total were randomly assigned into four triplicate groups (20 fish/per aquarium) corresponding to four dietary treatments: control (PSO0; no addition of PSO), 0.5% (PSO5), 1.00% (PSO10), and 2.00% (PSO20) of PSO, respectively. After the 60 day-feeding trial, fish blood samples were collected and compared. Statistical analysis (one-way ANOVA) showed a significant (P < 0.05) effect of PSO on red blood cell count, hemoglobin concentration, mean corpuscular volume, mean corpuscular hemoglobin concentration, cholesterol, aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase parameters in PSO5 and PSO10 with regard to control. Moreover, a pronounced (P < 0.05) increased in weight gain, growth and feed conversion was found in fish fed with PSO supplemented diets. After the feeding trial, fish were challenged with Y. ruckeri and survival recorded for 20 days. Cumulative survival was 45.10% in fish fed with the control diet, whereas in fish fed with PSO5, PSO10, and PSO20 supplemented diets, survival was 58.82, 56.86, and 56.86%, respectively. In conclusion, dietary administration of PSO induced a reduction in mortality of rainbow trout infected with Y. ruckeri, intercalary significant differences occurred on growth performance and some blood values among treated groups. These positive effects of PSO could be considered for new applications in aquaculture. PMID:29875694

Risk of Incident Liver Disease in Patients with Psoriasis, Psoriatic Arthritis, and Rheumatoid Arthritis: A Population-Based Study.

PubMed

Ogdie, Alexis; Grewal, Sungat K; Noe, Megan H; Shin, Daniel B; Takeshita, Junko; Chiesa Fuxench, Zelma C; Carr, Rotonya M; Gelfand, Joel M

2018-04-01

Relatively little is known about the risk for incident liver disease in psoriasis (PsO), psoriatic arthritis (PsA), and rheumatoid arthritis (RA). We performed a cohort study among patients with PsO, PsA, or RA and matched controls in The Health Improvement Network from 1994 to 2014. Outcomes of interest were any liver disease, nonalcoholic fatty liver disease, and cirrhosis (any etiology). Among patients with PsO (N = 197,130), PsA (N = 12,308), RA (N = 54,251), and matched controls (N = 1,279,754), the adjusted hazard ratios for any liver disease were elevated among patients with PsO (without systemic therapy [ST] 1.37; with ST 1.97), PsA (without ST 1.38; with ST 1.67), and RA without an ST (1.49) but not elevated in patients with RA prescribed an ST (0.96). Incident nonalcoholic fatty liver disease was highest in patients with PsO prescribed an ST (2.23) and PsA with an ST (2.11). The risk of cirrhosis was highest among patients with PsO with an ST (2.62) and PsA without an ST (3.15). Additionally, the prevalence of liver disease and cirrhosis increased in a stepwise fashion with increasing body surface area affected by PsO (P for trend <0.001). More so than RA, PsO and PsA are associated with liver disease, particularly nonalcoholic fatty liver disease and cirrhosis, and this was true even among patients without ST exposure. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Security Analysis of Image Encryption Based on Gyrator Transform by Searching the Rotation Angle with Improved PSO Algorithm.

PubMed

Sang, Jun; Zhao, Jun; Xiang, Zhili; Cai, Bin; Xiang, Hong

2015-08-05

Gyrator transform has been widely used for image encryption recently. For gyrator transform-based image encryption, the rotation angle used in the gyrator transform is one of the secret keys. In this paper, by analyzing the properties of the gyrator transform, an improved particle swarm optimization (PSO) algorithm was proposed to search the rotation angle in a single gyrator transform. Since the gyrator transform is continuous, it is time-consuming to exhaustedly search the rotation angle, even considering the data precision in a computer. Therefore, a computational intelligence-based search may be an alternative choice. Considering the properties of severe local convergence and obvious global fluctuations of the gyrator transform, an improved PSO algorithm was proposed to be suitable for such situations. The experimental results demonstrated that the proposed improved PSO algorithm can significantly improve the efficiency of searching the rotation angle in a single gyrator transform. Since gyrator transform is the foundation of image encryption in gyrator transform domains, the research on the method of searching the rotation angle in a single gyrator transform is useful for further study on the security of such image encryption algorithms.

A Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling

PubMed Central

Liu, Ruochen; Ma, Chenlin; Ma, Wenping; Li, Yangyang

2013-01-01

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP. PMID:24453841

Canonical PSO Based K-Means Clustering Approach for Real Datasets.

PubMed

Dey, Lopamudra; Chakraborty, Sanjay

2014-01-01

"Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

Canonical PSO Based K-Means Clustering Approach for Real Datasets

PubMed Central

Dey, Lopamudra; Chakraborty, Sanjay

2014-01-01

“Clustering” the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms. PMID:27355083

Surface and interfacial properties of soy-based polysoaps

USDA-ARS?s Scientific Manuscript database

Soybean oil (SO) was polymerized by the reaction of its double bonds in the presence of a catalyst. The resulting polymer (PSO) was positively identified using a combination of FTIR, 1H NMR, 13C NMR, and GPC methods. PSO was hydrolyzed into polysoaps with Na+, K+ or TEA+ (triethanol amine) counter i...

A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

NASA Technical Reports Server (NTRS)

Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw

2005-01-01

A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network

PubMed Central

López-Caraballo, C. H.; Lazzús, J. A.; Salfate, I.; Rojas, P.; Rivera, M.; Palma-Chilla, L.

2015-01-01

An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σ N) from 0.01 to 0.1. PMID:26351449

Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network.

PubMed

López-Caraballo, C H; Lazzús, J A; Salfate, I; Rojas, P; Rivera, M; Palma-Chilla, L

2015-01-01

An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σ(N)) from 0.01 to 0.1.

Convergence analysis of particle swarm optimization (PSO) method on the with-in host dengue infection treatment model

NASA Astrophysics Data System (ADS)

Handayani, D.; Nuraini, N.; Tse, O.; Saragih, R.; Naiborhu, J.

2016-04-01

PSO is a computational optimization method motivated by the social behavior of organisms like bird flocking, fish schooling and human social relations. PSO is one of the most important swarm intelligence algorithms. In this study, we analyze the convergence of PSO when it is applied to with-in host dengue infection treatment model simulation in our early research. We used PSO method to construct the initial adjoin equation and to solve a control problem. Its properties of control input on the continuity of objective function and ability of adapting to the dynamic environment made us have to analyze the convergence of PSO. With the convergence analysis of PSO we will have some parameters that ensure the convergence result of numerical simulations on this model using PSO.

The influence of crystalline lens accommodation on post-saccadic oscillations in pupil-based eye trackers.

PubMed

Nyström, Marcus; Andersson, Richard; Magnusson, Måns; Pansell, Tony; Hooge, Ignace

2015-02-01

It is well known that the crystalline lens (henceforth lens) can oscillate (or 'wobble') relative to the eyeball at the end of saccades. Recent research has proposed that such wobbling of the lens is a source of post-saccadic oscillations (PSOs) seen in data recorded by eye trackers that estimate gaze direction from the location of the pupil. Since the size of the lens wobbles increases with accommodative effort, one would predict a similar increase of PSO-amplitude in data recorded with a pupil based eye tracker. In four experiments, we investigated the role of lens accommodation on PSOs in a video-based eye tracker. In Experiment 1, we replicated previous results showing that PSO-amplitudes increase at near viewing distances (large vergence angles), when the lens is highly accommodated. In Experiment 2a, we manipulated the accommodative state of the lens pharmacologically using eye drops at a fixed viewing distance and found, in contrast to Experiment 1, no significant difference in PSO-amplitude related to the accommodative state of the lens. Finally, in Experiment 2b, the effect of vergence angle was investigated by comparing PSO-amplitudes at near and far while maintaining a fixed lens accommodation. Despite the pharmacologically fixed degree of accommodation, PSO-amplitudes were systematically larger in the near condition. In summary, PSOs cannot exhaustively be explained by lens wobbles. Possible confounds related to pupil size and eye-camera angle are investigated in Experiments 3 and 4, and alternative mechanisms behind PSOs are probed in the discussion. Copyright © 2014 Elsevier Ltd. All rights reserved.

Cultural-based particle swarm for dynamic optimisation problems

NASA Astrophysics Data System (ADS)

Daneshyari, Moayed; Yen, Gary G.

2012-07-01

Many practical optimisation problems are with the existence of uncertainties, among which a significant number belong to the dynamic optimisation problem (DOP) category in which the fitness function changes through time. In this study, we propose the cultural-based particle swarm optimisation (PSO) to solve DOP problems. A cultural framework is adopted incorporating the required information from the PSO into five sections of the belief space, namely situational, temporal, domain, normative and spatial knowledge. The stored information will be adopted to detect the changes in the environment and assists response to the change through a diversity-based repulsion among particles and migration among swarms in the population space, and also helps in selecting the leading particles in three different levels, personal, swarm and global levels. Comparison of the proposed heuristics over several difficult dynamic benchmark problems demonstrates the better or equal performance with respect to most of other selected state-of-the-art dynamic PSO heuristics.

Identification of inelastic parameters based on deep drawing forming operations using a global-local hybrid Particle Swarm approach

NASA Astrophysics Data System (ADS)

Vaz, Miguel; Luersen, Marco A.; Muñoz-Rojas, Pablo A.; Trentin, Robson G.

2016-04-01

Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress-strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder-Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global-local PSO-Nelder-Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder-Mead algorithm to obtain the minimum itself.

A comparative analysis of chaotic particle swarm optimizations for detecting single nucleotide polymorphism barcodes.

PubMed

Chuang, Li-Yeh; Moi, Sin-Hua; Lin, Yu-Da; Yang, Cheng-Hong

2016-10-01

Evolutionary algorithms could overcome the computational limitations for the statistical evaluation of large datasets for high-order single nucleotide polymorphism (SNP) barcodes. Previous studies have proposed several chaotic particle swarm optimization (CPSO) methods to detect SNP barcodes for disease analysis (e.g., for breast cancer and chronic diseases). This work evaluated additional chaotic maps combined with the particle swarm optimization (PSO) method to detect SNP barcodes using a high-dimensional dataset. Nine chaotic maps were used to improve PSO method results and compared the searching ability amongst all CPSO methods. The XOR and ZZ disease models were used to compare all chaotic maps combined with PSO method. Efficacy evaluations of CPSO methods were based on statistical values from the chi-square test (χ 2 ). The results showed that chaotic maps could improve the searching ability of PSO method when population are trapped in the local optimum. The minor allele frequency (MAF) indicated that, amongst all CPSO methods, the numbers of SNPs, sample size, and the highest χ 2 value in all datasets were found in the Sinai chaotic map combined with PSO method. We used the simple linear regression results of the gbest values in all generations to compare the all methods. Sinai chaotic map combined with PSO method provided the highest β values (β≥0.32 in XOR disease model and β≥0.04 in ZZ disease model) and the significant p-value (p-value<0.001 in both the XOR and ZZ disease models). The Sinai chaotic map was found to effectively enhance the fitness values (χ 2 ) of PSO method, indicating that the Sinai chaotic map combined with PSO method is more effective at detecting potential SNP barcodes in both the XOR and ZZ disease models. Copyright © 2016 Elsevier B.V. All rights reserved.

Discrete particle swarm optimization to solve multi-objective limited-wait hybrid flow shop scheduling problem

NASA Astrophysics Data System (ADS)

Santosa, B.; Siswanto, N.; Fiqihesa

2018-04-01

This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution

Discrete particle swarm optimization for identifying community structures in signed social networks.

PubMed

Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng

2014-10-01

Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Copyright © 2014 Elsevier Ltd. All rights reserved.

PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization

PubMed Central

Chen, Shuangqing; Wei, Lixin; Guan, Bing

2018-01-01

Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems. PMID:29675036

A dynamic programming-based particle swarm optimization algorithm for an inventory management problem under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Zeng, Ziqiang; Han, Bernard; Lei, Xiao

2013-07-01

This article presents a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm for solving an inventory management problem for large-scale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multi-objective fuzzy random dynamic programming model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform fuzzy random parameters into fuzzy variables that are subsequently defuzzified by using an expected value operator with optimistic-pessimistic index. The iterative nature of the authors' model motivates them to develop a DP-based PSO algorithm. More specifically, their approach treats the state variables as hidden parameters. This in turn eliminates many redundant feasibility checks during initialization and particle updates at each iteration. Results and sensitivity analysis are presented to highlight the performance of the authors' optimization method, which is very effective as compared to the standard PSO algorithm.

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

A Novel Mu Rhythm-based Brain Computer Interface Design that uses a Programmable System on Chip.

PubMed

Joshi, Rohan; Saraswat, Prateek; Gajendran, Rudhram

2012-01-01

This paper describes the system design of a portable and economical mu rhythm based Brain Computer Interface which employs Cypress Semiconductors Programmable System on Chip (PSoC). By carrying out essential processing on the PSoC, the use of an extra computer is eliminated, resulting in considerable cost savings. Microsoft Visual Studio 2005 and PSoC Designer 5.01 are employed in developing the software for the system, the hardware being custom designed. In order to test the usability of the BCI, preliminary testing is carried out by training three subjects who were able to demonstrate control over their electroencephalogram by moving a cursor present at the center of the screen towards the indicated direction with an average accuracy greater than 70% and a bit communication rate of up to 7 bits/min.

Subpixel displacement measurement method based on the combination of particle swarm optimization and gradient algorithm

NASA Astrophysics Data System (ADS)

Guang, Chen; Qibo, Feng; Keqin, Ding; Zhan, Gao

2017-10-01

A subpixel displacement measurement method based on the combination of particle swarm optimization (PSO) and gradient algorithm (GA) was proposed for accuracy and speed optimization in GA, which is a subpixel displacement measurement method better applied in engineering practice. An initial integer-pixel value was obtained according to the global searching ability of PSO, and then gradient operators were adopted for a subpixel displacement search. A comparison was made between this method and GA by simulated speckle images and rigid-body displacement in metal specimens. The results showed that the computational accuracy of the combination of PSO and GA method reached 0.1 pixel in the simulated speckle images, or even 0.01 pixels in the metal specimen. Also, computational efficiency and the antinoise performance of the improved method were markedly enhanced.

A Novel Mu Rhythm-based Brain Computer Interface Design that uses a Programmable System on Chip

PubMed Central

Joshi, Rohan; Saraswat, Prateek; Gajendran, Rudhram

2012-01-01

This paper describes the system design of a portable and economical mu rhythm based Brain Computer Interface which employs Cypress Semiconductors Programmable System on Chip (PSoC). By carrying out essential processing on the PSoC, the use of an extra computer is eliminated, resulting in considerable cost savings. Microsoft Visual Studio 2005 and PSoC Designer 5.01 are employed in developing the software for the system, the hardware being custom designed. In order to test the usability of the BCI, preliminary testing is carried out by training three subjects who were able to demonstrate control over their electroencephalogram by moving a cursor present at the center of the screen towards the indicated direction with an average accuracy greater than 70% and a bit communication rate of up to 7 bits/min. PMID:23493871

Estimating SPT-N Value Based on Soil Resistivity using Hybrid ANN-PSO Algorithm

NASA Astrophysics Data System (ADS)

Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

2018-04-01

Standard Penetration Resistance (N value) is used in many empirical geotechnical engineering formulas. Meanwhile, soil resistivity is a measure of soil’s resistance to electrical flow. For a particular site, usually, only a limited N value data are available. In contrast, resistivity data can be obtained extensively. Moreover, previous studies showed evidence of a correlation between N value and resistivity value. Yet, no existing method is able to interpret resistivity data for estimation of N value. Thus, the aim is to develop a method for estimating N-value using resistivity data. This study proposes a hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) method to estimate N value using resistivity data. Five different ANN-PSO models based on five boreholes were developed and analyzed. The performance metrics used were the coefficient of determination, R2 and mean absolute error, MAE. Analysis of result found that this method can estimate N value (R2 best=0.85 and MAEbest=0.54) given that the constraint, Δ {\\bar{l}}ref, is satisfied. The results suggest that ANN-PSO method can be used to estimate N value with good accuracy.

42 CFR 422.386 - Liquidity.

Code of Federal Regulations, 2011 CFR

2011-10-01

... PROGRAM MEDICARE ADVANTAGE PROGRAM Provider-Sponsored Organizations § 422.386 Liquidity. (a) A PSO must... determine whether the PSO meets the requirement in paragraph (a) of this section, CMS will examine the following— (1) The PSO's timeliness in meeting current obligations; (2) The extent to which the PSO's...

42 CFR 422.386 - Liquidity.

Code of Federal Regulations, 2010 CFR

2010-10-01

... PROGRAM MEDICARE ADVANTAGE PROGRAM Provider-Sponsored Organizations § 422.386 Liquidity. (a) A PSO must... determine whether the PSO meets the requirement in paragraph (a) of this section, CMS will examine the following— (1) The PSO's timeliness in meeting current obligations; (2) The extent to which the PSO's...

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.

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

PubMed Central

Shamsi, Mousa; Sedaaghi, Mohammad Hossein

2016-01-01

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate. PMID:27560945

TH-A-9A-02: BEST IN PHYSICS (THERAPY) - 4D IMRT Planning Using Highly- Parallelizable Particle Swarm Optimization

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

Modiri, A; Gu, X; Sawant, A

2014-06-15

Purpose: We present a particle swarm optimization (PSO)-based 4D IMRT planning technique designed for dynamic MLC tracking delivery to lung tumors. The key idea is to utilize the temporal dimension as an additional degree of freedom rather than a constraint in order to achieve improved sparing of organs at risk (OARs). Methods: The target and normal structures were manually contoured on each of the ten phases of a 4DCT scan acquired from a lung SBRT patient who exhibited 1.5cm tumor motion despite the use of abdominal compression. Corresponding ten IMRT plans were generated using the Eclipse treatment planning system. Thesemore » plans served as initial guess solutions for the PSO algorithm. Fluence weights were optimized over the entire solution space i.e., 10 phases × 12 beams × 166 control points. The size of the solution space motivated our choice of PSO, which is a highly parallelizable stochastic global optimization technique that is well-suited for such large problems. A summed fluence map was created using an in-house B-spline deformable image registration. Each plan was compared with a corresponding, internal target volume (ITV)-based IMRT plan. Results: The PSO 4D IMRT plan yielded comparable PTV coverage and significantly higher dose—sparing for parallel and serial OARs compared to the ITV-based plan. The dose-sparing achieved via PSO-4DIMRT was: lung Dmean = 28%; lung V20 = 90%; spinal cord Dmax = 23%; esophagus Dmax = 31%; heart Dmax = 51%; heart Dmean = 64%. Conclusion: Truly 4D IMRT that uses the temporal dimension as an additional degree of freedom can achieve significant dose sparing of serial and parallel OARs. Given the large solution space, PSO represents an attractive, parallelizable tool to achieve globally optimal solutions for such problems. This work was supported through funding from the National Institutes of Health and Varian Medical Systems. Amit Sawant has research funding from Varian Medical Systems, VisionRT Ltd. and Elekta.« less

Exploiting diurnal variations to evaluate the ISCCP-FD flux calculations and radiative-flux-analysis-processed surface observations from BSRN, ARM, and SURFRAD

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

Zhang, Yuanchong; Long, Charles N.; Rossow, William B.

2010-01-01

Based on monthly-3-hourly and 3-hourly mean surface radiative fluxes and their associated meteorological parameters for 2004 from the International Satellite Cloud Climatology Project-FD (ISCCP-FD) and the Radiative Flux Analysis method-Produced Surface Observations (RFA-PSO) for 15 high-quality-controlled surface stations, operated by the Baseline Surface Radiation Network (BSRN), the Atmospheric Radiation Measurement (ARM) and the National Oceanic and Atmospheric Administration's Surface Radiation budget network (SURFRAD), this work, goes beyond the previous validation for FD against surface observation by introducing the Meteorological Similarity Comparison Method (MSCM) to make a more precise, mutual evaluation of both FD and PSO products. The comparison results inmore » substantial uncertainty reduction and provides reasonable physical explanations for the flux differences. This approach compares fluxes for cases where the atmospheric and surface physical properties (specifically, the input parameters for radiative transfer model) are as close as possible to the values determined at the observational sites by matching the RFA-produced cloud fraction (CF) and/or optical thickness (Tau), etc., or alternatively, by directly changing the model input variables for FD to match PSO values, and using such-produced matched sub-datasets to make more accurate comparisons based on more similar meteorological environments between FD and PSO. The crucial part is the availability of flux-associated meteorological parameters from RFA-PSO, which was only recently made available that makes this work possible. For surface downwelling shortwave(SW) flux (SWdn) and its two components, diffuse (Dif) and direct (Dir), uncertainty for monthly mean is 15, 15 and 17 W/m 2, respectively, smaller than the separately estimated uncertainty values from both FD and PSO. When applying MSCM by reducing their CF difference, the differences can be reduced by a factor of 2. The strength of MSCM is particularly shown in the comparisons of diurnal variations. For clear sky, reducing the FD values of aerosol optical depth (AOD) by 50% to approximately match the PSO values brings all downward SW flux components into substantial agreement. For cloudy scenes, when both CF and Tau are matched to within 0.1 – 0.25 and ~10, respectively, the majority of the SW flux components have nearly-perfect agreement between FD and PSO. The best restriction differences are not zero indicates the influence of other parameters that are not accounted for yet. For longwave (LW) fluxes, general evaluation also confirms uncertainty values for FD and PSO less than separately estimated. When applying MSCM to CF and surface air temperature, the agreement is substantially improved. For downwelling LW diurnal variation comparison, FD shows good agreement with PSO for both RFA-defined or true clear sky but overestimates the amplitude for cloudy sky by 3-7 W/m 2, which may be caused by different sensitivities to cirrus clouds. For upwelling LW diurnal cycle, the situation is reversed; FD now underestimates the diurnal amplitude for all and clear sky but generally agrees for overcast (CF > 0.7). The combined effect of downwelling and upwelling LW fluxes results in FD's underestimates of the diurnal variation of the net-LW-loss for all the scenes by up to 10 W/m 2, although the daily mean net loss is more accurate. Therefore, in terms of amplitude and phase, both FD and PSO seem to have caught correct diurnal variations.« less

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

Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz

This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variablesmore » that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.« less

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

PubMed

Vimalarani, C; Subramanian, R; Sivanandam, S N

2016-01-01

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Analytical Model-Based Design Optimization of a Transverse Flux Machine

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

Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz

This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variablesmore » that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.« less

Preparation of psoralen polymer-lipid hybrid nanoparticles and their reversal of multidrug resistance in MCF-7/ADR cells.

PubMed

Huang, Qingqing; Cai, Tiange; Li, Qianwen; Huang, Yinghong; Liu, Qian; Wang, Bingyue; Xia, Xi; Wang, Qi; Whitney, John C C; Cole, Susan P C; Cai, Yu

2018-11-01

Multidrug resistance (MDR) is the leading cause of failure for breast cancer in the clinic. Thus far, polymer-lipid hybrid nanoparticles (PLN) loaded chemotherapeutic agents has been used to overcome MDR in breast cancer. In this study, we prepared psoralen polymer-lipid hybrid nanoparticles (PSO-PLN) to reverse drug resistant MCF-7/ADR cells in vitro and in vivo. PSO-PLN was prepared by the emulsification evaporation-low temperature solidification method. The formulation, water solubility and bioavailability, particle size, zeta potential and entrapment efficiency, and in vitro release experiments were optimized in order to improve the activity of PSO to reverse MDR. Optimal formulation: soybean phospholipids 50 mg, poly(lactic-co-glycolic) acid (PLGA) 15 mg, PSO 3 mg, and Tween-80 1%. The PSO-PLN possessed a round appearance, uniform size, exhibited no adhesion. The average particle size was 93.59 ± 2.87 nm, the dispersion co-efficient was 0.249 ± 0.06, the zeta potential was 25.47 ± 2.84 mV. In vitro analyses revealed that PSO resistance index was 3.2, and PSO-PLN resistance index was 5.6, indicating that PSO-PLN versus MCF-7/ADR reversal effect was significant. Moreover, PSO-PLN is somewhat targeted to the liver, and has an antitumor effect in the xenograft model of drug-resistant MCF-7/ADR cells. In conclusion, PSO-PLN not only reverses MDR but also improves therapeutic efficiency by enhancing sustained release of PSO.

42 CFR 422.352 - Basic requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

.... (a) General rule. An organization is considered a PSO for purposes of a MA contract if the... definition of a PSO set forth in § 422.350 and other applicable requirements of this subpart; and (3) Is... that established and operate the PSO. (b) Provision of services. A PSO must demonstrate to CMS's...

42 CFR 3.104 - Secretarial actions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... ORGANIZATIONS AND PATIENT SAFETY WORK PRODUCT PSO Requirements and Agency Procedures § 3.104 Secretarial actions. (a) Actions in response to certification submissions for initial and continued listing as a PSO. (1... the entity as a PSO, or maintain the listing of a PSO, if the Secretary determines that the entity...

42 CFR 422.352 - Basic requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

.... (a) General rule. An organization is considered a PSO for purposes of a MA contract if the... definition of a PSO set forth in § 422.350 and other applicable requirements of this subpart; and (3) Is... that established and operate the PSO. (b) Provision of services. A PSO must demonstrate to CMS's...

42 CFR 3.110 - Assessment of PSO compliance.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 1 2011-10-01 2011-10-01 false Assessment of PSO compliance. 3.110 Section 3.110... SAFETY ORGANIZATIONS AND PATIENT SAFETY WORK PRODUCT PSO Requirements and Agency Procedures § 3.110 Assessment of PSO compliance. The Secretary may request information or conduct announced or unannounced...

Determination of the carmine content based on spectrum fluorescence spectral and PSO-SVM

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Peng, Tao; Cheng, Qi; Wang, Gui-chuan; Kong, De-ming; Wang, Yu-tian

2018-03-01

Carmine is a widely used food pigment in various food and beverage additives. Excessive consumption of synthetic pigment shall do harm to body seriously. The food is generally associated with a variety of colors. Under the simulation context of various food pigments' coexistence, we adopted the technology of fluorescence spectroscopy, together with the PSO-SVM algorithm, so that to establish a method for the determination of carmine content in mixed solution. After analyzing the prediction results of PSO-SVM, we collected a bunch of data: the carmine average recovery rate was 100.84%, the root mean square error of prediction (RMSEP) for 1.03e-04, 0.999 for the correlation coefficient between the model output and the real value of the forecast. Compared with the prediction results of reverse transmission, the correlation coefficient of PSO-SVM was 2.7% higher, the average recovery rate for 0.6%, and the root mean square error was nearly one order of magnitude lower. According to the analysis results, it can effectively avoid the interference caused by pigment with the combination of the fluorescence spectrum technique and PSO-SVM, accurately determining the content of carmine in mixed solution with an effect better than that of BP.

Identifying Psoriasis and Psoriatic Arthritis Patients in Retrospective Databases When Diagnosis Codes Are Not Available: A Validation Study Comparing Medication/Prescriber Visit-Based Algorithms with Diagnosis Codes.

PubMed

Dobson-Belaire, Wendy; Goodfield, Jason; Borrelli, Richard; Liu, Fei Fei; Khan, Zeba M

2018-01-01

Using diagnosis code-based algorithms is the primary method of identifying patient cohorts for retrospective studies; nevertheless, many databases lack reliable diagnosis code information. To develop precise algorithms based on medication claims/prescriber visits (MCs/PVs) to identify psoriasis (PsO) patients and psoriatic patients with arthritic conditions (PsO-AC), a proxy for psoriatic arthritis, in Canadian databases lacking diagnosis codes. Algorithms were developed using medications with narrow indication profiles in combination with prescriber specialty to define PsO and PsO-AC. For a 3-year study period from July 1, 2009, algorithms were validated using the PharMetrics Plus database, which contains both adjudicated medication claims and diagnosis codes. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of the developed algorithms were assessed using diagnosis code as the reference standard. Chosen algorithms were then applied to Canadian drug databases to profile the algorithm-identified PsO and PsO-AC cohorts. In the selected database, 183,328 patients were identified for validation. The highest PPVs for PsO (85%) and PsO-AC (65%) occurred when a predictive algorithm of two or more MCs/PVs was compared with the reference standard of one or more diagnosis codes. NPV and specificity were high (99%-100%), whereas sensitivity was low (≤30%). Reducing the number of MCs/PVs or increasing diagnosis claims decreased the algorithms' PPVs. We have developed an MC/PV-based algorithm to identify PsO patients with a high degree of accuracy, but accuracy for PsO-AC requires further investigation. Such methods allow researchers to conduct retrospective studies in databases in which diagnosis codes are absent. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

The pso4-1 mutation reduces spontaneous mitotic gene conversion and reciprocal recombination in Saccharomyces cerevisiae.

PubMed

Meira, L B; Fonseca, M B; Averbeck, D; Schenberg, A C; Henriques, J A

1992-11-01

Spontaneous mitotic recombination was examined in the haploid pso4-1 mutant of Saccharomyces cerevisiae and in the corresponding wild-type strain. Using a genetic system involving a duplication of the his4 gene it was shown that the pso4-1 mutation decreases at least fourfold the spontaneous rate of mitotic recombination. The frequency of spontaneous recombination was reduced tenfold in pso4-1 strains, as previously observed in the rad52-1 mutant. However, whereas the rad52-1 mutation specifically reduces gene conversion, the pso4-1 mutation reduces both gene conversion and reciprocal recombination. Induced mitotic recombination was also studied in pso4-1 mutant and wild-type strains after treatment with 8-methoxypsoralen plus UVA and 254 nm UV irradiation. Consistent with previous results, the pso4-1 mutation was found strongly to affect recombination induction.

42 CFR 422.390 - Guarantees.

Code of Federal Regulations, 2010 CFR

2010-10-01

... policy. A PSO, or the legal entity of which the PSO is a component, may apply to CMS to use the financial... financial resources of a guarantor, a PSO must submit to CMS— (1) Documentation that the guarantor meets the... including other guarantees, intangibles and restricted reserves) equal to three times the amount of the PSO...

42 CFR 422.390 - Guarantees.

Code of Federal Regulations, 2011 CFR

2011-10-01

... policy. A PSO, or the legal entity of which the PSO is a component, may apply to CMS to use the financial... financial resources of a guarantor, a PSO must submit to CMS— (1) Documentation that the guarantor meets the... including other guarantees, intangibles and restricted reserves) equal to three times the amount of the PSO...

Effects of replacing beef fat with pre-emulsified pumpkin seed oil on some quality characteristics of model system chicken meat emulsions

NASA Astrophysics Data System (ADS)

Serdaroğlu, M.; Nacak, B.; Karabıyıkoğlu, M.; Tepe, M.; Baykara, I.; Kökmen, Y.

2017-09-01

In this study, the effects of adding pumpkin seed oil (PSO) in water emulsion to model system chicken meat emulsions (MSME) on product quality and oxidative stability were investigated. MSME were produced by replacing 25% (P25) and 50% (P50) of beef fat with PSO-in-water emulsion (PSO/W) while control treatment was prepared with only beef fat. Addition of PSO/W to the formulation resulted in significant differences in chemical composition and pH values of both raw and cooked MSME treatments. The use of PSO/W produced significant improvements to emulsion stability, oxidative stability and cooking yield of MSME. It was determined that the use of PSO/W formulation results in decreased total expressible fluid values and increased cooking yields of the emulsions. It was observed that the highest cooking yield and the lowest total expressible fluid were found in the sample containing 50% PSO/W. It should be a feasible strategy to produce fat-reduced meat products with healthier lipid profiles by using PSO/W.

Decentralized automatic generation control of interconnected power systems incorporating asynchronous tie-lines.

PubMed

Ibraheem; Hasan, Naimul; Hussein, Arkan Ahmed

2014-01-01

This Paper presents the design of decentralized automatic generation controller for an interconnected power system using PID, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The designed controllers are tested on identical two-area interconnected power systems consisting of thermal power plants. The area interconnections between two areas are considered as (i) AC tie-line only (ii) Asynchronous tie-line. The dynamic response analysis is carried out for 1% load perturbation. The performance of the intelligent controllers based on GA and PSO has been compared with the conventional PID controller. The investigations of the system dynamic responses reveal that PSO has the better dynamic response result as compared with PID and GA controller for both type of area interconnection.

Performance evaluation of different types of particle representation procedures of Particle Swarm Optimization in Job-shop Scheduling Problems

NASA Astrophysics Data System (ADS)

Izah Anuar, Nurul; Saptari, Adi

2016-02-01

This paper addresses the types of particle representation (encoding) procedures in a population-based stochastic optimization technique in solving scheduling problems known in the job-shop manufacturing environment. It intends to evaluate and compare the performance of different particle representation procedures in Particle Swarm Optimization (PSO) in the case of solving Job-shop Scheduling Problems (JSP). Particle representation procedures refer to the mapping between the particle position in PSO and the scheduling solution in JSP. It is an important step to be carried out so that each particle in PSO can represent a schedule in JSP. Three procedures such as Operation and Particle Position Sequence (OPPS), random keys representation and random-key encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the OR-Library, where the objective function is to minimize the makespan by the use of MATLAB software. Based on the experimental results, it is discovered that OPPS gives the best performance in solving both benchmark problems. The contribution of this paper is the fact that it demonstrates to the practitioners involved in complex scheduling problems that different particle representation procedures can have significant effects on the performance of PSO in solving JSP.

Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks

PubMed Central

Robinson, Y. Harold; Rajaram, M.

2015-01-01

Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique. PMID:26819966

New HCFA regulations clarify PSO requirements.

PubMed

Brock, T H

1998-06-01

In March and April of 1998, HCFA promulgated regulations regarding various requirements for provider-sponsored organizations (PSOs). These regulations define what constitutes an affiliated provider to a PSO, identify what percentage of services must be provided directly to beneficiaries by PSO affiliated providers, define what constitutes provider ownership in a PSO, and set minimum capitalization and liquidity standards for PSOs.

42 CFR 422.514 - Minimum enrollment requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

... individuals if the organization is a PSO) are enrolled for the purpose of receiving health benefits from the organization; or (2) At least 1,500 individuals (or 500 individuals if the organization is a PSO) are enrolled... individuals residing outside of urbanized areas as defined in § 412.62(f) (or, in the case of a PSO, the PSO...

42 CFR 422.514 - Minimum enrollment requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... individuals if the organization is a PSO) are enrolled for the purpose of receiving health benefits from the organization; or (2) At least 1,500 individuals (or 500 individuals if the organization is a PSO) are enrolled... individuals residing outside of urbanized areas as defined in § 412.62(f) (or, in the case of a PSO, the PSO...

42 CFR 3.108 - Correction of deficiencies, revocation, and voluntary relinquishment.

Code of Federal Regulations, 2011 CFR

2011-10-01

... HUMAN SERVICES GENERAL PROVISIONS PATIENT SAFETY ORGANIZATIONS AND PATIENT SAFETY WORK PRODUCT PSO... entity as a PSO if he determines— (i) The PSO is not fulfilling the certifications made to the Secretary as required by § 3.102; (ii) The PSO has not met the two contract requirement, as required by § 3.102...

The PSO4 gene is responsible for an error-prone recombinational DNA repair pathway in Saccharomyces cerevisiae.

PubMed

de Andrade, H H; Marques, E K; Schenberg, A C; Henriques, J A

1989-06-01

The induction of mitotic gene conversion and crossing-over in Saccharomyces cerevisiae diploid cells homozygous for the pso4-1 mutation was examined in comparison to the corresponding wild-type strain. The pso4-1 mutant strain was found to be completely blocked in mitotic recombination induced by photoaddition of mono- and bifunctional psoralen derivatives as well as by mono- (HN1) and bifunctional (HN2) nitrogen mustards or 254 nm UV radiation in both stationary and exponential phases of growth. Concerning the lethal effect, diploids homozygous for the pso4-1 mutation are more sensitive to all agents tested in any growth phase. However, this effect is more pronounced in the G2 phase of the cell cycle. These results imply that the ploidy effect and the resistance of budding cells are under the control of the PSO4 gene. On the other hand, the pso4-1 mutant is mutationally defective for all agents used. Therefore, the pso4-1 mutant has a generalized block in both recombination and mutation ability. This indicates that the PSO4 gene is involved in an error-prone repair pathway which relies on a recombinational mechanism, strongly suggesting an analogy between the pso4-1 mutation and the RecA or LexA mutation of Escherichia coli.

76 FR 21744 - Agency Information Collection Activities: Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-18

... outcomes and (1) is assembled or developed by a provider for reporting to a PSO and is reported to a PSO or (2) is developed by a PSO for the conduct of patient safety activities. Civil money penalties may be... be listed as a PSO by the Secretary must certify that it meets certain requirements and, upon listing...

The Contribution of Particle Swarm Optimization to Three-Dimensional Slope Stability Analysis

PubMed Central

A Rashid, Ahmad Safuan; Ali, Nazri

2014-01-01

Over the last few years, particle swarm optimization (PSO) has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D) slope stability analysis. This paper applied PSO in three-dimensional (3D) slope stability problem to determine the critical slip surface (CSS) of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes. PMID:24991652

The contribution of particle swarm optimization to three-dimensional slope stability analysis.

PubMed

Kalatehjari, Roohollah; Rashid, Ahmad Safuan A; Ali, Nazri; Hajihassani, Mohsen

2014-01-01

Over the last few years, particle swarm optimization (PSO) has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D) slope stability analysis. This paper applied PSO in three-dimensional (3D) slope stability problem to determine the critical slip surface (CSS) of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes.

Incremental social learning in particle swarms.

PubMed

de Oca, Marco A Montes; Stutzle, Thomas; Van den Enden, Ken; Dorigo, Marco

2011-04-01

Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization algorithms. Our study focuses on two particle swarm optimization (PSO) algorithms: a) the incremental particle swarm optimizer (IPSO), which is a PSO algorithm with a growing population size in which the initial position of new particles is biased toward the best-so-far solution, and b) the incremental particle swarm optimizer with local search (IPSOLS), in which solutions are further improved through a local search procedure. We first derive analytically the probability density function induced by the proposed initialization rule applied to new particles. Then, we compare the performance of IPSO and IPSOLS on a set of benchmark functions with that of other PSO algorithms (with and without local search) and a random restart local search algorithm. Finally, we measure the benefits of using incremental social learning on PSO algorithms by running IPSO and IPSOLS on problems with different fitness distance correlations.

Prediction of Tibial Rotation Pathologies Using Particle Swarm Optimization and K-Means Algorithms.

PubMed

Sari, Murat; Tuna, Can; Akogul, Serkan

2018-03-28

The aim of this article is to investigate pathological subjects from a population through different physical factors. To achieve this, particle swarm optimization (PSO) and K-means (KM) clustering algorithms have been combined (PSO-KM). Datasets provided by the literature were divided into three clusters based on age and weight parameters and each one of right tibial external rotation (RTER), right tibial internal rotation (RTIR), left tibial external rotation (LTER), and left tibial internal rotation (LTIR) values were divided into three types as Type 1, Type 2 and Type 3 (Type 2 is non-pathological (normal) and the other two types are pathological (abnormal)), respectively. The rotation values of every subject in any cluster were noted. Then the algorithm was run and the produced values were also considered. The values of the produced algorithm, the PSO-KM, have been compared with the real values. The hybrid PSO-KM algorithm has been very successful on the optimal clustering of the tibial rotation types through the physical criteria. In this investigation, Type 2 (pathological subjects) is of especially high predictability and the PSO-KM algorithm has been very successful as an operation system for clustering and optimizing the tibial motion data assessments. These research findings are expected to be very useful for health providers, such as physiotherapists, orthopedists, and so on, in which this consequence may help clinicians to appropriately designing proper treatment schedules for patients.

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

PubMed Central

Zhu, Zhichuan; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified. PMID:29853983

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO.

PubMed

Li, Yang; Zhu, Zhichuan; Hou, Alin; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

Motivational interviewing-based training enhances clinicians' skills and knowledge in psoriasis: findings from the Pso Well® study.

PubMed

Chisholm, A; Nelson, P A; Pearce, C J; Littlewood, A J; Kane, K; Henry, A L; Thorneloe, R; Hamilton, M P; Lavallee, J; Lunt, M; Griffiths, C E M; Cordingley, L; Bundy, C

2017-03-01

Psoriasis is a common long-term, immune-mediated skin condition associated with behavioural factors (e.g. smoking, excess alcohol, obesity), which increase the risk of psoriasis onset, flares and comorbidities. Motivational interviewing (MI) is an evidence-based approach to health-related behaviour change that has been used successfully for patients with long-term conditions. This study assessed change in clinicians' MI skills and psoriasis knowledge following Psoriasis and Wellbeing (Pso Well ® ) training. To investigate whether the Pso Well training intervention improves clinicians' MI skills and knowledge about psoriasis-related comorbidities and risk factors; and to explore the acceptability and feasibility of the Pso Well training content, delivery and evaluation. Clinicians attended the 1-day training programme focused on MI skills development in the context of psoriasis. MI skills were assessed pre- and post-training using the Behaviour Change Counselling Index. Knowledge about psoriasis-related comorbidity and risk factors was assessed with a novel 22-point measure developed for the study. Interviews with clinicians were analysed qualitatively to identify perceptions about the feasibility and acceptability of the training. Sixty-one clinicians completed the training (35 dermatology nurses, 23 dermatologists and three primary-care clinicians). Clinicians' MI skills (P < 0·001) and knowledge (P < 0·001) increased significantly post-training. Clinicians found the training valuable and relevant to psoriasis management. Attendance at the Pso Well training resulted in improvements in clinicians' knowledge and skills to manage psoriasis holistically. Clinicians deemed the training itself and the assessment procedures used both feasible and acceptable. Future research should investigate how this training may influence patient outcomes. © 2016 British Association of Dermatologists.

An Approach to Economic Dispatch with Multiple Fuels Based on Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Sriyanyong, Pichet

2011-06-01

Particle Swarm Optimization (PSO), a stochastic optimization technique, shows superiority to other evolutionary computation techniques in terms of less computation time, easy implementation with high quality solution, stable convergence characteristic and independent from initialization. For this reason, this paper proposes the application of PSO to the Economic Dispatch (ED) problem, which occurs in the operational planning of power systems. In this study, ED problem can be categorized according to the different characteristics of its cost function that are ED problem with smooth cost function and ED problem with multiple fuels. Taking the multiple fuels into account will make the problem more realistic. The experimental results show that the proposed PSO algorithm is more efficient than previous approaches under consideration as well as highly promising in real world applications.

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

PubMed Central

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

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.

Can Stable Isotope combined with Trace Element Analysis distinguish between pure and g.g.A. (protected geographical indication, P.G.I.) certified Pumpkin Seed Oils?

NASA Astrophysics Data System (ADS)

Meier-Augenstein, Wolfram; Kemp, Helen; Midwood, Andy

2013-04-01

Styrian Pumpkin Seed Oil is a premium single seed vegetable oil that is uniquely linked to the geographic region of Styria where it is grown and produced. In 1996, the strong regional ties of this typical Styrian speciality were recognised by the EU-Commission who declared "Styrian Pumpkin Seed Oil P.G.I." as a Protected Geographical Indication (article 5 VO(EWG) Nr. 2081/92). In 1998, more than 2,000 domestic pumpkin seed producers and 30 oil mills formed an association of Styrian pumpkin seed oil producers, which is now called the "Gemeinschaft Steirisches Kürbiskernöl g.g.A.". This producers' association was formed in order to protect the regionality and the high quality of Styrian Pumpkin Seed Oil P.G.I. Procedures implemented by this producers' association document every step in the process from pumpkin seeds to seed crushing in oil mills and finally bottling of Styrian Pumpkin Seed Oil P.G.I., keeping a contiguous record of all production steps including annual harvest amounts. This permits full traceability of every bottle of Styrian Pumpkin Seed Oil P.G.I from harvest to the finished, bottled products found on the shelf of delis and even supermarkets. Despite these efforts of the producers' association, there have been repeated claims of g.g.A. (P.G.I.) certified bottles of Styrian Pumpkin Seed Oil (PSO) having been analysed independently and shown to contain either mixtures of Styrian and non-Styrian PSO or no Styrian PSO at all. Since keeping records of annual harvest amounts of pumpkin seeds would make it very difficult for an "over-production" by mixing or substitution of alien PSO's to go unnoticed, we formed the hypothesis that the red-flagged bottles could have been counterfeits containing alien PSO with bottles sporting fake g.g.A. seals and fake serial numbers. An alternative hypothesis was that the chosen method of detection of allegedly misrepresented g.g.A. Styrian PSO resulted in a high number of false negatives thus incorrectly rejecting genuine Styrian PSO as alien PSO and mixtures of Styrian PSO with alien PSO. To investigate the potential of multivariate stable isotope analysis as a means to correctly distinguish between genuine Syrian PSOs and other PSOs, we purchased 13 + 1 PSOs (13 different brands) from high-street and on-line shops. Samples were given alpha-numerical sample IDs and were analysed in a single-blinded fashion. Based on 2H, 13C and 18O abundance values alone sensitivity and specificity were 0.75 (1 false negative; 3 true positives) and 0.86 (1 false positive; 6 true negatives), respectively. However, when combining stable isotope data with trace element data, sensitivity and specificity both improved with no false negatives or false positives being detected. Chemometric statistical analysis clearly separated the 3 g.g.A. certified Styrian PSOs from all but one other PSO, which was also a genuine Styrian PSO in as much as it was pressed from genuine Styrian pumpkin seeds though not by a Styrian oil mill and thus not qualifying for the g.g.A. mark.

Clinical and radiographic outcomes of thoracic and lumbar pedicle subtraction osteotomy for fixed sagittal imbalance.

PubMed

Yang, Benson P; Ondra, Stephen L; Chen, Larry A; Jung, Hee Soo; Koski, Tyler R; Salehi, Sean A

2006-07-01

The authors conducted a study to evaluate the radiographically documented and functional outcomes obtained in patients who underwent pedicle subtraction osteotomy (PSO). They also compared outcomes after classification of cases into thoracic and lumbar PSO subgroups. The authors analyzed data obtained in 35 consecutive PSO-treated patients with sagittal imbalance. One surgeon performed all surgeries. The minimal follow-up period was 2 years. Events during the perioperative course and complications were noted. Standing long-film radiographs of the spine were obtained and measurements were made preoperatively, immediately postoperatively, and at most recent follow-up examination. The modified Prolo Scale and the 22-item Scoliosis Research Society (SRS-22) Outcomes Questionnaire were administered. Early complications after PSO included neurological injury, wound-related problems, and nosocomial infections. Late complications were limited to pseudarthrosis and attendant instrumentation failure. Early and late complication rates ranged from 10 to 30% for both thoracic and lumbar PSO cohorts. Lumbar PSO was associated with improvements in local, segmental, and global measures of sagittal balance, whereas thoracic PSO was only associated with local improvement. Most patients rated their functional status as fair to good according to the modified Prolo Scale and reported, according to the SRS-22 Outcomes Questionnaire, that they were satisfied with the overall treatment of their back condition. The ability to perform a PSO at both lumbar and thoracic levels is a powerful asset for the spine surgeon treating spinal deformity. In the present study radiographic and clinical outcomes were superior when PSO was used to treat lumbar deformity rather than thoracic deformity because of several anatomical and technical obstacles that hindered the thoracic procedure. Nevertheless, the thoracic PSO proved a useful addition with which to produce regional improvement in sagittal balance for patients with a fixed thoracic kyphosis.

An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization.

PubMed

Zou, Feng; Chen, Debao; Wang, Jiangtao

2016-01-01

An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Azmi, Nur Iffah Mohamed; Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Romlay, Fadhlur Rahman Mohd

2018-03-01

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey- Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO- PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.

Chemical Profile and Antioxidant Activity of the Oil from Peony Seeds (Paeonia suffruticosa Andr.)

PubMed Central

Yang, Xin; Song, Li-min; Xu, Qian; Li, Hong

2017-01-01

Peony seed oil (PSO) is a novel vegetable oil developed from the seeds of Paeonia suffruticosa Andr. The present study aimed to make an overall investigation on the chemical profile and antioxidant activities of PSO for reasonable development and utilization of this new resource food. Chemical analysis revealed that PSO was characterized by an uncommon high portion of α-linolenic acid (>38%), fairly low ratio of n-6 to n-3 polyunsaturated fatty acids (0.69), and much higher content of γ-tocopherol than various conventional seed oils. In vitro assay indicated that PSO is a more potent scavenger of free radicals than extra virgin olive oil. Moderate intake of PSO exhibited obvious protection against various oxidative damages such as tetrachloromethane-induced acute liver injury in mice and diet-induced hyperlipidemia in rats. The changes in the key indicators of oxidative injury and fatty acid composition in the liver caused by PSO administration were measured, and the results demonstrated that antioxidant properties of PSO are closely related to their characteristic chemical composition. Consequently, the present study provided new evidence for the health implications of PSO, which deserves further development for medical and nutritional use against oxidative damages that are associated with various diseases. PMID:29081895

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

PubMed

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

An RBF-PSO based approach for modeling prostate cancer

NASA Astrophysics Data System (ADS)

Perracchione, Emma; Stura, Ilaria

2016-06-01

Prostate cancer is one of the most common cancers in men; it grows slowly and it could be diagnosed in an early stage by dosing the Prostate Specific Antigen (PSA). However, a relapse after the primary therapy could arise in 25 - 30% of cases and different growth characteristics of the new tumor are observed. In order to get a better understanding of the phenomenon, a two parameters growth model is considered. To estimate the parameters values identifying the disease risk level a novel approach, based on combining Particle Swarm Optimization (PSO) with meshfree interpolation methods, is proposed.

Extraterrestrial applications of solar optics for interior illumination

NASA Technical Reports Server (NTRS)

Eijadi, David A.; Williams, Kyle D.

1992-01-01

Solar optics is a terrestrial technology that has potential extraterrestrial applications. Active solar optics (ASO) and passive solar optics (PSO) are two approaches to the transmission of sunlight to remote interior spaces. Active solar optics is most appropriate for task illumination, while PSO is most appropriate for general illumination. Research into solar optics, motivated by energy conservation, has produced lightweight and low-cost materials, products that have applications to NASA's Controlled Ecological Life Support System (CELSS) program and its lunar base studies. Specifically, prism light guides have great potential in these contexts. Several applications of solar optics to lunar base concepts are illustrated.

Time-optimal trajectory planning for underactuated spacecraft using a hybrid particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Yufei; Huang, Haibin

2014-02-01

A hybrid algorithm combining particle swarm optimization (PSO) algorithm with the Legendre pseudospectral method (LPM) is proposed for solving time-optimal trajectory planning problem of underactuated spacecrafts. At the beginning phase of the searching process, an initialization generator is constructed by the PSO algorithm due to its strong global searching ability and robustness to random initial values, however, PSO algorithm has a disadvantage that its convergence rate around the global optimum is slow. Then, when the change in fitness function is smaller than a predefined value, the searching algorithm is switched to the LPM to accelerate the searching process. Thus, with the obtained solutions by the PSO algorithm as a set of proper initial guesses, the hybrid algorithm can find a global optimum more quickly and accurately. 200 Monte Carlo simulations results demonstrate that the proposed hybrid PSO-LPM algorithm has greater advantages in terms of global searching capability and convergence rate than both single PSO algorithm and LPM algorithm. Moreover, the PSO-LPM algorithm is also robust to random initial values.

Optimizing Multiple QoS for Workflow Applications using PSO and Min-Max Strategy

NASA Astrophysics Data System (ADS)

Umar Ambursa, Faruku; Latip, Rohaya; Abdullah, Azizol; Subramaniam, Shamala

2017-08-01

Workflow scheduling under multiple QoS constraints is a complicated optimization problem. Metaheuristic techniques are excellent approaches used in dealing with such problem. Many metaheuristic based algorithms have been proposed, that considers various economic and trustworthy QoS dimensions. However, most of these approaches lead to high violation of user-defined QoS requirements in tight situation. Recently, a new Particle Swarm Optimization (PSO)-based QoS-aware workflow scheduling strategy (LAPSO) is proposed to improve performance in such situations. LAPSO algorithm is designed based on synergy between a violation handling method and a hybrid of PSO and min-max heuristic. Simulation results showed a great potential of LAPSO algorithm to handling user requirements even in tight situations. In this paper, the performance of the algorithm is anlysed further. Specifically, the impact of the min-max strategy on the performance of the algorithm is revealed. This is achieved by removing the violation handling from the operation of the algorithm. The results show that LAPSO based on only the min-max method still outperforms the benchmark, even though the LAPSO with the violation handling performs more significantly better.

Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization

NASA Astrophysics Data System (ADS)

Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.

2011-01-01

One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

Demographics, clinical disease characteristics, and quality of life in a large cohort of psoriasis patients with and without psoriatic arthritis

PubMed Central

Truong, B; Rich-Garg, N; Ehst, BD; Deodhar, AA; Ku, JH; Vakil-Gilani, K; Danve, A; Blauvelt, A

2015-01-01

Innovation What is already known about the topic: psoriasis (PsO) is a common skin disease with major impact on quality of life (QoL). Patient-reported data on QoL from large number of PsO patients with and without psoriatic arthritis (PsA) are limited. What this study adds: In a large cohort referred to a university psoriasis center, patients with PsO and concomitant PsA (~30% in this group) had greater degrees of skin and nail involvement and experienced greater negative impacts on QoL. Despite large numbers of patients with moderate-to-severe disease, use of systemic therapy by community practitioners was uncommon. Background PsO and PsA are common diseases that have marked adverse impacts on QoL. The disease features and patient-reported QoL data comparing PsO and PsA patients are limited. Objective To identify and compare demographics, clinical disease characteristics, and QoL scores in a large cohort of PsO patients with and without PsA. Methods All PsO patients seen in a psoriasis specialty clinic, named the Center of Excellence for Psoriasis and Psoriatic Arthritis, were enrolled in an observational cohort. Demographic, QoL, and clinical data were collected from patient-reported questionnaires and from physical examinations performed by Center of Excellence for Psoriasis and Psoriatic Arthritis dermatologists and a rheumatologists. Cross sectional descriptive data were collected and comparisons between patients with PsO alone and those with concomitant PsA are presented. Results A total of 568 patients were enrolled in the database. Mean age of PsO onset was 28 years and mean disease duration was 18 years. Those with family history had an earlier onset of PsO by ~7 years. Mean body surface area involvement with PsO was 14%. Mean body mass index was 30.7. Prevalence of PsA was 29.8%. PsA patients had a higher mean body surface area compared to patients with PsO alone (16.7% vs 13.4%, P<0.05), higher prevalence of psoriatic nail changes (54.4% vs 36%, P<0.0002), and worse QoL scores as assessed by the Short Form-12 (67 vs 52, P<0.00001), Psoriasis Quality of Life-12 questionnaire (62 vs 71, P<0.01), and Routine Assessment of Patient Index Data 3 (2.3 vs 4.7, P<0.01). Strikingly, 49% of patients with PsO had never received any systemic therapy. Conclusion These data highlight that PsO has marked negative impacts on QoL, while those patients with concomitant PsA are affected to a much greater degree. Despite large numbers of patients presenting with moderate-to-severe disease, use of systemic therapy for both PsO and PsA was uncommon. PMID:26622188

A possible role for CD26/DPPIV enzyme activity in the regulation of psoriatic pruritus.

PubMed

Komiya, Eriko; Hatano, Ryo; Otsuka, Haruna; Itoh, Takumi; Yamazaki, Hiroto; Yamada, Taketo; Dang, Nam H; Tominaga, Mitsutoshi; Suga, Yasushi; Kimura, Utako; Takamori, Kenji; Morimoto, Chikao; Ohnuma, Kei

2017-06-01

Psoriasis (PSO) is one of the most common chronic inflammatory skin diseases, and pruritus affects approximately 60-90% of patients with PSO. However, the pathogenesis of pruritus in PSO remains unclear. Dipeptidyl peptidase IV (DPPIV) enzyme activity is involved in the regulation of peptide hormones, chemokines and neurotransmitters. Our aim is to evaluate for a potential association between DPPIV and an increased risk of pruritus, and to identify possible underlying treatment targets in affected patients. Utilizing clinical serum samples of PSO patients and in vivo experimental pruritus models, we evaluated for a potential association between DPPIV and an increased risk for pruritus, and attempted to identify possible underlying treatment targets in pruritus of PSO. We first showed that levels of DPPIV enzyme activity in sera of patients with PSO were significantly increased compared to those of healthy controls. We next evaluated levels of substance-P (SP), which is a neurotransmitter for pruritus and a substrate for DPPIV enzyme. Truncated form SP cleaved by DPPIV was significantly increased in sera of PSO. In an in vivo pruritus model induced by SP, scratching was decreased by treatment with a DPPIV inhibitor. Moreover, DPPIV-knockout mice showed attenuation of scratching induced by SP. Finally, scratching was decreased following the administration of a DPPIV inhibitor in an imiquimod-induced PSO model. On the other hand, scratching induced by imiquimod was increased in DPPIV overexpressing-mice. These results suggest that inhibition of DPPIV enzyme activity regulates pruritus in PSO. Copyright © 2017 Japanese Society for Investigative Dermatology. Published by Elsevier B.V. All rights reserved.

Prevalence of psoriatic arthritis in a large cohort of Brazilian patients with psoriasis.

PubMed

Ranza, Roberto; Carneiro, Sueli; Qureshi, Abrar A; Martins, Gladys; Rodrigues, Jose Joaquim; Romiti, Ricardo; Barros, Thiago Bitar M; Carneiro, Jamille; Sampaio, Ana Luisa; Grynszpan, Rachel; Markus, Juliana; Pinto, Rogerio Melo Costa; Goldenstein-Schainberg, Claudia

2015-05-01

To determine the prevalence of psoriatic arthritis (PsA) in a large cohort of Brazilian patients with psoriasis (PsO) being seen at dermatology centers. A multicenter study was conducted in 4 university dermatology clinics. In each center, consecutive patients with confirmed diagnoses of PsO were evaluated by a rheumatologist. Individuals were classified as having PsA according to the ClASsification criteria for Psoriatic ARthritis (CASPAR). Laboratory tests and radiographs were performed, as needed, based on the clinical judgment of the rheumatologist. A total of 524 patients with PsO were evaluated. The mean age was 48.5 ± 14.5 years, 50% were women, and the mean PsO duration was 15.4 ± 11.7 years. A diagnosis of PsA was documented in 175 patients (33%), of whom 49% were newly identified by the rheumatologist. Most individuals with PsA (72%) had peripheral involvement, 11% had isolated axial involvement, and 17% had both peripheral and axial involvement. Dactylitis occurred in 20% and clinical enthesitis in 30% of the patients. Laboratory and/or radiograph tests were necessary for a definitive diagnosis of PsA in 42 of 175 individuals (24%). In our study, one-third of Brazilian patients with PsO, followed in dermatology settings, were diagnosed with PsA by a rheumatologist. Almost half of subjects with PsA had no previous diagnosis. A collaboration between dermatologists and rheumatologists is greatly needed to establish earlier PsA diagnoses and adequate multidisciplinary management.

42 CFR 3.106 - Security requirements.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., maintenance, storage, removal, disclosure, transmission and destruction. (b) Security framework. A PSO must... subsection. In addressing the framework that follows, the PSO may develop appropriate and scalable security...) Security management. A PSO must address: (i) Maintenance and effective implementation of written policies...

42 CFR 3.106 - Security requirements.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., maintenance, storage, removal, disclosure, transmission and destruction. (b) Security framework. A PSO must... subsection. In addressing the framework that follows, the PSO may develop appropriate and scalable security...) Security management. A PSO must address: (i) Maintenance and effective implementation of written policies...

42 CFR 3.106 - Security requirements.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., maintenance, storage, removal, disclosure, transmission and destruction. (b) Security framework. A PSO must... subsection. In addressing the framework that follows, the PSO may develop appropriate and scalable security...) Security management. A PSO must address: (i) Maintenance and effective implementation of written policies...

Particle Swarm Optimization for inverse modeling of solute transport in fractured gneiss aquifer

NASA Astrophysics Data System (ADS)

Abdelaziz, Ramadan; Zambrano-Bigiarini, Mauricio

2014-08-01

Particle Swarm Optimization (PSO) has received considerable attention as a global optimization technique from scientists of different disciplines around the world. In this article, we illustrate how to use PSO for inverse modeling of a coupled flow and transport groundwater model (MODFLOW2005-MT3DMS) in a fractured gneiss aquifer. In particular, the hydroPSO R package is used as optimization engine, because it has been specifically designed to calibrate environmental, hydrological and hydrogeological models. In addition, hydroPSO implements the latest Standard Particle Swarm Optimization algorithm (SPSO-2011), with an adaptive random topology and rotational invariance constituting the main advancements over previous PSO versions. A tracer test conducted in the experimental field at TU Bergakademie Freiberg (Germany) is used as case study. A double-porosity approach is used to simulate the solute transport in the fractured Gneiss aquifer. Tracer concentrations obtained with hydroPSO were in good agreement with its corresponding observations, as measured by a high value of the coefficient of determination and a low sum of squared residuals. Several graphical outputs automatically generated by hydroPSO provided useful insights to assess the quality of the calibration results. It was found that hydroPSO required a small number of model runs to reach the region of the global optimum, and it proved to be both an effective and efficient optimization technique to calibrate the movement of solute transport over time in a fractured aquifer. In addition, the parallel feature of hydroPSO allowed to reduce the total computation time used in the inverse modeling process up to an eighth of the total time required without using that feature. This work provides a first attempt to demonstrate the capability and versatility of hydroPSO to work as an optimizer of a coupled flow and transport model for contaminant migration.

Treatment of a multiple sclerosis animal model by a novel nanodrop formulation of a natural antioxidant

PubMed Central

Binyamin, Orli; Larush, Liraz; Frid, Kati; Keller, Guy; Friedman-Levi, Yael; Ovadia, Haim; Abramsky, Oded; Magdassi, Shlomo; Gabizon, Ruth

2015-01-01

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system and is associated with demyelination, neurodegeneration, and sensitivity to oxidative stress. In this work, we administered a nanodroplet formulation of pomegranate seed oil (PSO), denominated Nano-PSO, to mice induced for experimental autoimmune encephalomyelitis (EAE), an established model of MS. PSO comprises high levels of punicic acid, a unique polyunsaturated fatty acid considered as one of the strongest natural antioxidants. We show here that while EAE-induced mice treated with natural PSO presented some reduction in disease burden, this beneficial effect increased significantly when EAE mice were treated with Nano-PSO of specific size nanodroplets at much lower concentrations of the oil. Pathological examinations revealed that Nano-PSO administration dramatically reduced demyelination and oxidation of lipids in the brains of the affected animals, which are hallmarks of this severe neurological disease. We propose that novel formulations of natural antioxidants such as Nano-PSO may be considered for the treatment of patients suffering from demyelinating diseases. On the mechanistic side, our results demonstrate that lipid oxidation may be a seminal feature in both demyelination and neurodegeneration. PMID:26648720

A new inertia weight control strategy for particle swarm optimization

NASA Astrophysics Data System (ADS)

Zhu, Xianming; Wang, Hongbo

2018-04-01

Particle Swarm Optimization is a member of swarm intelligence algorithms, which is inspired by the behavior of bird flocks. The inertia weight, one of the most important parameters of PSO, is crucial for PSO, for it balances the performance of exploration and exploitation of the algorithm. This paper proposes a new inertia weight control strategy and PSO with this new strategy is tested by four benchmark functions. The results shows that the new strategy provides the PSO with better performance.

A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities

NASA Astrophysics Data System (ADS)

Muscoloni, Alessandro; Vittorio Cannistraci, Carlo

2018-05-01

The investigation of the hidden metric space behind complex network topologies is a fervid topic in current network science and the hyperbolic space is one of the most studied, because it seems associated to the structural organization of many real complex systems. The popularity-similarity-optimization (PSO) model simulates how random geometric graphs grow in the hyperbolic space, generating realistic networks with clustering, small-worldness, scale-freeness and rich-clubness. However, it misses to reproduce an important feature of real complex networks, which is the community organization. The geometrical-preferential-attachment (GPA) model was recently developed in order to confer to the PSO also a soft community structure, which is obtained by forcing different angular regions of the hyperbolic disk to have a variable level of attractiveness. However, the number and size of the communities cannot be explicitly controlled in the GPA, which is a clear limitation for real applications. Here, we introduce the nonuniform PSO (nPSO) model. Differently from GPA, the nPSO generates synthetic networks in the hyperbolic space where heterogeneous angular node attractiveness is forced by sampling the angular coordinates from a tailored nonuniform probability distribution (for instance a mixture of Gaussians). The nPSO differs from GPA in other three aspects: it allows one to explicitly fix the number and size of communities; it allows one to tune their mixing property by means of the network temperature; it is efficient to generate networks with high clustering. Several tests on the detectability of the community structure in nPSO synthetic networks and wide investigations on their structural properties confirm that the nPSO is a valid and efficient model to generate realistic complex networks with communities.

76 FR 79192 - Patient Safety Organizations: Voluntary Relinquishment From the Georgia Hospital Association...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-21

... The Georgia Hospital Association Research and Education Foundation Patient Safety Organization (GHA... Hospital Association Research and Education Foundation Patient Safety Organization (GHA-PSO), PSO number... and Education Foundation Patient Safety Organization (GHA-PSO) was delisted effective at 12:00...

Intervention of pumpkin seed oil on metabolic disease revealed by metabonomics and transcript profile.

PubMed

Zhao, Xiu-Ju; Chen, Yu-Lian; Fu, Bing; Zhang, Wen; Liu, Zhiguo; Zhuo, Hexian

2017-03-01

Understanding the metabolic and transcription basis of pumpkin seed oil (PSO) intervention on metabolic disease (MD) is essential to daily nutrition and health. This study analyzed the liver metabolic variations of Wistar rats fed normal diet (CON), high-fat diet (HFD) and high-fat plus PSO diet (PSO) to establish the relationship between the liver metabolite composition/transcript profile and the effects of PSO on MD. By using proton nuclear magnetic resonance spectroscopy together with multivariate data analysis, it was found that, compared with CON rats, HFD rats showed clear dysfunctions of choline metabolism, glucose metabolism and nucleotide and amino acid metabolism. Using quantitative real-time polymerase chain reaction (qPCR), it was found that, compared with HFD rats, PSO rats showed alleviated endoplasmic reticulum stress accompanied by lowered unfolded protein response. These findings provide useful information to understand the metabolic alterations triggered by MD and to evaluate the effects of PSO intervention. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Modeling the milling tool wear by using an evolutionary SVM-based model from milling runs experimental data

NASA Astrophysics Data System (ADS)

Nieto, Paulino José García; García-Gonzalo, Esperanza; Vilán, José Antonio Vilán; Robleda, Abraham Segade

2015-12-01

The main aim of this research work is to build a new practical hybrid regression model to predict the milling tool wear in a regular cut as well as entry cut and exit cut of a milling tool. The model was based on Particle Swarm Optimization (PSO) in combination with support vector machines (SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, a PSO-SVM-based model, which is based on the statistical learning theory, was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. To accomplish the objective of this study, the experimental dataset represents experiments from runs on a milling machine under various operating conditions. In this way, data sampled by three different types of sensors (acoustic emission sensor, vibration sensor and current sensor) were acquired at several positions. A second aim is to determine the factors with the greatest bearing on the milling tool flank wear with a view to proposing milling machine's improvements. Firstly, this hybrid PSO-SVM-based regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the flank wear (output variable) and input variables (time, depth of cut, feed, etc.). Indeed, regression with optimal hyperparameters was performed and a determination coefficient of 0.95 was obtained. The agreement of this model with experimental data confirmed its good performance. Secondly, the main advantages of this PSO-SVM-based model are its capacity to produce a simple, easy-to-interpret model, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, the main conclusions of this study are exposed.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

PubMed Central

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987

Automatic PSO-Based Deformable Structures Markerless Tracking in Laparoscopic Cholecystectomy

NASA Astrophysics Data System (ADS)

Djaghloul, Haroun; Batouche, Mohammed; Jessel, Jean-Pierre

An automatic and markerless tracking method of deformable structures (digestive organs) during laparoscopic cholecystectomy intervention that uses the (PSO) behavour and the preoperative a priori knowledge is presented. The associated shape to the global best particles of the population determines a coarse representation of the targeted organ (the gallbladder) in monocular laparoscopic colored images. The swarm behavour is directed by a new fitness function to be optimized to improve the detection and tracking performance. The function is defined by a linear combination of two terms, namely, the human a priori knowledge term (H) and the particle's density term (D). Under the limits of standard (PSO) characteristics, experimental results on both synthetic and real data show the effectiveness and robustness of our method. Indeed, it outperforms existing methods without need of explicit initialization (such as active contours, deformable models and Gradient Vector Flow) on accuracy and convergence rate.

SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm.

PubMed

Wang, Jie-sheng; Li, Shu-xia; Gao, Jie

2014-01-01

For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.

Particle Swarm Optimization of Low-Thrust, Geocentric-to-Halo-Orbit Transfers

NASA Astrophysics Data System (ADS)

Abraham, Andrew J.

Missions to Lagrange points are becoming increasingly popular amongst spacecraft mission planners. Lagrange points are locations in space where the gravity force from two bodies, and the centrifugal force acting on a third body, cancel. To date, all spacecraft that have visited a Lagrange point have done so using high-thrust, chemical propulsion. Due to the increasing availability of low-thrust (high efficiency) propulsive devices, and their increasing capability in terms of fuel efficiency and instantaneous thrust, it has now become possible for a spacecraft to reach a Lagrange point orbit without the aid of chemical propellant. While at any given time there are many paths for a low-thrust trajectory to take, only one is optimal. The traditional approach to spacecraft trajectory optimization utilizes some form of gradient-based algorithm. While these algorithms offer numerous advantages, they also have a few significant shortcomings. The three most significant shortcomings are: (1) the fact that an initial guess solution is required to initialize the algorithm, (2) the radius of convergence can be quite small and can allow the algorithm to become trapped in local minima, and (3) gradient information is not always assessable nor always trustworthy for a given problem. To avoid these problems, this dissertation is focused on optimizing a low-thrust transfer trajectory from a geocentric orbit to an Earth-Moon, L1, Lagrange point orbit using the method of Particle Swarm Optimization (PSO). The PSO method is an evolutionary heuristic that was originally written to model birds swarming to locate hidden food sources. This PSO method will enable the exploration of the invariant stable manifold of the target Lagrange point orbit in an effort to optimize the spacecraft's low-thrust trajectory. Examples of these optimized trajectories are presented and contrasted with those found using traditional, gradient-based approaches. In summary, the results of this dissertation find that the PSO method does, indeed, successfully optimize the low-thrust trajectory transfer problem without the need for initial guessing. Furthermore, a two-degree-of-freedom PSO problem formulation significantly outperformed a one-degree-of-freedom formulation by at least an order of magnitude, in terms of CPU time. Finally, the PSO method is also used to solve a traditional, two-burn, impulsive transfer to a Lagrange point orbit using a hybrid optimization algorithm that incorporates a gradient-based shooting algorithm as a pre-optimizer. Surprisingly, the results of this study show that "fast" transfers outperform "slow" transfers in terms of both Deltav and time of flight.

Fate of SO2 and Particulate SO4 Based on Airborne Measurements in the Oil Sands Region of Alberta, Canada

NASA Astrophysics Data System (ADS)

Hayden, K. L.; Li, S. M.; McLaren, R.; Liu, P.; O'brien, J.; Gordon, M.; Darlington, A.; Liggio, J.; Mittermeier, R. L.; Staebler, R. M.; Makar, P.; Stroud, C.; Akingunola, A.; Leithead, A.; Moussa, S.

2016-12-01

An intensive airborne measurement campaign was undertaken in August to September 2013 to support the objectives of the Joint Oil Sands Monitoring (JOSM) program. The overarching objectives of the study were to characterize air pollutants being emitted, to determine the extent of atmospheric transport and chemical transformation, to support air quality model prediction capabilities, and to compare measurements with satellite column retrievals. Sulphur dioxide (SO2) and particulate sulphate (p-SO4) were among the pollutants studied. SO2 is emitted from elevated stacks within the oil sands facilities and undergoes atmospheric transformation into p-SO4. Deposition of these species from the atmosphere to the surface can lead to impacts on ecosystems downwind of the facilities. The processes of emission, transformation, transport, and deposition of SO2 and p-SO4 were investigated in detail using data collected during aircraft flights that were designed to study pollution transformation. The aircraft was flown at increasing distances downwind of the oil sands facilities, sampling the same plume at different times as it was transported away from the sources. Flight tracks were perpendicular to the wind direction at multiple altitudes to create virtual flight screens that encompassed the entire plume. Fluxes across each of the virtual screens were determined using the wind speed vector normal to the screen and the pollutant concentrations; the flux integration across the two-dimensional plume transect on the screen yielded the pollutant transfer rates at that particular screen location. Transformation of SO2 to p-SO4 between screens was determined based on OH radical levels estimated using concurrently measured concentrations of a suite of hydrocarbons. Based on mass balance between screens using the transfer rates, SO2 oxidation rates and p-SO4 formation rates, the deposition rates of both species are estimated along the plume transport path downwind of the oil sands operations. These observation-derived estimates are compared to corresponding predicted results from a nested air-quality model (GEM-MACH) operating for the same time period.

42 CFR 3.212 - Nonidentification of patient safety work product.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., characteristic, or code except as permitted for re-identification; and (ii) The provider, PSO or responsible... identify the particular provider or reporter. (3) Re-identification. A provider, PSO, or responsible person... under this section to be re-identified by such provider, PSO, or responsible person, provided that: (i...

42 CFR 422.354 - Requirements for affiliated providers.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Requirements for affiliated providers. A PSO that consists of two or more providers must demonstrate to CMS'S... with the PSO's operations; (3) Both, or all, providers are part of a controlled group of corporations... affiliated service group under section 414 of that Code. (b) Each affiliated provider of the PSO shares...

42 CFR 422.356 - Determining substantial financial risk and majority financial interest.

Code of Federal Regulations, 2011 CFR

2011-10-01

...) Determining substantial financial risk. The PSO must demonstrate to CMS's satisfaction that it apportions a significant part of the financial risk of the PSO enterprise under the MA contract to each affiliated provider. The PSO must demonstrate that the financial arrangements among its affiliated providers constitute...

42 CFR 422.356 - Determining substantial financial risk and majority financial interest.

Code of Federal Regulations, 2010 CFR

2010-10-01

...) Determining substantial financial risk. The PSO must demonstrate to CMS's satisfaction that it apportions a significant part of the financial risk of the PSO enterprise under the MA contract to each affiliated provider. The PSO must demonstrate that the financial arrangements among its affiliated providers constitute...

42 CFR 422.384 - Financial plan requirement.

Code of Federal Regulations, 2010 CFR

2010-10-01

... plan. A financial plan must— (1) Cover the first 12 months after the estimated effective date of a PSO's MA contract; or (2) If the PSO is projecting losses, cover 12 months beyond the end of the period... timely manner, in accordance with the PSO's financial plan. (e) Guarantees and projected losses...

42 CFR 422.384 - Financial plan requirement.

Code of Federal Regulations, 2011 CFR

2011-10-01

... plan. A financial plan must— (1) Cover the first 12 months after the estimated effective date of a PSO's MA contract; or (2) If the PSO is projecting losses, cover 12 months beyond the end of the period... timely manner, in accordance with the PSO's financial plan. (e) Guarantees and projected losses...

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.

PubMed

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Wong, Wai Peng; Chen, Chun-Hung

2017-04-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.

Continues administration of Nano-PSO significantly increased survival of genetic CJD mice.

PubMed

Binyamin, Orli; Keller, Guy; Frid, Kati; Larush, Liraz; Magdassi, Shlomo; Gabizon, Ruth

2017-12-01

We have shown previously that Nano-PSO, a nanodroplet formulation of pomegranate seed oil, delayed progression of neurodegeneration signs when administered for a designated period of time to TgMHu2ME199K mice, modeling for genetic prion disease. In the present work, we treated these mice with a self-emulsion formulation of Nano-PSO or a parallel Soybean oil formulation from their day of birth until a terminal disease stage. We found that long term Nano-PSO administration resulted in increased survival of TgMHu2ME199K lines by several months. Interestingly, initiation of treatment at day 1 had no clinical advantage over initiation at day 70, however cessation of treatment at 9months of age resulted in the rapid loss of the beneficial clinical effect. Pathological studies revealed that treatment with Nano-PSO resulted in the reduction of GAG accumulation and lipid oxidation, indicating a strong neuroprotective effect. Contrarily, the clinical effect of Nano-PSO did not correlate with reduction in the levels of disease related PrP, the main prion marker. We conclude that long term administration of Nano-PSO is safe and may be effective in the prevention/delay of onset of neurodegenerative conditions such as genetic CJD. Copyright © 2017. Published by Elsevier Inc.

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization

PubMed Central

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Chen, Chun-Hung

2017-01-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort. PMID:29170617

42 CFR 3.102 - Process and requirements for initial and continued listing of PSOs.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SERVICES GENERAL PROVISIONS PATIENT SAFETY ORGANIZATIONS AND PATIENT SAFETY WORK PRODUCT PSO Requirements... continued listing as a PSO by submitting a completed certification form that meets the requirements of this... delisted; (vi) Attest that the PSO will promptly notify the Secretary during its period of listing if it...

33 CFR 148.228 - What if a formal evidentiary hearing is necessary?

Code of Federal Regulations, 2011 CFR

2011-07-01

... § 148.222 of this part are concluded, the Commandant (G-PSO), in coordination with the MARAD... resolved by a formal evidentiary hearing. (b) If the Commandant (G-PSO), in coordination with the MARAD...) The Commandant (G-PSO) files a request for assignment of an administrative law judge (ALJ) with the...

33 CFR 148.228 - What if a formal evidentiary hearing is necessary?

Code of Federal Regulations, 2010 CFR

2010-07-01

... § 148.222 of this part are concluded, the Commandant (G-PSO), in coordination with the MARAD... resolved by a formal evidentiary hearing. (b) If the Commandant (G-PSO), in coordination with the MARAD...) The Commandant (G-PSO) files a request for assignment of an administrative law judge (ALJ) with the...

An Innovative Method of Teaching Electronic System Design with PSoC

ERIC Educational Resources Information Center

Ye, Zhaohui; Hua, Chengying

2012-01-01

Programmable system-on-chip (PSoC), which provides a microprocessor and programmable analog and digital peripheral functions in a single chip, is very convenient for mixed-signal electronic system design. This paper presents the experience of teaching contemporary mixed-signal electronic system design with PSoC in the Department of Automation,…

Bullying and Quality of Life in Youths Perceived as Gay, Lesbian, or Bisexual in Washington State, 2010

PubMed Central

Bell, Janice F.; Huang, Jon Y.; Lazarakis, Nicholas C.; Edwards, Todd C.

2013-01-01

Objectives. We examined the association between perceived sexual orientation (PSO), bullying, and quality of life (QOL) among US adolescents. Methods. We analyzed data from the 2010 Washington State Healthy Youth Survey collected in public school grades 8, 10, and 12 (n = 27 752). Bullying status was characterized as never bullied, bullied because of PSO, or bullied for other reasons. Survey-weighted regression examined differences in QOL, depressed mood, and consideration of suicide by bullying status. Results. Among male students, 14%, 11%, and 9% reported being bullied because of PSO in 8th, 10th, and 12th grades, respectively; and among female students, 11%, 10%, and 6%. In all gender and grade strata, being bullied because of PSO was associated with lower QOL scores and increased the odds of depressed mood or consideration of suicide. Moreover, the magnitudes of these associations were greater than for being bullied for other reasons. Conclusions. Bullying because of PSO is widely prevalent and significantly affects several facets of youth QOL. Bully-prevention or harm-reduction programs must address bullying because of PSO. PMID:23678925

Bullying and quality of life in youths perceived as gay, lesbian, or bisexual in Washington State, 2010.

PubMed

Patrick, Donald L; Bell, Janice F; Huang, Jon Y; Lazarakis, Nicholas C; Edwards, Todd C

2013-07-01

We examined the association between perceived sexual orientation (PSO), bullying, and quality of life (QOL) among US adolescents. We analyzed data from the 2010 Washington State Healthy Youth Survey collected in public school grades 8, 10, and 12 (n = 27,752). Bullying status was characterized as never bullied, bullied because of PSO, or bullied for other reasons. Survey-weighted regression examined differences in QOL, depressed mood, and consideration of suicide by bullying status. Among male students, 14%, 11%, and 9% reported being bullied because of PSO in 8th, 10th, and 12th grades, respectively; and among female students, 11%, 10%, and 6%. In all gender and grade strata, being bullied because of PSO was associated with lower QOL scores and increased the odds of depressed mood or consideration of suicide. Moreover, the magnitudes of these associations were greater than for being bullied for other reasons. Bullying because of PSO is widely prevalent and significantly affects several facets of youth QOL. Bully-prevention or harm-reduction programs must address bullying because of PSO.

A comparison between metaheuristics as strategies for minimizing cyclic instability in Ambient Intelligence.

PubMed

Romero, Leoncio A; Zamudio, Victor; Baltazar, Rosario; Mezura, Efren; Sotelo, Marco; Callaghan, Vic

2012-01-01

In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO), which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them.

Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO.

PubMed

Fan, Mingyi; Hu, Jiwei; Cao, Rensheng; Xiong, Kangning; Wei, Xionghui

2017-12-21

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites were prepared and then applied in the Cu(II) removal from aqueous solutions. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and superconduction quantum interference device magnetometer were performed to characterize the nZVI/rGO nanocomposites. In order to reduce the number of experiments and the economic cost, response surface methodology (RSM) combined with artificial intelligence (AI) techniques, such as artificial neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), has been utilized as a major tool that can model and optimize the removal processes, because a tremendous advance has recently been made on AI that may result in extensive applications. Based on RSM, ANN-GA and ANN-PSO were employed to model the Cu(II) removal process and optimize the operating parameters, e.g., operating temperature, initial pH, initial concentration and contact time. The ANN-PSO model was proven to be an effective tool for modeling and optimizing the Cu(II) removal with a low absolute error and a high removal efficiency. Furthermore, the isotherm, kinetic, thermodynamic studies and the XPS analysis were performed to explore the mechanisms of Cu(II) removal process.

A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence

PubMed Central

Romero, Leoncio A.; Zamudio, Victor; Baltazar, Rosario; Mezura, Efren; Sotelo, Marco; Callaghan, Vic

2012-01-01

In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO), which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them. PMID:23112643

An improved input shaping design for an efficient sway control of a nonlinear 3D overhead crane with friction

NASA Astrophysics Data System (ADS)

Maghsoudi, Mohammad Javad; Mohamed, Z.; Sudin, S.; Buyamin, S.; Jaafar, H. I.; Ahmad, S. M.

2017-08-01

This paper proposes an improved input shaping scheme for an efficient sway control of a nonlinear three dimensional (3D) overhead crane with friction using the particle swarm optimization (PSO) algorithm. Using this approach, a higher payload sway reduction is obtained as the input shaper is designed based on a complete nonlinear model, as compared to the analytical-based input shaping scheme derived using a linear second order model. Zero Vibration (ZV) and Distributed Zero Vibration (DZV) shapers are designed using both analytical and PSO approaches for sway control of rail and trolley movements. To test the effectiveness of the proposed approach, MATLAB simulations and experiments on a laboratory 3D overhead crane are performed under various conditions involving different cable lengths and sway frequencies. Their performances are studied based on a maximum residual of payload sway and Integrated Absolute Error (IAE) values which indicate total payload sway of the crane. With experiments, the superiority of the proposed approach over the analytical-based is shown by 30-50% reductions of the IAE values for rail and trolley movements, for both ZV and DZV shapers. In addition, simulations results show higher sway reductions with the proposed approach. It is revealed that the proposed PSO-based input shaping design provides higher payload sway reductions of a 3D overhead crane with friction as compared to the commonly designed input shapers.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

PubMed

Subasi, Abdulhamit

2013-06-01

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. Copyright © 2013 Elsevier Ltd. All rights reserved.

Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2015-10-01

Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.

An evolutionary computation based algorithm for calculating solar differential rotation by automatic tracking of coronal bright points

NASA Astrophysics Data System (ADS)

Shahamatnia, Ehsan; Dorotovič, Ivan; Fonseca, Jose M.; Ribeiro, Rita A.

2016-03-01

Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a prototype of a modular framework for solar feature detection, characterization, and tracking is presented. To develop an efficient system capable of automatic solar feature tracking and measuring, a hybrid approach combining specialized image processing, evolutionary optimization, and soft computing algorithms is being followed. The specialized hybrid algorithm for tracking solar features allows automatic feature tracking while gathering characterization details about the tracked features. The hybrid algorithm takes advantages of the snake model, a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. Further, it exploits the flexibility and efficiency of Particle Swarm Optimization (PSO), a stochastic population based optimization algorithm. PSO has been used successfully in a wide range of applications including combinatorial optimization, control, clustering, robotics, scheduling, and image processing and video analysis applications. The proposed tool, denoted PSO-Snake model, was already successfully tested in other works for tracking sunspots and coronal bright points. In this work, we discuss the application of the PSO-Snake algorithm for calculating the sidereal rotational angular velocity of the solar corona. To validate the results we compare them with published manual results performed by an expert.

Swarm size and iteration number effects to the performance of PSO algorithm in RFID tag coverage optimization

NASA Astrophysics Data System (ADS)

Prathabrao, M.; Nawawi, Azli; Sidek, Noor Azizah

2017-04-01

Radio Frequency Identification (RFID) system has multiple benefits which can improve the operational efficiency of the organization. The advantages are the ability to record data systematically and quickly, reducing human errors and system errors, update the database automatically and efficiently. It is often more readers (reader) is needed for the installation purposes in RFID system. Thus, it makes the system more complex. As a result, RFID network planning process is needed to ensure the RFID system works perfectly. The planning process is also considered as an optimization process and power adjustment because the coordinates of each RFID reader to be determined. Therefore, algorithms inspired by the environment (Algorithm Inspired by Nature) is often used. In the study, PSO algorithm is used because it has few number of parameters, the simulation time is fast, easy to use and also very practical. However, PSO parameters must be adjusted correctly, for robust and efficient usage of PSO. Failure to do so may result in disruption of performance and results of PSO optimization of the system will be less good. To ensure the efficiency of PSO, this study will examine the effects of two parameters on the performance of PSO Algorithm in RFID tag coverage optimization. The parameters to be studied are the swarm size and iteration number. In addition to that, the study will also recommend the most optimal adjustment for both parameters that is, 200 for the no. iterations and 800 for the no. of swarms. Finally, the results of this study will enable PSO to operate more efficiently in order to optimize RFID network planning system.

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods.

PubMed

Li, Mengshan; Zhang, Huaijing; Chen, Bingsheng; Wu, Yan; Guan, Lixin

2018-03-05

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

Retention in porous layer pillar array planar separation platforms

DOE PAGES

Lincoln, Danielle R.; Lavrik, Nickolay V.; Kravchenko, Ivan I.; ...

2016-08-11

Here, this work presents the retention capabilities and surface area enhancement of highly ordered, high-aspect-ratio, open-platform, two-dimensional (2D) pillar arrays when coated with a thin layer of porous silicon oxide (PSO). Photolithographically prepared pillar arrays were coated with 50–250 nm of PSO via plasma-enhanced chemical vapor deposition and then functionalized with either octadecyltrichlorosilane or n-butyldimethylchlorosilane. Theoretical calculations indicate that a 50 nm layer of PSO increases the surface area of a pillar nearly 120-fold. Retention capabilities were tested by observing capillary-action-driven development under various conditions, as well as by running one-dimensional separations on varying thicknesses of PSO. Increasing the thicknessmore » of PSO on an array clearly resulted in greater retention of the analyte(s) in question in both experiments. In culmination, a two-dimensional separation of fluorescently derivatized amines was performed to further demonstrate the capabilities of these fabricated platforms.« less

Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.

PubMed

Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L

2017-02-01

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

Retention in porous layer pillar array planar separation platforms

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

Lincoln, Danielle R.; Lavrik, Nickolay V.; Kravchenko, Ivan I.

Here, this work presents the retention capabilities and surface area enhancement of highly ordered, high-aspect-ratio, open-platform, two-dimensional (2D) pillar arrays when coated with a thin layer of porous silicon oxide (PSO). Photolithographically prepared pillar arrays were coated with 50–250 nm of PSO via plasma-enhanced chemical vapor deposition and then functionalized with either octadecyltrichlorosilane or n-butyldimethylchlorosilane. Theoretical calculations indicate that a 50 nm layer of PSO increases the surface area of a pillar nearly 120-fold. Retention capabilities were tested by observing capillary-action-driven development under various conditions, as well as by running one-dimensional separations on varying thicknesses of PSO. Increasing the thicknessmore » of PSO on an array clearly resulted in greater retention of the analyte(s) in question in both experiments. In culmination, a two-dimensional separation of fluorescently derivatized amines was performed to further demonstrate the capabilities of these fabricated platforms.« less

76 FR 77859 - Self-Regulatory Organizations; NYSE Arca, Inc.; Notice of Filing and Immediate Effectiveness of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-14

... only be applicable to PSOs that remove liquidity from the NYSE and that a PSO that provides liquidity...(kk). A PSO is a Primary Only (``PO'') Order that initially sweeps the Exchange's Book before being routed to the security's primary market. \\11\\ In limited circumstances where a PSO in a Tape A security...

76 FR 34788 - Self-Regulatory Organizations; NYSE Arca, Inc.; Notice of Filing and Immediate Effectiveness of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-14

... routing fee applies as noted in the table. The Primary Sweep Order (PSO) is a market or limit order that... a PSO designation should be marketable. Non-marketable orders will function as regular limit orders... Primary Sweep Order (PSO) is a market or limit order that sweeps the NYSE Arca Book and routes any...

A novel discrete PSO algorithm for solving job shop scheduling problem to minimize makespan

NASA Astrophysics Data System (ADS)

Rameshkumar, K.; Rajendran, C.

2018-02-01

In this work, a discrete version of PSO algorithm is proposed to minimize the makespan of a job-shop. A novel schedule builder has been utilized to generate active schedules. The discrete PSO is tested using well known benchmark problems available in the literature. The solution produced by the proposed algorithms is compared with best known solution published in the literature and also compared with hybrid particle swarm algorithm and variable neighborhood search PSO algorithm. The solution construction methodology adopted in this study is found to be effective in producing good quality solutions for the various benchmark job-shop scheduling problems.

Protective effect of pomegranate seed oil against H2O2 -induced oxidative stress in cardiomyocytes

PubMed Central

Bihamta, Mehdi; Hosseini, Azar; Ghorbani, Ahmad; Boroushaki, Mohammad Taher

2017-01-01

Objective: It has been well documented that oxidative stress is involved in the pathogenesis of cardiac diseases. Previous studies have shown that pomegranate seed oil (PSO) has antioxidant properties. This study was designed to investigate probable protective effects of PSO against hydrogen peroxide (H2O2)-induced damage in H9c2 cardiomyocytes. Materials and Methods: The cells were pretreated 24 hr with PSO 1 hr before exposure to 200 µM H2O2. Cell viability was evaluated using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium (MTT) assay. The level of reactive oxygen species (ROS) and lipid peroxidation were measured by fluorimetric methods. Results: H2O2 significantly decreased cell viability which was accompanied by an increase in ROS production and lipid peroxidation and a decline in superoxide dismutase activity. Pretreatment with PSO increased viability of cardiomyocytes and decrease the elevated ROS production and lipid peroxidation. Also, PSO was able to restore superoxide dismutase activity. Conclusion: PSO has protective effect against oxidative stress-induced damage in cardiomyocytes and can be considered as a natural cardioprotective agent to prevent cardiovascular diseases. PMID:28265546

A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms

PubMed Central

Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei

2016-01-01

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. PMID:26861348

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

The Prediction of the Gas Utilization Ratio Based on TS Fuzzy Neural Network and Particle Swarm Optimization

PubMed Central

Jiang, Haihe; Yin, Yixin; Xiao, Wendong; Zhao, Baoyong

2018-01-01

Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control. PMID:29461469

The Prediction of the Gas Utilization Ratio based on TS Fuzzy Neural Network and Particle Swarm Optimization.

PubMed

Zhang, Sen; Jiang, Haihe; Yin, Yixin; Xiao, Wendong; Zhao, Baoyong

2018-02-20

Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.

10Gbps 2D MGC OCDMA Code over FSO Communication System

NASA Astrophysics Data System (ADS)

Professor Urmila Bhanja, Associate, Dr.; Khuntia, Arpita; Alamasety Swati, (Student

2017-08-01

Currently, wide bandwidth signal dissemination along with low latency is a leading requisite in various applications. Free space optical wireless communication has introduced as a realistic technology for bridging the gap in present high data transmission fiber connectivity and as a provisional backbone for rapidly deployable wireless communication infrastructure. The manuscript highlights on the implementation of 10Gbps SAC-OCDMA FSO communications using modified two dimensional Golomb code (2D MGC) that possesses better auto correlation, minimum cross correlation and high cardinality. A comparison based on pseudo orthogonal (PSO) matrix code and modified two dimensional Golomb code (2D MGC) is developed in the proposed SAC OCDMA-FSO communication module taking different parameters into account. The simulative outcome signifies that the communication radius is bounded by the multiple access interference (MAI). In this work, a comparison is made in terms of bit error rate (BER), and quality factor (Q) based on modified two dimensional Golomb code (2D MGC) and PSO matrix code. It is observed that the 2D MGC yields better results compared to the PSO matrix code. The simulation results are validated using optisystem version 14.

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

PubMed

Bao, Yukun; Xiong, Tao; Hu, Zhongyi

2014-05-01

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

Tunning PID controller using particle swarm optimization algorithm on automatic voltage regulator system

NASA Astrophysics Data System (ADS)

Aranza, M. F.; Kustija, J.; Trisno, B.; Hakim, D. L.

2016-04-01

PID Controller (Proportional Integral Derivative) was invented since 1910, but till today still is used in industries, even though there are many kind of modern controllers like fuzz controller and neural network controller are being developed. Performance of PID controller is depend on on Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). These gains can be got by using method Ziegler-Nichols (ZN), gain-phase margin, Root Locus, Minimum Variance dan Gain Scheduling however these methods are not optimal to control systems that nonlinear and have high-orde, in addition, some methods relative hard. To solve those obstacles, particle swarm optimization (PSO) algorithm is proposed to get optimal Kp, Ki and Kd. PSO is proposed because PSO has convergent result and not require many iterations. On this research, PID controller is applied on AVR (Automatic Voltage Regulator). Based on result of analyzing transient, stability Root Locus and frequency response, performance of PID controller is better than Ziegler-Nichols.

Comparison result of inversion of gravity data of a fault by particle swarm optimization and Levenberg-Marquardt methods.

PubMed

Toushmalani, Reza

2013-01-01

The purpose of this study was to compare the performance of two methods for gravity inversion of a fault. First method [Particle swarm optimization (PSO)] is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. Second method [The Levenberg-Marquardt algorithm (LM)] is an approximation to the Newton method used also for training ANNs. In this paper first we discussed the gravity field of a fault, then describes the algorithms of PSO and LM And presents application of Levenberg-Marquardt algorithm, and a particle swarm algorithm in solving inverse problem of a fault. Most importantly the parameters for the algorithms are given for the individual tests. Inverse solution reveals that fault model parameters are agree quite well with the known results. A more agreement has been found between the predicted model anomaly and the observed gravity anomaly in PSO method rather than LM method.

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

PubMed

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

PSO/ACO algorithm-based risk assessment of human neural tube defects in Heshun County, China.

PubMed

Liao, Yi Lan; Wang, Jin Feng; Wu, Ji Lei; Wang, Jiao Jiao; Zheng, Xiao Ying

2012-10-01

To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world. The region of interest in this study was Heshun County, the county in China with the highest rate of neural tube defects (NTDs). A hybrid particle swarm optimization/ant colony optimization (PSO/ACO) algorithm was used to quantify the probability of NTDs occurring at villages with no births. The hybrid PSO/ACO algorithm is a form of artificial intelligence adapted for hierarchical classification. It is a powerful technique for modeling complex problems involving impacts of causes. The algorithm was easy to apply, with the accuracy of the results being 69.5%±7.02% at the 95% confidence level. The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations. Copyright © 2012 The Editorial Board of Biomedical and Environmental Sciences. Published by Elsevier B.V. All rights reserved.

PSO4: a novel gene involved in error-prone repair in Saccharomyces cerevisiae.

PubMed

Henriques, J A; Vicente, E J; Leandro da Silva, K V; Schenberg, A C

1989-09-01

The haploid xs9 mutant, originally selected for on the basis of a slight sensitivity to the lethal effect of X-rays, was found to be extremely sensitive to inactivation by 8-methoxypsoralen (8MOP) photoaddition, especially when cells are treated in the G2 phase of the cell cycle. As the xs9 mutation showed no allelism with any of the 3 known pso mutations, it was now given the name of pso4-1. Regarding inactivation, the pso4-1 mutant is also sensitive to mono- (HN1) or bi-functional (HN2) nitrogen mustards, it is slightly sensitive to 254 nm UV radiation (UV), and shows nearly normal sensitivity to 3-carbethoxypsoralen (3-CPs) photoaddition or methyl methanesulfonate (MMS). Regarding mutagenesis, the pso4-1 mutation completely blocks reverse and forward mutations induced by either 8MOP or 3CPs photoaddition, or by gamma-rays. In the cases of UV, HN1, HN2 or MMS treatments, while reversion induction is still completely abolished, forward mutagenesis is only partially inhibited for UV, HN1, or MMS, and it is unaffected for HN2. Besides severely inhibiting induced mutagenesis, the pso4-1 mutation was found to be semi-dominant, to block sporulation, to abolish the diploid resistance effect, and to block induced mitotic recombination, which indicates that the PSO4 gene is involved in a recombinational pathway of error-prone repair, comparable to the E. coli SOS repair pathway.

Hybrid intelligent optimization methods for engineering problems

NASA Astrophysics Data System (ADS)

Pehlivanoglu, Yasin Volkan

The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients of these increments are essential to search the yield space. However, the landscape of yield is not a simple one and mostly multi-modal. Another issue is differentiability. Engineering design problems are usually nonlinear and they sometimes exhibit discontinuous derivatives for the objective and constraint functions. Due to these difficulties, non-gradient-based algorithms have become more popular in recent decades. Genetic algorithms (GA) and particle swarm optimization (PSO) algorithms are popular, non-gradient based algorithms. Both are population-based search algorithms and have multiple points for initiation. A significant difference from a gradient-based method is the nature of the search methodologies. For example, randomness is essential for the search in GA or PSO. Hence, they are also called stochastic optimization methods. These algorithms are simple, robust, and have high fidelity. However, they suffer from similar defects, such as, premature convergence, less accuracy, or large computational time. The premature convergence is sometimes inevitable due to the lack of diversity. As the generations of particles or individuals in the population evolve, they may lose their diversity and become similar to each other. To overcome this issue, we studied the diversity concept in GA and PSO algorithms. Diversity is essential for a healthy search, and mutations are the basic operators to provide the necessary variety within a population. After having a close scrutiny of the diversity concept based on qualification and quantification studies, we improved new mutation strategies and operators to provide beneficial diversity within the population. We called this new approach as multi-frequency vibrational GA or PSO. They were applied to different aeronautical engineering problems in order to study the efficiency of these new approaches. These implementations were: applications to selected benchmark test functions, inverse design of two-dimensional (2D) airfoil in subsonic flow, optimization of 2D airfoil in transonic flow, path planning problems of autonomous unmanned aerial vehicle (UAV) over a 3D terrain environment, 3D radar cross section minimization problem for a 3D air vehicle, and active flow control over a 2D airfoil. As demonstrated by these test cases, we observed that new algorithms outperform the current popular algorithms. The principal role of this multi-frequency approach was to determine which individuals or particles should be mutated, when they should be mutated, and which ones should be merged into the population. The new mutation operators, when combined with a mutation strategy and an artificial intelligent method, such as, neural networks or fuzzy logic process, they provided local and global diversities during the reproduction phases of the generations. Additionally, the new approach also introduced random and controlled diversity. Due to still being population-based techniques, these methods were as robust as the plain GA or PSO algorithms. Based on the results obtained, it was concluded that the variants of the present multi-frequency vibrational GA and PSO were efficient algorithms, since they successfully avoided all local optima within relatively short optimization cycles.

Performance of Multi-chaotic PSO on a shifted benchmark functions set

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

Pluhacek, Michal; Senkerik, Roman; Zelinka, Ivan

2015-03-10

In this paper the performance of Multi-chaotic PSO algorithm is investigated using two shifted benchmark functions. The purpose of shifted benchmark functions is to simulate the time-variant real-world problems. The results of chaotic PSO are compared with canonical version of the algorithm. It is concluded that using the multi-chaotic approach can lead to better results in optimization of shifted functions.

Solubilization of docetaxel in poly(ethylene oxide)-block-poly(butylene/styrene oxide) micelles.

PubMed

Elsabahy, Mahmoud; Perron, Marie-Eve; Bertrand, Nicolas; Yu, Ga-Er; Leroux, Jean-Christophe

2007-07-01

Poly(ethylene oxide)-block-poly(styrene oxide) (PEO-b-PSO) and PEO-b-poly(butylene oxide) (PEO-b-PBO) of different chain lengths were synthesized and characterized for their self-assembling properties in water by dynamic/static light scattering, spectrofluorimetry, and transmission electron microscopy. The resulting polymeric micelles were evaluated for their ability to solubilize and protect the anticancer drug docetaxel (DCTX) from degradation. The drug release kinetics as well as the cytotoxicity of the loaded micelles were assessed in vitro. All polymers formed micelles with a highly viscous core at low critical association concentrations (<10 mg/L). Micelle morphology depended on the nature of the hydrophobic block, with PBO- and PSO-based micelles yielding monodisperse spherical and cylindrical nanosized aggregates, respectively. The maximum solubilization capacity for DCTX ranged from 0.7 to 4.2% and was the highest for PSO micelles exhibiting the longest hydrophobic segment. Despite their high affinity for DCTX, PEO-b-PSO micelles were not able to efficiently protect DCTX against hydrolysis under accelerated stability testing conditions. Only PEO-b-PBO bearing 24 BO units afforded significant protection against degradation. In vitro, DCTX was released slower from the latter micelles, but all formulations possessed a similar cytotoxic effect against PC-3 prostate cancer cells. These data suggest that PEO-b-P(SO/BO) micelles could be used as alternatives to conventional surfactants for the solubilization of taxanes.

Assessment of kinetic models on Fe adsorption in groundwater using high-quality limestone

NASA Astrophysics Data System (ADS)

Akbar, N. A.; Kamil, N. A. F. Mohd; Zin, N. S. Md; Adlan, M. N.; Aziz, H. A.

2018-04-01

During the groundwater pumping process, dissolved Fe2+ is oxidized into Fe3+ and produce rust-coloured iron mineral. Adsorption kinetic models are used to evaluate the performance of limestone adsorbent and describe the mechanism of adsorption and the diffusion processes of Fe adsorption in groundwater. This work presents the best kinetic model of Fe adsorption, which was chosen based on a higher value of coefficient correlation, R2. A batch adsorption experiment was conducted for various contact times ranging from 0 to 135 minutes. From the results of the batch study, three kinetic models were analyzed for Fe removal onto limestone sorbent, including the pseudo-first order (PFO), pseudo-second order (PSO) and intra-particle diffusion (IPD) models. Results show that the adsorption kinetic models follow the sequence: PSO > PFO > IPD, where the values of R2 are 0.997 > 0.919 > 0.918. A high value of R2 (0.997) reveals better fitted experimental data. Furthermore, the value of qe cal in the PSO kinetic model is very near to qe exp rather than that in other models. This finding therefore suggests that the PSO kinetic model has the good fitted with the experimental data which involved chemisorption process of divalent Fe removal in groundwater solution. Thus, limestone adsorbent media found to be an alternative and effective treatment of Fe removal from groundwater.

Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.

PubMed

Qiu, Jiaheng; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee

2014-10-01

Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.

Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting

PubMed Central

Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.

2016-01-01

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048

Evaluation of multilayer perceptron algorithms for an analysis of network flow data

NASA Astrophysics Data System (ADS)

Bieniasz, Jedrzej; Rawski, Mariusz; Skowron, Krzysztof; Trzepiński, Mateusz

2016-09-01

The volume of exchanged information through IP networks is larger than ever and still growing. It creates a space for both benign and malicious activities. The second one raises awareness on security network devices, as well as network infrastructure and a system as a whole. One of the basic tools to prevent cyber attacks is Network Instrusion Detection System (NIDS). NIDS could be realized as a signature-based detector or an anomaly-based one. In the last few years the emphasis has been placed on the latter type, because of the possibility of applying smart and intelligent solutions. An ideal NIDS of next generation should be composed of self-learning algorithms that could react on known and unknown malicious network activities respectively. In this paper we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms - Backpropagation (BP) and Particle Swarm Optimization (PSO). This paper includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.

Effect of Pumpkin Seed Oil on Hair Growth in Men with Androgenetic Alopecia: A Randomized, Double-Blind, Placebo-Controlled Trial

PubMed Central

Jeong, Dong Wook; Choi, Eun Jung; Kim, Yun Jin; Lee, Jeong Gyu; Yi, Yu Hyeon; Cha, Hyeong Soo

2014-01-01

Pumpkin seed oil (PSO) has been shown to block the action of 5-alpha reductase and to have antiandrogenic effects on rats. This randomized, placebo-controlled, double-blind study was designed to investigate the efficacy and tolerability of PSO for treatment of hair growth in male patients with mild to moderate androgenetic alopecia (AGA). 76 male patients with AGA received 400 mg of PSO per day or a placebo for 24 weeks. Change over time in scalp hair growth was evaluated by four outcomes: assessment of standardized clinical photographs by a blinded investigator; patient self-assessment scores; scalp hair thickness; and scalp hair counts. Reports of adverse events were collected throughout the study. After 24 weeks of treatment, self-rated improvement score and self-rated satisfaction scores in the PSO-treated group were higher than in the placebo group (P = 0.013, 0.003). The PSO-treated group had more hair after treatment than at baseline, compared to the placebo group (P < 0.001). Mean hair count increases of 40% were observed in PSO-treated men at 24 weeks, whereas increases of 10% were observed in placebo-treated men (P < 0.001). Adverse effects were not different in the two groups. PMID:24864154

Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach.

PubMed

Cakar, Tarik; Koker, Rasit

2015-01-01

A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

Inhibition of autophagy prevents cadmium-induced prostate carcinogenesis.

PubMed

Pal, Deeksha; Suman, Suman; Kolluru, Venkatesh; Sears, Sophia; Das, Trinath P; Alatassi, Houda; Ankem, Murali K; Freedman, Jonathan H; Damodaran, Chendil

2017-06-27

Cadmium, an established carcinogen, is a risk factor for prostate cancer. Induction of autophagy is a prerequisite for cadmium-induced transformation and metastasis. The ability of Psoralidin (Pso), a non-toxic, orally bioavailable compound to inhibit cadmium-induced autophagy to prevent prostate cancer was investigated. Psoralidin was studied using cadmium-transformed prostate epithelial cells (CTPE), which exhibit high proliferative, invasive and colony forming abilities. Gene and protein expression were evaluated by qPCR, western blot, immunohistochemistry and immunofluorescence. Xenograft models were used to study the chemopreventive effects in vivo. Cadmium-transformed prostate epithelial cells were treated with Pso resulting in growth inhibition, without causing toxicity to normal prostate epithelial cells (RWPE-1). Psoralidin-treatment of CTPE cells inhibited the expression of Placenta Specific 8, a lysosomal protein essential for autophagosome and autolysosome fusion, which resulted in growth inhibition. Additionally, Pso treatment caused decreased expression of pro-survival signalling proteins, NFκB and Bcl2, and increased expression of apoptotic genes. In vivo, Pso effectively suppressed CTPE xenografts growth, without any observable toxicity. Tumours from Pso-treated animals showed decreased autophagic morphology, mesenchymal markers expression and increased epithelial protein expression. These results confirm that inhibition of autophagy by Pso plays an important role in the chemoprevention of cadmium-induced prostate carcinogenesis.

Effects of Pomegranate Seed Oil on Metabolic State of Patients with Type 2 Diabetes Mellitus.

PubMed

Faghihimani, Zahra; Mirmiran, Parvin; Sohrab, Golbon; Iraj, Bijan; Faghihimani, Elham

2016-01-01

Rapid increasing prevalence of diabetes mellitus is a serious health concern in the world. New data determined that the pathogenesis of diabetes mellitus is chronic low-grade inflammation, resulting insulin resistance. Pomegranate seed oil (PSO) has anti-inflammatory effects; though it may reduce insulin resistance and improve glycemia in diabetes mellitus. The present study has been designed to investigate the effects of PSO as a natural dietary component on metabolic state of patients with Type 2 diabetes mellitus. In a randomized double-blind clinical trial study, 80 patients (28 men) with Type 2 diabetes were randomly allocated to the intervention and control groups. The intervention group consumed PSO capsules, containing 1000 mg PSO twice daily (2000 mg PSO), whereas controls take placebo for 8 weeks. The participants followed their previous dietary patterns and medication use. Dietary factors and metabolic factors including lipid profile, fasting plasma sugar, and insulin and were assayed at the baseline and after 8 weeks. Participants in two intervention and control group were similar regarding anthropometric and the dietary factors at baseline and after trial ( P > 0.05). Mean level of total cholesterol, triglyceride, low-density lipoprotein-cholesterol, and high-density lipoprotein was not different significantly between groups after trial ( P > 0.05). Consumption of PSO did not significantly affect the levels of parameters such as fasting blood sugar (FBS), insulin, HbA1c, alanine transferase, and homeostasis model assessment-insulin resistance. Consumption of 2000mg PSO per day for 8 weeks had no effect on FBS, insulin resistance and lipid profile in diabetic patients.

Pharmacy Service Orientation: a measure of organizational culture in pharmacy practice sites.

PubMed

Clark, Bartholomew E; Mount, Jeanine K

2006-03-01

The importance of organizational culture in shaping everyday organizational life is well accepted, but little work has focused on organizational culture in pharmacy. Examining new pharmacists' experiences at various practice sites may help us to understand how these shape their professional ethos and practice habits. (1) Present development and assessment of the Pharmacy Service Orientation (PSO) measure, a tool for assessing pharmacists' impressions of pharmacy practice sites. (2) Use data gathered from a sample of new pharmacists to explore potential predictors of PSO, including type of practice site, type of pharmacy work experience, and type of pharmacy degree. Mail survey of randomly selected class of 1999 pharmacy graduates within 3 months of graduation (response rate: 259 of 1,850; 14%), each of whom reported on up to 6 different pharmacy practice sites for a total of 1,192 pharmacy observations. Pharmacy Service Orientation is scored on a 1-10 semantic differential scale and reliability was assessed using Cronbach's alpha. Predictors of PSO were explored using t test and ordinary least squares regression procedures. Reliability of the PSO across all observations was 0.86. When divided according to recency of experience and type of experience, reliabilities ranged from 0.78 to 0.87. Analysis of potential predictors of PSO showed that non-corporate-community sites had significantly greater pharmaceutical care-oriented cultures (mean PSOs of 7.42 and 5.13, respectively; P<.001). The same pattern was seen for academic and nonacademic worksites (mean PSOs of 7.46 and 6.01, respectively; P<.001). The pharmacist's pharmacy degree type was not predictive of PSO. Multivariate regression results showed that type of practice site and type of pharmacy work experience explained more than 25% of the observed variance in PSO. Pharmacy Service Orientation is a reliable measure. Statistically significant differences in PSO comparisons by degree and by experience type are explained by significant differences between the PSOs of corporate-community and non-corporate-community sites.

Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines

PubMed Central

Xu, Jingjing; Yang, Wei; Zhang, Linyuan; Han, Ruisong; Shao, Xiaotao

2015-01-01

In this paper, a wireless sensor network (WSN) technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA) to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI) arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD) algorithm with particle swarm optimization (PSO), namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER) performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization. PMID:26343660

Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines.

PubMed

Xu, Jingjing; Yang, Wei; Zhang, Linyuan; Han, Ruisong; Shao, Xiaotao

2015-08-27

In this paper, a wireless sensor network (WSN) technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA) to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI) arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD) algorithm with particle swarm optimization (PSO), namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER) performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization.

Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2016-01-01

Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

NASA Astrophysics Data System (ADS)

Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

2018-03-01

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

Color Image Enhancement Using Multiscale Retinex Based on Particle Swarm Optimization Method

NASA Astrophysics Data System (ADS)

Matin, F.; Jeong, Y.; Kim, K.; Park, K.

2018-01-01

This paper introduces, a novel method for the image enhancement using multiscale retinex and practical swarm optimization. Multiscale retinex is widely used image enhancement technique which intemperately pertains on parameters such as Gaussian scales, gain and offset, etc. To achieve the privileged effect, the parameters need to be tuned manually according to the image. In order to handle this matter, a developed retinex algorithm based on PSO has been used. The PSO method adjusted the parameters for multiscale retinex with chromaticity preservation (MSRCP) attains better outcome to compare with other existing methods. The experimental result indicates that the proposed algorithm is an efficient one and not only provides true color loyalty in low light conditions but also avoid color distortion at the same time.

Energy aware swarm optimization with intercluster search for wireless sensor network.

PubMed

Thilagavathi, Shanmugasundaram; Geetha, Bhavani Gnanasambandan

2015-01-01

Wireless sensor networks (WSNs) are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO) algorithm with modified connected dominating set (CDS) based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH). Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

A parallel competitive Particle Swarm Optimization for non-linear first arrival traveltime tomography and uncertainty quantification

NASA Astrophysics Data System (ADS)

Luu, Keurfon; Noble, Mark; Gesret, Alexandrine; Belayouni, Nidhal; Roux, Pierre-François

2018-04-01

Seismic traveltime tomography is an optimization problem that requires large computational efforts. Therefore, linearized techniques are commonly used for their low computational cost. These local optimization methods are likely to get trapped in a local minimum as they critically depend on the initial model. On the other hand, global optimization methods based on MCMC are insensitive to the initial model but turn out to be computationally expensive. Particle Swarm Optimization (PSO) is a rather new global optimization approach with few tuning parameters that has shown excellent convergence rates and is straightforwardly parallelizable, allowing a good distribution of the workload. However, while it can traverse several local minima of the evaluated misfit function, classical implementation of PSO can get trapped in local minima at later iterations as particles inertia dim. We propose a Competitive PSO (CPSO) to help particles to escape from local minima with a simple implementation that improves swarm's diversity. The model space can be sampled by running the optimizer multiple times and by keeping all the models explored by the swarms in the different runs. A traveltime tomography algorithm based on CPSO is successfully applied on a real 3D data set in the context of induced seismicity.

Spatiotemporal dynamics in excitable homogeneous random networks composed of periodically self-sustained oscillation.

PubMed

Qian, Yu; Liu, Fei; Yang, Keli; Zhang, Ge; Yao, Chenggui; Ma, Jun

2017-09-19

The collective behaviors of networks are often dependent on the network connections and bifurcation parameters, also the local kinetics plays an important role in contributing the consensus of coupled oscillators. In this paper, we systematically investigate the influence of network structures and system parameters on the spatiotemporal dynamics in excitable homogeneous random networks (EHRNs) composed of periodically self-sustained oscillation (PSO). By using the dominant phase-advanced driving (DPAD) method, the one-dimensional (1D) Winfree loop is exposed as the oscillation source supporting the PSO, and the accurate wave propagation pathways from the oscillation source to the whole network are uncovered. Then, an order parameter is introduced to quantitatively study the influence of network structures and system parameters on the spatiotemporal dynamics of PSO in EHRNs. Distinct results induced by the network structures and the system parameters are observed. Importantly, the corresponding mechanisms are revealed. PSO influenced by the network structures are induced not only by the change of average path length (APL) of network, but also by the invasion of 1D Winfree loop from the outside linking nodes. Moreover, PSO influenced by the system parameters are determined by the excitation threshold and the minimum 1D Winfree loop. Finally, we confirmed that the excitation threshold and the minimum 1D Winfree loop determined PSO will degenerate as the system size is expanded.

Design and multi-physics optimization of rotary MRF brakes

NASA Astrophysics Data System (ADS)

Topcu, Okan; Taşcıoğlu, Yiğit; Konukseven, Erhan İlhan

2018-03-01

Particle swarm optimization (PSO) is a popular method to solve the optimization problems. However, calculations for each particle will be excessive when the number of particles and complexity of the problem increases. As a result, the execution speed will be too slow to achieve the optimized solution. Thus, this paper proposes an automated design and optimization method for rotary MRF brakes and similar multi-physics problems. A modified PSO algorithm is developed for solving multi-physics engineering optimization problems. The difference between the proposed method and the conventional PSO is to split up the original single population into several subpopulations according to the division of labor. The distribution of tasks and the transfer of information to the next party have been inspired by behaviors of a hunting party. Simulation results show that the proposed modified PSO algorithm can overcome the problem of heavy computational burden of multi-physics problems while improving the accuracy. Wire type, MR fluid type, magnetic core material, and ideal current inputs have been determined by the optimization process. To the best of the authors' knowledge, this multi-physics approach is novel for optimizing rotary MRF brakes and the developed PSO algorithm is capable of solving other multi-physics engineering optimization problems. The proposed method has showed both better performance compared to the conventional PSO and also has provided small, lightweight, high impedance rotary MRF brake designs.

Pomegranate seed oil nanoemulsions for the prevention and treatment of neurodegenerative diseases: the case of genetic CJD.

PubMed

Mizrahi, Michal; Friedman-Levi, Yael; Larush, Liraz; Frid, Kati; Binyamin, Orli; Dori, Dvir; Fainstein, Nina; Ovadia, Haim; Ben-Hur, Tamir; Magdassi, Shlomo; Gabizon, Ruth

2014-08-01

Neurodegenerative diseases generate the accumulation of specific misfolded proteins, such as PrP(Sc) prions or A-beta in Alzheimer's diseases, and share common pathological features, like neuronal death and oxidative damage. To test whether reduced oxidation alters disease manifestation, we treated TgMHu2ME199K mice, modeling for genetic prion disease, with Nano-PSO, a nanodroplet formulation of pomegranate seed oil (PSO). PSO comprises large concentrations of a unique polyunsaturated fatty acid, Punicic acid, among the strongest natural antioxidants. Nano-PSO significantly delayed disease presentation when administered to asymptomatic TgMHu2ME199K mice and postponed disease aggravation in already sick mice. Analysis of brain samples revealed that Nano-PSO treatment did not decrease PrP(Sc) accumulation, but rather reduced lipid oxidation and neuronal loss, indicating a strong neuroprotective effect. We propose that Nano-PSO and alike formulations may be both beneficial and safe enough to be administered for long years to subjects at risk or to those already affected by neurodegenerative conditions. This team of authors report that a nanoformulation of pomegranade seed oil, containing high levels of a strong antioxidant, can delay disease onset in a mouse model of genetic prion diseases, and the formulation also indicates a direct neuroprotective effect. Copyright © 2014 Elsevier Inc. All rights reserved.

Phase Response Design of Recursive All-Pass Digital Filters Using a Modified PSO Algorithm

PubMed Central

2015-01-01

This paper develops a new design scheme for the phase response of an all-pass recursive digital filter. A variant of particle swarm optimization (PSO) algorithm will be utilized for solving this kind of filter design problem. It is here called the modified PSO (MPSO) algorithm in which another adjusting factor is more introduced in the velocity updating formula of the algorithm in order to improve the searching ability. In the proposed method, all of the designed filter coefficients are firstly collected to be a parameter vector and this vector is regarded as a particle of the algorithm. The MPSO with a modified velocity formula will force all particles into moving toward the optimal or near optimal solution by minimizing some defined objective function of the optimization problem. To show the effectiveness of the proposed method, two different kinds of linear phase response design examples are illustrated and the general PSO algorithm is compared as well. The obtained results show that the MPSO is superior to the general PSO for the phase response design of digital recursive all-pass filter. PMID:26366168

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Comparison of potential preventive effects of pomegranate flower, peel and seed oil on insulin resistance and inflammation in high-fat and high-sucrose diet-induced obesity mice model.

PubMed

Harzallah, Arij; Hammami, Mohamed; Kępczyńska, Malgorzata A; Hislop, David C; Arch, Jonathan R S; Cawthorne, Michael A; Zaibi, Mohamed S

2016-01-01

The potentially beneficial effects of pomegranate peel (PPE), flower (PFE) and seed oil (PSO) extracts, in comparison with rosiglitazone, on adiposity, lipid profile, glucose homoeostasis, as well as on the underlying inflammatory mechanisms, were examined in high-fat and high-sucrose (HF/HS) diet-induced obese (DIO) mice. Body weight, body fat, energy expenditure, food and liquid intake, blood glucose, and plasma levels of insulin, lipids and cytokines were measured. After two weeks, PSO (2 ml/kg/day) and rosiglitazone (3 mg/kg/day) had not improved glucose intolerance. After 4 weeks, both treatments significantly reduced fasting blood glucose and an insulin tolerance test showed that they also improved insulin sensitivity. Treatment with PPE, PFE and PSO, reduced the plasma levels of the pro-inflammatory cytokines such as interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α), and PFE increased the level of the anti-inflammatory cytokine interleukin-10 (IL-10). PPE, PFE and PSO have anti-inflammatory properties. PSO also improved insulin sensitivity.

On the optimization of electromagnetic geophysical data: Application of the PSO algorithm

NASA Astrophysics Data System (ADS)

Godio, A.; Santilano, A.

2018-01-01

Particle Swarm optimization (PSO) algorithm resolves constrained multi-parameter problems and is suitable for simultaneous optimization of linear and nonlinear problems, with the assumption that forward modeling is based on good understanding of ill-posed problem for geophysical inversion. We apply PSO for solving the geophysical inverse problem to infer an Earth model, i.e. the electrical resistivity at depth, consistent with the observed geophysical data. The method doesn't require an initial model and can be easily constrained, according to external information for each single sounding. The optimization process to estimate the model parameters from the electromagnetic soundings focuses on the discussion of the objective function to be minimized. We discuss the possibility to introduce in the objective function vertical and lateral constraints, with an Occam-like regularization. A sensitivity analysis allowed us to check the performance of the algorithm. The reliability of the approach is tested on synthetic, real Audio-Magnetotelluric (AMT) and Long Period MT data. The method appears able to solve complex problems and allows us to estimate the a posteriori distribution of the model parameters.

Particle swarm optimization algorithm based parameters estimation and control of epileptiform spikes in a neural mass model

NASA Astrophysics Data System (ADS)

Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan

2016-07-01

This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.

Research on torsional vibration modelling and control of printing cylinder based on particle swarm optimization

NASA Astrophysics Data System (ADS)

Wang, Y. M.; Xu, W. C.; Wu, S. Q.; Chai, C. W.; Liu, X.; Wang, S. H.

2018-03-01

The torsional oscillation is the dominant vibration form for the impression cylinder of printing machine (printing cylinder for short), directly restricting the printing speed up and reducing the quality of the prints. In order to reduce torsional vibration, the active control method for the printing cylinder is obtained. Taking the excitation force and moment from the cylinder gap and gripper teeth open & closing cam mechanism as variable parameters, authors establish the dynamic mathematical model of torsional vibration for the printing cylinder. The torsional active control method is based on Particle Swarm Optimization(PSO) algorithm to optimize input parameters for the serve motor. Furthermore, the input torque of the printing cylinder is optimized, and then compared with the numerical simulation results. The conclusions are that torsional vibration active control based on PSO is an availability method to the torsional vibration of printing cylinder.

Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

Thermal and TEC anomalies detection using an intelligent hybrid system around the time of the Saravan, Iran, (Mw = 7.7) earthquake of 16 April 2013

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2014-02-01

A powerful earthquake of Mw = 7.7 struck the Saravan region (28.107° N, 62.053° E) in Iran on 16 April 2013. Up to now nomination of an automated anomaly detection method in a non linear time series of earthquake precursor has been an attractive and challenging task. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) have revealed strong potentials in accurate time series prediction. This paper presents the first study of an integration of ANN and PSO method in the research of earthquake precursors to detect the unusual variations of the thermal and total electron content (TEC) seismo-ionospheric anomalies induced by the strong earthquake of Saravan. In this study, to overcome the stagnation in local minimum during the ANN training, PSO as an optimization method is used instead of traditional algorithms for training the ANN method. The proposed hybrid method detected a considerable number of anomalies 4 and 8 days preceding the earthquake. Since, in this case study, ionospheric TEC anomalies induced by seismic activity is confused with background fluctuations due to solar activity, a multi-resolution time series processing technique based on wavelet transform has been applied on TEC signal variations. In view of the fact that the accordance in the final results deduced from some robust methods is a convincing indication for the efficiency of the method, therefore the detected thermal and TEC anomalies using the ANN + PSO method were compared to the results with regard to the observed anomalies by implementing the mean, median, Wavelet, Kalman filter, Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM) and Genetic Algorithm (GA) methods. The results indicate that the ANN + PSO method is quite promising and deserves serious attention as a new tool for thermal and TEC seismo anomalies detection.

Metabolomics in psoriatic disease: pilot study reveals metabolite differences in psoriasis and psoriatic arthritis

PubMed Central

Armstrong, April W.; Wu, Julie; Johnson, Mary Ann; Grapov, Dmitry; Azizi, Baktazh; Dhillon, Jaskaran; Fiehn, Oliver

2014-01-01

Importance: While “omics” studies have advanced our understanding of inflammatory skin diseases, metabolomics is mostly an unexplored field in dermatology. Objective: We sought to elucidate the pathogenesis of psoriatic diseases by determining the differences in metabolomic profiles among psoriasis patients with or without psoriatic arthritis and healthy controls. Design: We employed a global metabolomics approach to compare circulating metabolites from patients with psoriasis, psoriasis and psoriatic arthritis, and healthy controls. Setting: Study participants were recruited from the general community and from the Psoriasis Clinic at the University of California Davis in United States. Participants: We examined metabolomic profiles using blood serum samples from 30 patients age and gender matched into three groups: 10 patients with psoriasis, 10 patients with psoriasis and psoriatic arthritis and 10 control participants. Main outcome(s) and measures(s): Metabolite levels were measured calculating the mean peak intensities from gas chromatography time-of-flight mass spectrometry. Results: Multivariate analyses of metabolomics profiles revealed altered serum metabolites among the study population. Compared to control patients, psoriasis patients had a higher level of alpha ketoglutaric acid (Pso: 288 ± 88; Control: 209 ± 69; p=0.03), a lower level of asparagine (Pso: 5460 ± 980; Control: 7260 ± 2100; p=0.02), and a lower level of glutamine (Pso: 86000 ± 20000; Control: 111000 ± 27000; p=0.02). Compared to control patients, patients with psoriasis and psoriatic arthritis had increased levels of glucuronic acid (Pso + PsA: 638 ± 250; Control: 347 ± 61; p=0.001). Compared to patients with psoriasis alone, patients with both psoriasis and psoriatic arthritis had a decreased level of alpha ketoglutaric acid (Pso + PsA: 186 ± 80; Pso: 288 ± 88; p=0.02) and an increased level of lignoceric acid (Pso + PsA: 442 ± 280; Pso: 214 ± 64; p=0.02). Conclusions and relevance: The metabolite differences help elucidate the pathogenesis of psoriasis and psoriatic arthritis and they may provide insights for therapeutic development. PMID:25580230

Impact of active and stable psoriasis on health-related quality of life: the PSO-LIFE study.

PubMed

Daudén, E; Herrera, E; Puig, L; Sánchez-Carazo, J L; Toribio, J; Perulero, N

2013-10-01

The aim of this study was to assess the impact of psoriasis on health-related quality of life (HRQOL) using different questionnaires. Prospective observational study of patients with plaque psoriasis of at least 6 months' duration stratified by active and stable disease. The patients were evaluated at baseline, 7 days, and 12 weeks. At the 3 visits, the investigators recorded sociodemographic and clinical data and the patients completed the following HRQOL questionnaires: the Dermatology Life Quality Index (DLQI), the Psoriasis Disability Index (PDI), and psoriasis quality of life questionnaire (PSO-LIFE). In total, 304 patients (182 with active psoriasis and 122 with stable psoriasis) were evaluated. The mean (SD) age was 45.3 (14.5) years, and 56.3% of the group were men. At baseline, the mean (SD) psoriasis and area severity index (PASI) score was 17.0 (7.4) in patients with active disease and 5.6 (5.3) in those with stable disease; a reduction was seen in PASI scores during the evaluation period (P<.01). The mean (SD) score on the PSO-LIFE questionnaire increased significantly from 57.4 (20.4) to 72.2 (19.6) in patients with active psoriasis and from 76.4 (20.6) to 82.3 (18.3) in those with stable disease (P<0.01 in both groups). The difference in standardized mean scores between the 2 groups was 0.79 for the DLQI, 0.62 for the PDI, and 0.85 for the PSO-LIFE questionnaire. The impact of psoriasis on HRQOL as assessed by the PSO-LIFE questionnaire was greater in patients with lesions in visible areas than in those with less visible lesions (P<.01). Changes in PSO-LIFE and PASI scores were moderately and significantly correlated (r=-0.4). The impact of psoriasis on HRQOL is higher in patients with active disease. The PSO-LIFE questionnaire showed a greater tendency to discriminate between active and stable psoriasis than either the DLQI or the PDI. PSO-LIFE scores correlated significantly with lesion site and disease severity as measured by PASI. Copyright © 2012 Elsevier España, S.L. and AEDV. All rights reserved.

A LiDAR data-based camera self-calibration method

NASA Astrophysics Data System (ADS)

Xu, Lijun; Feng, Jing; Li, Xiaolu; Chen, Jianjun

2018-07-01

To find the intrinsic parameters of a camera, a LiDAR data-based camera self-calibration method is presented here. Parameters have been estimated using particle swarm optimization (PSO), enhancing the optimal solution of a multivariate cost function. The main procedure of camera intrinsic parameter estimation has three parts, which include extraction and fine matching of interest points in the images, establishment of cost function, based on Kruppa equations and optimization of PSO using LiDAR data as the initialization input. To improve the precision of matching pairs, a new method of maximal information coefficient (MIC) and maximum asymmetry score (MAS) was used to remove false matching pairs based on the RANSAC algorithm. Highly precise matching pairs were used to calculate the fundamental matrix so that the new cost function (deduced from Kruppa equations in terms of the fundamental matrix) was more accurate. The cost function involving four intrinsic parameters was minimized by PSO for the optimal solution. To overcome the issue of optimization pushed to a local optimum, LiDAR data was used to determine the scope of initialization, based on the solution to the P4P problem for camera focal length. To verify the accuracy and robustness of the proposed method, simulations and experiments were implemented and compared with two typical methods. Simulation results indicated that the intrinsic parameters estimated by the proposed method had absolute errors less than 1.0 pixel and relative errors smaller than 0.01%. Based on ground truth obtained from a meter ruler, the distance inversion accuracy in the experiments was smaller than 1.0 cm. Experimental and simulated results demonstrated that the proposed method was highly accurate and robust.

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

Cancer Diagnosis Epigenomics Scientific Workflow Scheduling in the Cloud Computing Environment Using an Improved PSO Algorithm

PubMed

N, Sadhasivam; R, Balamurugan; M, Pandi

2018-01-27

Objective: Epigenetic modifications involving DNA methylation and histone statud are responsible for the stable maintenance of cellular phenotypes. Abnormalities may be causally involved in cancer development and therefore could have diagnostic potential. The field of epigenomics refers to all epigenetic modifications implicated in control of gene expression, with a focus on better understanding of human biology in both normal and pathological states. Epigenomics scientific workflow is essentially a data processing pipeline to automate the execution of various genome sequencing operations or tasks. Cloud platform is a popular computing platform for deploying large scale epigenomics scientific workflow. Its dynamic environment provides various resources to scientific users on a pay-per-use billing model. Scheduling epigenomics scientific workflow tasks is a complicated problem in cloud platform. We here focused on application of an improved particle swam optimization (IPSO) algorithm for this purpose. Methods: The IPSO algorithm was applied to find suitable resources and allocate epigenomics tasks so that the total cost was minimized for detection of epigenetic abnormalities of potential application for cancer diagnosis. Result: The results showed that IPSO based task to resource mapping reduced total cost by 6.83 percent as compared to the traditional PSO algorithm. Conclusion: The results for various cancer diagnosis tasks showed that IPSO based task to resource mapping can achieve better costs when compared to PSO based mapping for epigenomics scientific application workflow. Creative Commons Attribution License

An Energy Integrated Dispatching Strategy of Multi- energy Based on Energy Internet

NASA Astrophysics Data System (ADS)

Jin, Weixia; Han, Jun

2018-01-01

Energy internet is a new way of energy use. Energy internet achieves energy efficiency and low cost by scheduling a variety of different forms of energy. Particle Swarm Optimization (PSO) is an advanced algorithm with few parameters, high computational precision and fast convergence speed. By improving the parameters ω, c1 and c2, PSO can improve the convergence speed and calculation accuracy. The objective of optimizing model is lowest cost of fuel, which can meet the load of electricity, heat and cold after all the renewable energy is received. Due to the different energy structure and price in different regions, the optimization strategy needs to be determined according to the algorithm and model.

Fuzzy mutual information based grouping and new fitness function for PSO in selection of miRNAs in cancer.

PubMed

Pal, Jayanta Kumar; Ray, Shubhra Sankar; Pal, Sankar K

2017-10-01

MicroRNAs (miRNA) are one of the important regulators of cell division and also responsible for cancer development. Among the discovered miRNAs, not all are important for cancer detection. In this regard a fuzzy mutual information (FMI) based grouping and miRNA selection method (FMIGS) is developed to identify the miRNAs responsible for a particular cancer. First, the miRNAs are ranked and divided into several groups. Then the most important group is selected among the generated groups. Both the steps viz., ranking of miRNAs and selection of the most relevant group of miRNAs, are performed using FMI. Here the number of groups is automatically determined by the grouping method. After the selection process, redundant miRNAs are removed from the selected set of miRNAs as per user's necessity. In a part of the investigation we proposed a FMI based particle swarm optimization (PSO) method for selecting relevant miRNAs, where FMI is used as a fitness function to determine the fitness of the particles. The effectiveness of FMIGS and FMI based PSO is tested on five data sets and their efficiency in selecting relevant miRNAs are demonstrated. The superior performance of FMIGS to some existing methods are established and the biological significance of the selected miRNAs is observed by the findings of the biological investigation and publicly available pathway analysis tools. The source code related to our investigation is available at http://www.jayanta.droppages.com/FMIGS.html. Copyright © 2017 Elsevier Ltd. All rights reserved.

42 CFR 3.110 - Assessment of PSO compliance.

Code of Federal Regulations, 2010 CFR

2010-10-01

... reviews of, or site visits to, PSOs, to assess or verify PSO compliance with the requirements of this subpart and for these purposes will be allowed to inspect the physical or virtual sites maintained or...

76 FR 60494 - Patient Safety Organizations: Voluntary Relinquishment From HPI-PSO

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-29

... at 12 midnight ET (2400) on August 31, 2011. ADDRESSES: Both directories can be accessed... midnight ET (2400) on August 31, 2011. More information on PSOs can be obtained through AHRQ's PSO Web site...

Insufficient restoration of lumbar lordosis and FBI index following pedicle subtraction osteotomy is an indicator of likely mechanical complication.

PubMed

Le Huec, J C; Cogniet, A; Demezon, H; Rigal, J; Saddiki, R; Aunoble, S

2015-01-01

Pedicle subtraction osteotomies (PSO) enable correction of spinal deformities but remain difficult and are associated with high complication rates. This study aimed to prospectively review different post-operative complications and mechanical problems in patients who underwent PSO as treatment for sagittal imbalance as sequelae of degenerative disc disease or previous spinal fusion. This was a descriptive prospective single center study of 63 patients who underwent sagittal imbalance correction by PSO. Radiographic analysis of pre- and post-operative pelvic and spinal parameters was completed based on EOS images following 3D modeling. Global and sub-group analyses were completed based on the Roussouly classification. A systematic analysis of post-operative complications was conducted during hospital stay and at follow-up visits. Complications included 15 cases (20.2%) of bilateral leg pain, with transient neurological deficit in 6 cases (9.5%), and 9 cases (12.5%) of early surgical site infections. Intra-operative complications included five tears of the dura mater and two cases of excessive blood loss (>5,000 mL). Two mortalities occurred from major intracerebral bleeds in the early post-operative period. Mechanical complications were principally non-union (9 cases) and junctional kyphosis (3 cases). All 19 post-operative complications (28.1%) were revised at an average of 2 years following surgery. All mechanical complications were found in the patients who had insufficient imbalance correction and this was mainly associated with high PI (>60°) or a moderate PI (45-60º) combined with excess FBI pre-operatively that remained >10° post-operatively. Infection and neurologic complications following PSO are relatively common, and frequently reported in the literature. The principal cause of mechanical complications, such as non-union or junctional kyphosis, was insufficient sagittal correction, characterized by post-operative FBI >10°. The risks of insufficient correction are greater in patients with higher pelvic incidence and those patients who required very high correction.

Guided particle swarm optimization method to solve general nonlinear optimization problems

NASA Astrophysics Data System (ADS)

Abdelhalim, Alyaa; Nakata, Kazuhide; El-Alem, Mahmoud; Eltawil, Amr

2018-04-01

The development of hybrid algorithms is becoming an important topic in the global optimization research area. This article proposes a new technique in hybridizing the particle swarm optimization (PSO) algorithm and the Nelder-Mead (NM) simplex search algorithm to solve general nonlinear unconstrained optimization problems. Unlike traditional hybrid methods, the proposed method hybridizes the NM algorithm inside the PSO to improve the velocities and positions of the particles iteratively. The new hybridization considers the PSO algorithm and NM algorithm as one heuristic, not in a sequential or hierarchical manner. The NM algorithm is applied to improve the initial random solution of the PSO algorithm and iteratively in every step to improve the overall performance of the method. The performance of the proposed method was tested over 20 optimization test functions with varying dimensions. Comprehensive comparisons with other methods in the literature indicate that the proposed solution method is promising and competitive.

Large-visual-angle microstructure inspired from quantitative design of Morpho butterflies' lamellae deviation using the FDTD/PSO method.

PubMed

Wang, Wanlin; Zhang, Wang; Chen, Weixin; Gu, Jiajun; Liu, Qinglei; Deng, Tao; Zhang, Di

2013-01-15

The wide angular range of the treelike structure in Morpho butterfly scales was investigated by finite-difference time-domain (FDTD)/particle-swarm-optimization (PSO) analysis. Using the FDTD method, different parameters in the Morpho butterflies' treelike structure were studied and their contributions to the angular dependence were analyzed. Then a wide angular range was realized by the PSO method from quantitatively designing the lamellae deviation (Δy), which was a crucial parameter with angular range. The field map of the wide-range reflection in a large area was given to confirm the wide angular range. The tristimulus values and corresponding color coordinates for various viewing directions were calculated to confirm the blue color in different observation angles. The wide angular range realized by the FDTD/PSO method will assist us in understanding the scientific principles involved and also in designing artificial optical materials.

Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding

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

Jiang, Huaiguang; Li, Yan; Zhang, Yingchen

In this paper, a big data-based approach is proposed for the security improvement of an unplanned microgrid islanding (UMI). The proposed approach contains two major steps: the first step is big data analysis of wide-area monitoring to detect a UMI and locate it; the second step is particle swarm optimization (PSO)-based stability enhancement for the UMI. First, an optimal synchrophasor measurement device selection (OSMDS) and matching pursuit decomposition (MPD)-based spatial-temporal analysis approach is proposed to significantly reduce the volume of data while keeping appropriate information from the synchrophasor measurements. Second, a random forest-based ensemble learning approach is trained to detectmore » the UMI. When combined with grid topology, the UMI can be located. Then the stability problem of the UMI is formulated as an optimization problem and the PSO is used to find the optimal operational parameters of the UMI. An eigenvalue-based multiobjective function is proposed, which aims to improve the damping and dynamic characteristics of the UMI. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed approach.« less

Effective motion planning strategy for space robot capturing targets under consideration of the berth position

NASA Astrophysics Data System (ADS)

Zhang, Xin; Liu, Jinguo

2018-07-01

Although many motion planning strategies for missions involving space robots capturing floating targets can be found in the literature, relatively little has discussed how to select the berth position where the spacecraft base hovers. In fact, the berth position is a flexible and controllable factor, and selecting a suitable berth position has a great impact on improving the efficiency of motion planning in the capture mission. Therefore, to make full use of the manoeuvrability of the space robot, this paper proposes a new viewpoint that utilizes the base berth position as an optimizable parameter to formulate a more comprehensive and effective motion planning strategy. Considering the dynamic coupling, the dynamic singularities, and the physical limitations of space robots, a unified motion planning framework based on the forward kinematics and parameter optimization technique is developed to convert the planning problem into the parameter optimization problem. For getting rid of the strict grasping position constraints in the capture mission, a new conception of grasping area is proposed to greatly simplify the difficulty of the motion planning. Furthermore, by utilizing the penalty function method, a new concise objective function is constructed. Here, the intelligent algorithm, Particle Swarm Optimization (PSO), is worked as solver to determine the free parameters. Two capturing cases, i.e., capturing a two-dimensional (2D) planar target and capturing a three-dimensional (3D) spatial target, are studied under this framework. The corresponding simulation results demonstrate that the proposed method is more efficient and effective for planning the capture missions.

Anticancer activity of Petroselinum sativum seed extracts on MCF-7 human breast cancer cells.

PubMed

Farshori, Nida Nayyar; Al-Sheddi, Ebtesam Saad; Al-Oqail, Mai Mohammad; Musarrat, Javed; Al-Khedhairy, Abdulaziz Ali; Siddiqui, Maqsood Ahmed

2013-01-01

Pharmacological and preventive properties of Petroselinum sativum seed extracts are well known, but the anticancer activity of alcoholic extracts and oil of Petroselinum sativum seeds on human breast cancer cells have not been explored so far. Therefore, the present study was designed to investigate the cytotoxic activities of these extracts against MCF-7 cells. Cells were exposed to 10 to 1000 μg/ml of alcoholic seed extract (PSA) and seed oil (PSO) of Petroselinum sativum for 24 h. Post-treatment, percent cell viability was studied by 3-(4, 5-dimethylthiazol-2yl)-2, 5-biphenyl tetrazolium bromide (MTT) and neutral red uptake (NRU) assays, and cellular morphology by phase contrast inverted microscopy. The results showed that PSA and PSO significantly reduced cell viability, and altered the cellular morphology of MCF-7 cells in a concentration dependent manner. Concentrations of 50 μg/ml and above of PSA and 100 μg/ml and above of PSO were found to be cytotoxic in MCF-7 cells. Cell viability at 50, 100, 250, 500 and 1000 μg/ml of PSA was recorded as 81%, 57%, 33%, 8% and 5%, respectively, whereas at 100, 250, 500, and 1000 μg/ml of PSO values were 90%, 78%, 62%, and 8%, respectively by MTT assay. MCF-7 cells exposed to 250, 500 and 1000 μg/ml of PSA and PSO lost their typical morphology and appeared smaller in size. The data revealed that the treatment with PSA and PSO of Petroselinum sativum induced cell death in MCF-7 cells.

SU-E-T-06: 4D Particle Swarm Optimization to Enable Lung SBRT in Patients with Central And/or Large Tumors

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

Modiri, A; Gu, X; Hagan, A

2015-06-15

Purpose: Patients presenting with large and/or centrally-located lung tumors are currently considered ineligible for highly potent regimens such as SBRT due to concerns of toxicity to normal tissues and organs-at-risk (OARs). We present a particle swarm optimization (PSO)-based 4D planning technique, designed for MLC tracking delivery, that exploits the temporal dimension as an additional degree of freedom to significantly improve OAR-sparing and reduce toxicity to levels clinically considered as acceptable for SBRT administration. Methods: Two early-stage SBRT-ineligible NSCLC patients were considered, presenting with tumors of maximum dimensions of 7.4cm (central-right lobe; 1.5cm motion) and 11.9cm (upper-right lobe; 1cm motion). Inmore » each case, the target and normal structures were manually contoured on each of the ten 4DCT phases. Corresponding ten initial 3D-conformal plans (Pt#1: 7-beams; Pt#2: 9-beams) were generated using the Eclipse planning system. Using 4D-PSO, fluence weights were optimized over all beams and all phases (70 and 90 apertures for Pt1&2, respectively). Doses to normal tissues and OARs were compared with clinicallyestablished lung SBRT guidelines based on RTOG-0236. Results: The PSO-based 4D SBRT plan yielded tumor coverage and dose—sparing for parallel and serial OARs within the SBRT guidelines for both patients. The dose-sparing compared to the clinically-delivered conventionallyfractionated plan for Patient 1 (Patient 2) was: heart Dmean = 11% (33%); lung V20 = 16% (21%); lung Dmean = 7% (20%); spinal cord Dmax = 5% (16%); spinal cord Dmean = 7% (33%); esophagus Dmax = 0% (18%). Conclusion: Truly 4D planning can significantly reduce dose to normal tissues and OARs. Such sparing opens up the possibility of using highly potent and effective regimens such as lung SBRT for patients who were conventionally considered SBRT non-eligible. Given the large, non-convex solution space, PSO represents an attractive, parallelizable tool to successfully achieve a globally optimal solution for this problem. This work was supported through funding from the National Institutes of Health and Varian Medical Systems.« less

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

PubMed

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-06-12

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks.

PubMed

Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

2016-07-14

Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

Robust Weighted Sum Harvested Energy Maximization for SWIPT Cognitive Radio Networks Based on Particle Swarm Optimization.

PubMed

Tuan, Pham Viet; Koo, Insoo

2017-10-06

In this paper, we consider multiuser simultaneous wireless information and power transfer (SWIPT) for cognitive radio systems where a secondary transmitter (ST) with an antenna array provides information and energy to multiple single-antenna secondary receivers (SRs) equipped with a power splitting (PS) receiving scheme when multiple primary users (PUs) exist. The main objective of the paper is to maximize weighted sum harvested energy for SRs while satisfying their minimum required signal-to-interference-plus-noise ratio (SINR), the limited transmission power at the ST, and the interference threshold of each PU. For the perfect channel state information (CSI), the optimal beamforming vectors and PS ratios are achieved by the proposed PSO-SDR in which semidefinite relaxation (SDR) and particle swarm optimization (PSO) methods are jointly combined. We prove that SDR always has a rank-1 solution, and is indeed tight. For the imperfect CSI with bounded channel vector errors, the upper bound of weighted sum harvested energy (WSHE) is also obtained through the S-Procedure. Finally, simulation results demonstrate that the proposed PSO-SDR has fast convergence and better performance as compared to the other baseline schemes.

Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm.

PubMed

Hannan, M A; Akhtar, Mahmuda; Begum, R A; Basri, H; Hussain, A; Scavino, Edgar

2018-01-01

Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

42 CFR 3.102 - Process and requirements for initial and continued listing of PSOs.

Code of Federal Regulations, 2010 CFR

2010-10-01

... information that is not available to other providers, or affect the independence of PSO operations, management... to information regarding the work and operation of the PSO that is not available to other contracting...

78 FR 70560 - Patient Safety Organizations: Voluntary Relinquishment From GE-PSO

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-26

... ongoing and reviewed weekly by AHRQ. The delisting was effective at 12:00 Midnight ET (2400) on September...:00 Midnight ET (2400) on September 30, 2013. GE-PSO has patient safety work product (PSWP) in its...

76 FR 60495 - Patient Safety Organizations: Voluntary Relinquishment From Illinois PSO

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-29

... ongoing and reviewed weekly by AHRQ. The delisting was effective at 12 Midnight ET (2400) on July 20, 2011.... Accordingly, the Illinois PSO was delisted effective at 12 Midnight ET (2400) on July 20, 2011. More...

Research on bulbous bow optimization based on the improved PSO algorithm

NASA Astrophysics Data System (ADS)

Zhang, Sheng-long; Zhang, Bao-ji; Tezdogan, Tahsin; Xu, Le-ping; Lai, Yu-yang

2017-08-01

In order to reduce the total resistance of a hull, an optimization framework for the bulbous bow optimization was presented. The total resistance in calm water was selected as the objective function, and the overset mesh technique was used for mesh generation. RANS method was used to calculate the total resistance of the hull. In order to improve the efficiency and smoothness of the geometric reconstruction, the arbitrary shape deformation (ASD) technique was introduced to change the shape of the bulbous bow. To improve the global search ability of the particle swarm optimization (PSO) algorithm, an improved particle swarm optimization (IPSO) algorithm was proposed to set up the optimization model. After a series of optimization analyses, the optimal hull form was found. It can be concluded that the simulation based design framework built in this paper is a promising method for bulbous bow optimization.

Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques.

PubMed

Chen, Shyi-Ming; Manalu, Gandhi Maruli Tua; Pan, Jeng-Shyang; Liu, Hsiang-Chuan

2013-06-01

In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization (PSO) techniques. First, we fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors second-order fuzzy logical relationships. Then, we group the two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, we obtain the optimal weighting vector for each fuzzy-trend logical relationship group by using PSO techniques to perform the forecasting. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index and the NTD/USD exchange rates. The experimental results show that the proposed method gets better forecasting performance than the existing methods.

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

PubMed Central

Wang, Jie-Sheng; Han, Shuang

2015-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:26583034

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

PubMed

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Comparison of Artificial Immune System and Particle Swarm Optimization Techniques for Error Optimization of Machine Vision Based Tool Movements

NASA Astrophysics Data System (ADS)

Mahapatra, Prasant Kumar; Sethi, Spardha; Kumar, Amod

2015-10-01

In conventional tool positioning technique, sensors embedded in the motion stages provide the accurate tool position information. In this paper, a machine vision based system and image processing technique for motion measurement of lathe tool from two-dimensional sequential images captured using charge coupled device camera having a resolution of 250 microns has been described. An algorithm was developed to calculate the observed distance travelled by the tool from the captured images. As expected, error was observed in the value of the distance traversed by the tool calculated from these images. Optimization of errors due to machine vision system, calibration, environmental factors, etc. in lathe tool movement was carried out using two soft computing techniques, namely, artificial immune system (AIS) and particle swarm optimization (PSO). The results show better capability of AIS over PSO.

PID controller tuning using metaheuristic optimization algorithms for benchmark problems

NASA Astrophysics Data System (ADS)

Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.

2017-11-01

This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.

Metabolic flux estimation using particle swarm optimization with penalty function.

PubMed

Long, Hai-Xia; Xu, Wen-Bo; Sun, Jun

2009-01-01

Metabolic flux estimation through 13C trace experiment is crucial for quantifying the intracellular metabolic fluxes. In fact, it corresponds to a constrained optimization problem that minimizes a weighted distance between measured and simulated results. In this paper, we propose particle swarm optimization (PSO) with penalty function to solve 13C-based metabolic flux estimation problem. The stoichiometric constraints are transformed to an unconstrained one, by penalizing the constraints and building a single objective function, which in turn is minimized using PSO algorithm for flux quantification. The proposed algorithm is applied to estimate the central metabolic fluxes of Corynebacterium glutamicum. From simulation results, it is shown that the proposed algorithm has superior performance and fast convergence ability when compared to other existing algorithms.

A Particle Swarm Optimization Algorithm for Optimal Operating Parameters of VMI Systems in a Two-Echelon Supply Chain

NASA Astrophysics Data System (ADS)

Sue-Ann, Goh; Ponnambalam, S. G.

This paper focuses on the operational issues of a Two-echelon Single-Vendor-Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. To determine the optimal sales quantity for each buyer in TSVMBC, a mathematical model is formulated. Based on the optimal sales quantity can be obtained and the optimal sales price that will determine the optimal channel profit and contract price between the vendor and buyer. All this parameters depends upon the understanding of the revenue sharing between the vendor and buyers. A Particle Swarm Optimization (PSO) is proposed for this problem. Solutions obtained from PSO is compared with the best known results reported in literature.

77 FR 42737 - Patient Safety Organizations: Delisting for Cause for The Steward Group PSO

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-20

... 12:00 Midnight ET (2400) on June 19, 2012. ADDRESSES: Both directories can be accessed electronically... ET (2400) on June 19, 2012. More information on PSOs can be obtained through AHRQ's PSO Web site at...

PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons.

PubMed

Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Zhao, Guang-Yu; Xu, Guo-Qiang; He, Long; Mao, Xi-Wang; Dong, Wei

2016-09-02

Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons

PubMed Central

Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Zhao, Guang-Yu; Xu, Guo-Qiang; He, Long; Mao, Xi-Wang; Dong, Wei

2016-01-01

Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance. PMID:27598160

76 FR 71346 - Patient Safety Organizations: Voluntary Relinquishment From Peminic Inc. dba The Peminic-Greeley PSO

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-17

... at 12:00 Midnight E.T. (2400) on September 13, 2011. ADDRESSES: Both directories can be accessed.... Accordingly, the Peminic, Inc. dba The Peminic Greeley PSO was delisted effective at 12:00 Midnight ET (2400...

78 FR 70560 - Patient Safety Organizations: Voluntary Relinquishment From Morgridge Institute for Research PSO

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-26

... delisting was effective at 12:00 Midnight ET (2400) on November 6, 2013. ADDRESSES: Both directories can be... Research PSO, Inc. was delisted effective at 12:00 Midnight ET (2400) on November 6, 2013. More information...

Extended Asymmetrical Pedicle Subtraction Osteotomy for Adult Spinal Deformity: 2-Dimensional Operative Video.

PubMed

Buell, Thomas J; Buchholz, Avery L; Quinn, John C; Mullin, Jeffrey P; Garces, Juanita; Mazur, Marcus D; Shaffrey, Mark E; Yen, Chun-Po; Shaffrey, Christopher I; Smith, Justin S

2018-06-16

Pedicle subtraction osteotomy (PSO) is an effective technique to correct fixed sagittal malalignment. A variation of this technique, the "trans-discal" or "extended" PSO (Schwab grade IV osteotomy), involves extending the posterior wedge resection of the index vertebra to include the superior adjacent disc for radical discectomy. The posterior wedge may be resected in asymmetric fashion to correct concurrent global coronal malalignment.This video illustrates the technical nuances of an extended asymmetrical lumbar PSO for adult spinal deformity. A 62-yr-old female with multiple prior lumbar fusions presented with worsening back pain and posture. Preoperative scoliosis X-rays demonstrated severe global sagittal and coronal malalignment (sagittal vertical axis [SVA, C7-plumbline] of 13.5 cm, pelvic incidence [PI] of 60°, lumbar lordosis [LL] of 14° [in kyphosis], pelvic tilt [PT] of 61°, thoracic kyphosis [TK] of 18°, and rightward coronal shift of 9.3 cm). The patient gave informed consent to surgery and for use of her imaging for medical publication. Briefly, surgery first involved transpedicular instrumentation from T10 to S1 with bilateral iliac screw fixation, and then T11-12 and T12-L1 Smith-Petersen osteotomies were performed. Next, an extended asymmetrical L4 PSO was performed and a 12° lordotic cage (9 × 14 × 40 mm) was placed at the PSO defect. Rods were placed from T10 to iliac bilaterally, and accessory supplemental rods spanning the PSO were attached. Postoperative scoliosis X-rays demonstrated improved alignment: SVA 5.5 cm, PI 60°, LL 55°, PT 36°, TK 37°, and 3.7 cm of rightward coronal shift. The patient had uneventful recovery.

Biomechanical advantages of supplemental accessory and satellite rods with and without interbody cages implantation for the stabilization of pedicle subtraction osteotomy.

PubMed

La Barbera, Luigi; Brayda-Bruno, Marco; Liebsch, Christian; Villa, Tomaso; Luca, Andrea; Galbusera, Fabio; Wilke, Hans-Joachim

2018-05-08

To investigate the effect of anterior interbody cages, accessory and satellite rods usage on primary stability and rod strains for PSO stabilization. Seven human cadaveric spine segments (T12-S1) underwent PSO at L4 with posterior fixation from L2 to S1. In vitro flexibility tests were performed under pure moments in flexion/extension (FE), lateral bending (LB) and axial rotation (AR) to determine the range of motion, while measuring the strains on the primary rods with strain gauge rosettes. Six constructs with 2, 3 and 4 rods, with and without interbody cages implantation adjacent to the PSO site, were compared. All constructs had comparable effects in reducing spine kinematics compared to the intact condition (- 94% in FE and LB; - 80% in AR). Supplementation of 2 rods with lateral accessory rods (4 rods) was the most effective strategy in minimizing primary rod strains, particularly when coupled to cages (p ≤ 0.005; - 50% in FE, - 42% in AR and - 11% in LB); even without cages, the strains were significantly reduced (p ≤ 0.009; - 26%, - 37%, - 9%). The addition of a central satellite rod with laminar hooks (3 rods) effectively reduced rod strains in FE (p ≤ 0.005; - 30%) only in combination with cages. The study supports the current clinical practice providing a strong biomechanical rationale to recommend 4-rod constructs based on accessory rods combined with cages adjacent to PSO site. Although weaker, the usage of accessory rods without cages and of a central satellite rod with hooks in combination with interbody spacers may also be justified. These slides can be retrieved under Electronic Supplementary Material.

PSO-based methods for medical image registration and change assessment of pigmented skin

NASA Astrophysics Data System (ADS)

Kacenjar, Steve; Zook, Matthew; Balint, Michael

2011-03-01

There are various scientific and technological areas in which it is imperative to rapidly detect and quantify changes in imagery over time. In fields such as earth remote sensing, aerospace systems, and medical imaging, searching for timedependent, regional changes across deformable topographies is complicated by varying camera acquisition geometries, lighting environments, background clutter conditions, and occlusion. Under these constantly-fluctuating conditions, the use of standard, rigid-body registration approaches often fail to provide sufficient fidelity to overlay image scenes together. This is problematic because incorrect assessments of the underlying changes of high-level topography can result in systematic errors in the quantification and classification of interested areas. For example, in the current naked-eye detection strategies of melanoma, a dermatologist often uses static morphological attributes to identify suspicious skin lesions for biopsy. This approach does not incorporate temporal changes which suggest malignant degeneration. By performing the co-registration of time-separated skin imagery, a dermatologist may more effectively detect and identify early morphological changes in pigmented lesions; enabling the physician to detect cancers at an earlier stage resulting in decreased morbidity and mortality. This paper describes an image processing system which will be used to detect changes in the characteristics of skin lesions over time. The proposed system consists of three main functional elements: 1.) coarse alignment of timesequenced imagery, 2.) refined alignment of local skin topographies, and 3.) assessment of local changes in lesion size. During the coarse alignment process, various approaches can be used to obtain a rough alignment, including: 1.) a manual landmark/intensity-based registration method1, and 2.) several flavors of autonomous optical matched filter methods2. These procedures result in the rough alignment of a patient's back topography. Since the skin is a deformable membrane, this process only provides an initial condition for subsequent refinements in aligning the localized topography of the skin. To achieve a refined enhancement, a Particle Swarm Optimizer (PSO) is used to optimally determine the local camera models associated with a generalized geometric transform. Here the optimization process is driven using the minimization of entropy between the multiple time-separated images. Once the camera models are corrected for local skin deformations, the images are compared using both pixel-based and regional-based methods. Limits on the detectability of change are established by the fidelity to which the algorithm corrects for local skin deformation and background alterations. These limits provide essential information in establishing early-warning thresholds for Melanoma detection. Key to this work is the development of a PSO alignment algorithm to perform the refined alignment in local skin topography between the time sequenced imagery (TSI). Test and validation of this alignment process is achieved using a forward model producing known geometric artifacts in the images and afterwards using a PSO algorithm to demonstrate the ability to identify and correct for these artifacts. Specifically, the forward model introduces local translational, rotational, and magnification changes within the image. These geometric modifiers are expected during TSI acquisition because of logistical issues to precisely align the patient to the image recording geometry and is therefore of paramount importance to any viable image registration system. This paper shows that the PSO alignment algorithm is effective in autonomously determining and mitigating these geometric modifiers. The degree of efficacy is measured by several statistically and morphologically based pre-image filtering operations applied to the TSI imagery before applying the PSO alignment algorithm. These trade studies show that global image threshold binarization provides rapid and superior convergence characteristics relative to that of morphologically based methods.

Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO)

NASA Astrophysics Data System (ADS)

Wang, Mingming; Luo, Jianjun; Walter, Ulrich

2015-07-01

This paper investigates the application of Particle Swarm Optimization (PSO) strategy to trajectory planning of the kinematically redundant space robot in free-floating mode. Due to the path dependent dynamic singularities, the volume of available workspace of the space robot is limited and enormous joint velocities are required when such singularities are met. In order to overcome this effect, the direct kinematics equations in conjunction with PSO are employed for trajectory planning of free-floating space robot. The joint trajectories are parametrized with the Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) redundant manipulator mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

Pretreatment of shale gas drilling flowback fluid (SGDF) by the microscale Fe0/persulfate/O3 process (mFe0/PS/O3).

PubMed

Zhang, Heng; Xiong, Zhaokun; Ji, Fangzhou; Lai, Bo; Yang, Ping

2017-06-01

Shale gas drilling flowback fluid (SGDF) generated during shale gas extraction is of great concern due to its high total dissolved solid, radioactive elements and organic matter. To remove the toxic and refractory pollutants in SGDF and improve its biodegradability, a microsacle Fe 0 /Persulfate/O 3 process (mFe 0 /PS/O 3 ) was developed to pretreat this wastewater obtained from a shale gas well in southwestern China. First, effects of mFe 0 dosage, O 3 flow rate, PS dosage, pH values on the treatment efficiency of mFe 0 /PS/O 3 process were investigated through single-factor experiments. Afterward, the optimal conditions (i.e., pH = 6.7, mFe 0 dosage = 6.74 g/L, PS = 16.89 mmol/L, O 3 flow rate = 0.73 L/min) were obtained by using response surface methodology (RSM). Under the optimal conditions, high COD removal (75.3%) and BOD 5 /COD ratio (0.49) were obtained after 120 min treatment. Moreover, compared with control experiments (i.e., mFe 0 , O 3 , PS, mFe 0 /O 3 , mFe 0 /PS, O 3 /PS), mFe 0 /PS/O 3 system exerted better performance for pollutants removal in SGDF due to strong synergistic effect between mFe 0 , PS and O 3 . In addition, the decomposition or transformation of the organic pollutants in SGDF was analyzed by using GC-MS. Finally, the reaction mechanism of the mFe 0 /PS/O 3 process was proposed according to the analysis results of SEM-EDS and XRD. It can be concluded that high-efficient mFe 0 /PS/O 3 process was mainly resulted from the combination effect of direct oxidation by ozone and persulfate, heterogeneous and homogeneous catalytic oxidation, Fenton-like reaction and adsorption. Therefore, mFe 0 /PS/O 3 process was proven to be an effective method for pretreatment of SGDF prior to biological treatment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fuzzy logic, PSO based fuzzy logic algorithm and current controls comparative for grid-connected hybrid system

NASA Astrophysics Data System (ADS)

Borni, A.; Abdelkrim, T.; Zaghba, L.; Bouchakour, A.; Lakhdari, A.; Zarour, L.

2017-02-01

In this paper the model of a grid connected hybrid system is presented. The hybrid system includes a variable speed wind turbine controlled by aFuzzy MPPT control, and a photovoltaic generator controlled with PSO Fuzzy MPPT control to compensate the power fluctuations caused by the wind in a short and long term, the inverter currents injected to the grid is controlled by a decoupled PI current control. In the first phase, we start by modeling of the conversion system components; the wind system is consisted of a turbine coupled to a gearless permanent magnet generator (PMG), the AC/DC and DC-DC (Boost) converter are responsible to feed the electric energy produced by the PMG to the DC-link. The solar system consists of a photovoltaic generator (GPV) connected to a DC/DC boost converter controlled by a PSO fuzzy MPPT control to extract at any moment the maximum available power at the GPV terminals, the system is based on maximum utilization of both of sources because of their complementary. At the end. The active power reached to the DC-link is injected to the grid through a DC/AC inverter, this function is achieved by controlling the DC bus voltage to keep it constant and close to its reference value, The simulation studies have been performed using Matlab/Simulink. It can be concluded that a good control system performance can be achieved.

A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment

PubMed Central

Zhao, Jia; Hu, Liang; Ding, Yan; Xu, Gaochao; Hu, Ming

2014-01-01

The field of live VM (virtual machine) migration has been a hotspot problem in green cloud computing. Live VM migration problem is divided into two research aspects: live VM migration mechanism and live VM migration policy. In the meanwhile, with the development of energy-aware computing, we have focused on the VM placement selection of live migration, namely live VM migration policy for energy saving. In this paper, a novel heuristic approach PS-ES is presented. Its main idea includes two parts. One is that it combines the PSO (particle swarm optimization) idea with the SA (simulated annealing) idea to achieve an improved PSO-based approach with the better global search's ability. The other one is that it uses the Probability Theory and Mathematical Statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization for energy saving as it has considered not only the optimization of the current problem scenario but also that of the future problem. The experimental results demonstrate that PS-ES evidently reduces the total incremental energy consumption and better protects the performance of VM running and migrating compared with randomly migrating and optimally migrating. As a result, the proposed PS-ES approach has capabilities to make the result of live VM migration events more high-effective and valuable. PMID:25251339

A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment.

PubMed

Zhao, Jia; Hu, Liang; Ding, Yan; Xu, Gaochao; Hu, Ming

2014-01-01

The field of live VM (virtual machine) migration has been a hotspot problem in green cloud computing. Live VM migration problem is divided into two research aspects: live VM migration mechanism and live VM migration policy. In the meanwhile, with the development of energy-aware computing, we have focused on the VM placement selection of live migration, namely live VM migration policy for energy saving. In this paper, a novel heuristic approach PS-ES is presented. Its main idea includes two parts. One is that it combines the PSO (particle swarm optimization) idea with the SA (simulated annealing) idea to achieve an improved PSO-based approach with the better global search's ability. The other one is that it uses the Probability Theory and Mathematical Statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization for energy saving as it has considered not only the optimization of the current problem scenario but also that of the future problem. The experimental results demonstrate that PS-ES evidently reduces the total incremental energy consumption and better protects the performance of VM running and migrating compared with randomly migrating and optimally migrating. As a result, the proposed PS-ES approach has capabilities to make the result of live VM migration events more high-effective and valuable.

Evidence for Fumonisin Inhibition of Ceramide Synthase in Humans Consuming Maize-Based Foods and Living in High Exposure Communities in Guatemala

PubMed Central

Riley, Ronald T.; Torres, Olga; Matute, Jorge; Gregory, Simon G.; Ashley-Koch, Allison E.; Showker, Jency L.; Mitchell, Trevor; Voss, Kenneth A.; Maddox, Joyce R.; Gelineau-van Waes, Janee B.

2016-01-01

Scope Fumonisin (FB) occurs in maize and is an inhibitor of ceramide synthase (CerS). We determined the urinary FB1 (UFB1) and sphingoid base 1-phosphate levels in blood from women consuming maize in high and low FB exposure communities in Guatemala. Methods and results FB1 intake was estimated using the UFB1. Sphinganine 1-phosphate (Sa 1-P), sphingosine 1-phosphate (So 1-P), and the Sa 1-P/So 1-P ratio were determined in blood spots collected on absorbent paper at the same time as urine collection. In the first study, blood spots and urine were collected every three months (March 2011 to February 2012) from women living in low (Chimaltenango and Escuintla) and high (Jutiapa) FB exposure communities (1240 total recruits). The UFB1, Sa 1-P/So 1-P ratio, and Sa 1-P/ml in blood spots were significantly higher in the high FB1 intake community compared to the low FB1 intake communities. The results were confirmed in a follow-up study (February 2013) involving 299 women living in low (Sacatepéquez) and high (Santa Rosa and Chiquimula) FB exposure communities. Conclusions High levels of FB1 intake are correlated with changes in Sa 1-P and the Sa 1-P/So 1-P ratio in human blood in a manner consistent with FB1 inhibition of CerS. PMID:26264677

Novel, posterior sensory organ in the trochophore larva of Phyllodoce maculata (Polychaeta).

PubMed Central

Nezlin, L P; Voronezhskaya, E E

2003-01-01

A new posterior sensory organ (PSO), located at the dorsal midline of the hyposphere, is described by immunocytochemical detection of acetylated alpha tubulin and serotonin (5-HT) in a laser-scanning microscope, as well as three-dimensional reconstructions after optical serial sectioning in the trochophore larva of the polychaete Phyllodoce maculata (Phyllodocidae). The unpaired PSO consists of five bipolar sensory cells, two of them being 5-HT immunopositive, which send axons to the cerebral ganglion and prototroch nerve. The dendrites of these cells project to the surface and bear one cilium each. A single neuronal fibre from the apical sensory organ innervates the PSO. PMID:14667369

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

NASA Astrophysics Data System (ADS)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Improved particle swarm optimization algorithm for android medical care IOT using modified parameters.

PubMed

Sung, Wen-Tsai; Chiang, Yen-Chun

2012-12-01

This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services.

42 CFR 422.388 - Deposits.

Code of Federal Regulations, 2011 CFR

2011-10-01

... uncovered expenditures exceed 10 percent of a PSO's total health care expenditures, then the PSO must place... 42 Public Health 3 2011-10-01 2011-10-01 false Deposits. 422.388 Section 422.388 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICARE...

42 CFR 422.388 - Deposits.

Code of Federal Regulations, 2010 CFR

2010-10-01

... uncovered expenditures exceed 10 percent of a PSO's total health care expenditures, then the PSO must place... 42 Public Health 3 2010-10-01 2010-10-01 false Deposits. 422.388 Section 422.388 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICARE...

Biomechanical stability of transverse connectors in the setting of a thoracic pedicle subtraction osteotomy.

PubMed

Lehman, Ronald A; Kang, Daniel G; Wagner, Scott C; Paik, Haines; Cardoso, Mario J; Bernstock, Joshua D; Dmitriev, Anton E

2015-07-01

Transverse connectors (TCs) are often used to improve the rigidity of posterior spinal instrumentation as previous investigations have suggested that TCs enhance torsional rigidity in long-segment thoracic constructs. Posterior osteotomies, such as pedicle subtraction osteotomy (PSO), are used in severe thoracic deformities and provide a significant amount of correction; as a consequence, however, PSOs also induce three-column spinal instability. In theory, augmentation of longitudinal constructs with TC after a thoracic PSO may provide additional rigidity, but the concept has not been previously evaluated. To evaluate the biomechanical contribution of TC to the rigidity of a long-segment pedicle screw-rod construct after a thoracic PSO. An in vitro fresh-frozen human cadaveric biomechanical analysis. Seven human cadaveric thoracic spines were prepared and instrumented from T4-T10 with bilateral pedicle screws/rods and a PSO was performed at T7. Intact range of motion (ROM) testing was performed with nondestructive loading and analyzed by loading modality (axial rotation [AR], flexion/extension [FE], and lateral bending [LB]). Range of motion analysis was performed in the unaugmented construct, the construct augmented with one TC, and the construct augmented with two TCs. After PSO and an unaugmented longitudinal pedicle screw-rod construct, T4-T10 (overall construct) and T6-T8 (PSO site) ROMs were significantly reduced in all planes of motion compared with intact condition (AR: 11.8° vs. 31.7°; FE: 2.4° vs. 12.3°; 3.4° vs. 17.9°, respectively, p<.05). Augmentation of longitudinal construct with either one or two TCs did not significantly increase construct rigidity in FE or LB compared with the unaugmented construct (p>.05). In contrast, during AR, global ROM was significantly reduced by 43% and 48% at T6-T8 (1.7° and 1.2° vs. 2.38°, respectively) after addition of one and two TCs (p<.05), respectively. One TC did not significantly reduce torsional ROM from the intact state. Two TCs significantly improved torsional rigidity of the entire construct and at the PSO site, with no differences in rigidity for FE and LB or with the addition of only one TC. In the setting of a PSO and long-segment pedicle screw-rod construct, augmentation with at least two TCs should be considered to improve torsional rigidity. Published by Elsevier Inc.

Maxillary growth after the use of protraction head gear in conjunction with presurgical orthopedics and gingivoperiosteoplasty for complete bilateral cleft lip and alveolus patients.

PubMed

Kobayashi, Shinji; Hirakawa, Takashi; Fukawa, Toshihiko; Maegawa, Jiro

2013-09-01

Maxillary development is often inadequate in bilateral cleft patients. The use of presurgical orthopedics (PSO) and gingivoperiosteoplasty (GPP) may promote bone formation at the alveolar cleft, but can also have detrimental effects on maxillary development. Our objective was to investigate the effect of PSO and GPP on maxillary development in bilateral cleft lip and alveolus (BCLA) patients. We had 3 complete BCLA patients who had received PSO. All patients underwent cheiloplasty and GPP simultaneously. At 4 years, maxillary protraction head gear was used as part of the protocol. They were evaluated by cephalometric analysis at 4 and 8 years of age, and by CT imaging at 5 years of age. At 4 years of age, patients with all BCLA had anterior crossbite of deciduous central incisors. As a result of maxillary protraction, jaw development at 8 years was good. Among all patients, only one showed bone formation at the alveolar cleft sufficient to avoid alveolar bone grafting (ABG). All patients presented anterior crossbite in the premaxillary region, but had good maxillary growth at 8 years old as a result of maxillary protraction. The combination of PSO and GPP can potentially eliminate the need for ABG and does not significantly retard maxillary development. PSO with GPP and protraction head gear may be an option, but long-term growth is not known.

Radiologic and clinical outcomes comparison between single- and two-level pedicle subtraction osteotomies in correcting ankylosing spondylitis kyphosis.

PubMed

Xu, Hui; Zhang, Yonggang; Zhao, Yongfei; Zhang, Xuesong; Xiao, Songhua; Wang, Yan

2015-02-01

Single pedicle subtraction osteotomy (PSO) has been used to correct ankylosing spondylitis (AS) kyphosis successfully, but this approach seems insufficient to correct severe kyphosis. Two-level PSO has been attempted to correct advanced kyphosis in recent years. However, studies have not yet compared outcomes between single and double PSOs, and the indications to perform two-level PSO are unclear. This study aimed to compare the radiologic and clinical outcomes between single- and two-level PSOs in correcting AS kyphosis. This work is a retrospective cohort study. Sixty patients were included. Thirty-seven underwent single-level PSO, and 23 underwent one stage two-level PSO. The radiologic analysis included thoracic kyphosis, thoracolumbar junction, lumbar lordosis, pelvic index, chin-brow vertical angle (CBVA), sagittal vertical axis (SVA), and pelvic tilt (PT). Clinical assessment was performed with a Scoliosis Research Society-22 (SRS-22) outcomes instrument. The operative time, blood loss, and complications were also documented. All of the aforementioned measurements were recorded before surgery, after surgery, and at the last follow-up. The outcomes were compared between the two groups. The operating time was 232±52 minutes for single- and 282±43 minutes for two-level PSOs. The blood loss was 1,240±542 mL (Level 1) and 2,202±737 mL (Level 2). The total spine correction was 43.2°±15.1° (Level 1) and 60.6°±19.1° (Level 2) (p<.001), the SVA correction was 13.2±10.6 cm (Level 1) and 23.6±10.2 cm (Level 2) (p<.001), and the PT correction was 10.1°±11.6° (Level 1) and 15.2°±10.8° (Level 2) (p<.001). The CBVA correction was 50.6°±17.8° (Level 1) and 51.4°±18.6° in (Level 2) (p>.05). All patients could walk with horizontal vision and lie on their backs postoperatively. The SRS-22 improved from 1.7±0.4 to 4.2±0.8 in the two-level group and 1.8±0.8 to 4.3±0.7 in the single-level group. The fusion of the osteotomy was achieved in each patient. The complications were similar in both groups. Pedicle subtraction osteotomy is an effective method to correct kyphosis with AS. Most patients can be successfully treated by single PSO. In severe patients, two-level PSO may be preferable because its correction ability is greater and spine curvature is better than that of single-level PSO. However, two-level PSO requires an increased operating time and results in increased blood loss. Nevertheless, the complications were similar between the two groups. Copyright © 2015 Elsevier Inc. All rights reserved.

Affiliation, joint venture or PSO? Case studies show why provider strategies differ.

PubMed

1998-03-01

Joint venture, affiliation or PSO? Here are three case studies of providers who chose different paths under Medicare risk, plus some key questions you'll want to ask of your own provider organization. Learn from these examples so you'll make the best contracting decisions.

42 CFR 3.104 - Secretarial actions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 1 2011-10-01 2011-10-01 false Secretarial actions. 3.104 Section 3.104 Public... ORGANIZATIONS AND PATIENT SAFETY WORK PRODUCT PSO Requirements and Agency Procedures § 3.104 Secretarial actions. (a) Actions in response to certification submissions for initial and continued listing as a PSO. (1...

Multivariable optimization of liquid rocket engines using particle swarm algorithms

NASA Astrophysics Data System (ADS)

Jones, Daniel Ray

Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.

Discovery of a very Lyman-α-luminous quasar at z = 6.62.

PubMed

Koptelova, Ekaterina; Hwang, Chorng-Yuan; Yu, Po-Chieh; Chen, Wen-Ping; Guo, Jhen-Kuei

2017-02-02

Distant luminous quasars provide important information on the growth of the first supermassive black holes, their host galaxies and the epoch of reionization. The identification of quasars is usually performed through detection of their Lyman-α line redshifted to 0.9 microns at z > 6.5. Here, we report the discovery of a very Lyman-α luminous quasar, PSO J006.1240 + 39.2219 at redshift z = 6.618, selected based on its red colour and multi-epoch detection of the Lyman-α emission in a single near-infrared band. The Lyman-α line luminosity of PSO J006.1240 + 39.2219 is unusually high and estimated to be 0.8 × 10 12 Solar luminosities (about 3% of the total quasar luminosity). The Lyman-α emission of PSO J006.1240 + 39.2219 shows fast variability on timescales of days in the quasar rest frame, which has never been detected in any of the known high-redshift quasars. The high luminosity of the Lyman-α line, its narrow width and fast variability resemble properties of local Narrow-Line Seyfert 1 galaxies which suggests that the quasar is likely at the active phase of the black hole growth accreting close or even beyond the Eddington limit.

Discovery of a very Lyman-α-luminous quasar at z = 6.62

PubMed Central

Koptelova, Ekaterina; Hwang, Chorng-Yuan; Yu, Po-Chieh; Chen, Wen-Ping; Guo, Jhen-Kuei

2017-01-01

Distant luminous quasars provide important information on the growth of the first supermassive black holes, their host galaxies and the epoch of reionization. The identification of quasars is usually performed through detection of their Lyman-α line redshifted to 0.9 microns at z > 6.5. Here, we report the discovery of a very Lyman-α luminous quasar, PSO J006.1240 + 39.2219 at redshift z = 6.618, selected based on its red colour and multi-epoch detection of the Lyman-α emission in a single near-infrared band. The Lyman-α line luminosity of PSO J006.1240 + 39.2219 is unusually high and estimated to be 0.8 × 1012 Solar luminosities (about 3% of the total quasar luminosity). The Lyman-α emission of PSO J006.1240 + 39.2219 shows fast variability on timescales of days in the quasar rest frame, which has never been detected in any of the known high-redshift quasars. The high luminosity of the Lyman-α line, its narrow width and fast variability resemble properties of local Narrow-Line Seyfert 1 galaxies which suggests that the quasar is likely at the active phase of the black hole growth accreting close or even beyond the Eddington limit. PMID:28150701

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

PubMed Central

Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

2016-01-01

Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption. PMID:27428971

Implementation of Particle Swarm Optimization Method for Voltage Stability Analysis in 150 kV Sub System Grati – Paiton East Java

NASA Astrophysics Data System (ADS)

Kusumaningtyas, A. B.; Hidayat, M. N.; Ronilaya, F.

2018-04-01

Based on the data from State Electric Company on 15 January 2013, the undistributed power in the 150 kV sub system Grati-Paiton Region IV, that consist of 26 bus 150 kV and 2 bus generation 500 kV system, was recorded 3.286,00 MW. At the same time, the frequency of the system was down to 49 Hz. This lead to a deficit generation and unstable voltage condition in the system. Fast Voltage Stability Index (FVSI) method is used in this research to analyze the voltage stability of the buses. For buses with unstable voltage condition, reactive power will be injected through capacitor installation. The site where the capacitor will be installed is determined using the Fast Voltage Stability Index (FVSI) method while the size of the capacitor is determined using the Particle Swarm Optimization (PSO) method. The PSO method has been applied in some researches, such as to determine optimal placement and sizing in radial distribution network as well as in transmission network.. In this research, the PSO method is used to find the Qloss of an interconnection transmission system, which in turn, the value of the Qloss is used to determine the capacitance of the capacitor needed by the system.

Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yuan, Chunhua; Wang, Jiang; Yi, Guosheng

2017-03-01

Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

Obtaining Approximate Values of Exterior Orientation Elements of Multi-Intersection Images Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Li, X.; Li, S. W.

2012-07-01

In this paper, an efficient global optimization algorithm in the field of artificial intelligence, named Particle Swarm Optimization (PSO), is introduced into close range photogrammetric data processing. PSO can be applied to obtain the approximate values of exterior orientation elements under the condition that multi-intersection photography and a small portable plane control frame are used. PSO, put forward by an American social psychologist J. Kennedy and an electrical engineer R.C. Eberhart, is a stochastic global optimization method based on swarm intelligence, which was inspired by social behavior of bird flocking or fish schooling. The strategy of obtaining the approximate values of exterior orientation elements using PSO is as follows: in terms of image coordinate observed values and space coordinates of few control points, the equations of calculating the image coordinate residual errors can be given. The sum of absolute value of each image coordinate is minimized to be the objective function. The difference between image coordinate observed value and the image coordinate computed through collinear condition equation is defined as the image coordinate residual error. Firstly a gross area of exterior orientation elements is given, and then the adjustment of other parameters is made to get the particles fly in the gross area. After iterative computation for certain times, the satisfied approximate values of exterior orientation elements are obtained. By doing so, the procedures like positioning and measuring space control points in close range photogrammetry can be avoided. Obviously, this method can improve the surveying efficiency greatly and at the same time can decrease the surveying cost. And during such a process, only one small portable control frame with a couple of control points is employed, and there are no strict requirements for the space distribution of control points. In order to verify the effectiveness of this algorithm, two experiments are carried out. In the first experiment, images of a standard grid board are taken according to multi-intersection photography using digital camera. Three points or six points which are located on the left-down corner of the standard grid are regarded as control points respectively, and the exterior orientation elements of each image are computed through PSO, and compared with these elements computed through bundle adjustment. In the second experiment, the exterior orientation elements obtained from the first experiment are used as approximate values in bundle adjustment and then the space coordinates of other grid points on the board can be computed. The coordinate difference of grid points between these computed space coordinates and their known coordinates can be used to compute the accuracy. The point accuracy computed in above experiments are ±0.76mm and ±0.43mm respectively. The above experiments prove the effectiveness of PSO used in close range photogrammetry to compute approximate values of exterior orientation elements, and the algorithm can meet the requirement of higher accuracy. In short, PSO can get better results in a faster, cheaper way compared with other surveying methods in close range photogrammetry.

Particle swarm optimization applied to automatic lens design

NASA Astrophysics Data System (ADS)

Qin, Hua

2011-06-01

This paper describes a novel application of Particle Swarm Optimization (PSO) technique to lens design. A mathematical model is constructed, and merit functions in an optical system are employed as fitness functions, which combined radiuses of curvature, thicknesses among lens surfaces and refractive indices regarding an optical system. By using this function, the aberration correction is carried out. A design example using PSO is given. Results show that PSO as optical design tools is practical and powerful, and this method is no longer dependent on the lens initial structure and can arbitrarily create search ranges of structural parameters of a lens system, which is an important step towards automatic design with artificial intelligence.

76 FR 67456 - Common Formats for Patient Safety Data Collection and Event Reporting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-01

... Common Formats, can be accessed electronically at the following HHS Web site: http://www.PSO.AHRQ.gov... Thromboembolism (VTE), which includes Deep Vein Thrombosis (DVT) and Pulmonary Embolism (PE), will apply to both... available at the PSO Privacy Protection Center (PPC) Web site: https://www.psoppc.org/web/patientsafety...

Presurgical cleft lip and palate orthopedics: an overview

PubMed Central

Alzain, Ibtesam; Batwa, Waeil; Cash, Alex; Murshid, Zuhair A

2017-01-01

Patients with cleft lip and/or palate go through a lifelong journey of multidisciplinary care, starting from before birth and extending until adulthood. Presurgical orthopedic (PSO) treatment is one of the earliest stages of this care plan. In this paper we provide a review of the PSO treatment. This review should help general and specialist dentists to better understand the cleft patient care path and to be able to answer patient queries more efficiently. The objectives of this paper were to review the basic principles of PSO treatment, the various types of techniques used in this therapy, and the protocol followed, and to critically evaluate the advantages and disadvantages of some of these techniques. In conclusion, we believe that PSO treatment, specifically nasoalveolar molding, does help to approximate the segments of the cleft maxilla and does reduce the intersegment space in readiness for the surgical closure of cleft sites. However, what we remain unable to prove equivocally at this point is whether the reduction in the dimensions of the cleft presurgically and the manipulation of the nasal complex benefit our patients in the long term. PMID:28615974

Numerical Computation of Homogeneous Slope Stability

PubMed Central

Xiao, Shuangshuang; Li, Kemin; Ding, Xiaohua; Liu, Tong

2015-01-01

To simplify the computational process of homogeneous slope stability, improve computational accuracy, and find multiple potential slip surfaces of a complex geometric slope, this study utilized the limit equilibrium method to derive expression equations of overall and partial factors of safety. This study transformed the solution of the minimum factor of safety (FOS) to solving of a constrained nonlinear programming problem and applied an exhaustive method (EM) and particle swarm optimization algorithm (PSO) to this problem. In simple slope examples, the computational results using an EM and PSO were close to those obtained using other methods. Compared to the EM, the PSO had a small computation error and a significantly shorter computation time. As a result, the PSO could precisely calculate the slope FOS with high efficiency. The example of the multistage slope analysis indicated that this slope had two potential slip surfaces. The factors of safety were 1.1182 and 1.1560, respectively. The differences between these and the minimum FOS (1.0759) were small, but the positions of the slip surfaces were completely different than the critical slip surface (CSS). PMID:25784927

Numerical computation of homogeneous slope stability.

PubMed

Xiao, Shuangshuang; Li, Kemin; Ding, Xiaohua; Liu, Tong

2015-01-01

To simplify the computational process of homogeneous slope stability, improve computational accuracy, and find multiple potential slip surfaces of a complex geometric slope, this study utilized the limit equilibrium method to derive expression equations of overall and partial factors of safety. This study transformed the solution of the minimum factor of safety (FOS) to solving of a constrained nonlinear programming problem and applied an exhaustive method (EM) and particle swarm optimization algorithm (PSO) to this problem. In simple slope examples, the computational results using an EM and PSO were close to those obtained using other methods. Compared to the EM, the PSO had a small computation error and a significantly shorter computation time. As a result, the PSO could precisely calculate the slope FOS with high efficiency. The example of the multistage slope analysis indicated that this slope had two potential slip surfaces. The factors of safety were 1.1182 and 1.1560, respectively. The differences between these and the minimum FOS (1.0759) were small, but the positions of the slip surfaces were completely different than the critical slip surface (CSS).

Optimization Of Feature Weight TheVoting Feature Intervals 5 Algorithm Using Partical Swarm Optimization Algorithm

NASA Astrophysics Data System (ADS)

Hayana Hasibuan, Eka; Mawengkang, Herman; Efendi, Syahril

2017-12-01

The use of Partical Swarm Optimization Algorithm in this research is to optimize the feature weights on the Voting Feature Interval 5 algorithm so that we can find the model of using PSO algorithm with VFI 5. Optimization of feature weight on Diabetes or Dyspesia data is considered important because it is very closely related to the livelihood of many people, so if there is any inaccuracy in determining the most dominant feature weight in the data will cause death. Increased accuracy by using PSO Algorithm ie fold 1 from 92.31% to 96.15% increase accuracy of 3.8%, accuracy of fold 2 on Algorithm VFI5 of 92.52% as well as generated on PSO Algorithm means accuracy fixed, then in fold 3 increase accuracy of 85.19% Increased to 96.29% Accuracy increased by 11%. The total accuracy of all three trials increased by 14%. In general the Partical Swarm Optimization algorithm has succeeded in increasing the accuracy to several fold, therefore it can be concluded the PSO algorithm is well used in optimizing the VFI5 Classification Algorithm.

A Novel Model for Stock Price Prediction Using Hybrid Neural Network

NASA Astrophysics Data System (ADS)

Senapati, Manas Ranjan; Das, Sumanjit; Mishra, Sarojananda

2018-06-01

The foremost challenge for investors is to select stock price by analyzing financial data which is a menial task as of distort associated and massive pattern. Thereby, selecting stock poses one of the greatest difficulties for investors. Nowadays, prediction of financial market like stock market, exchange rate and share value are very challenging field of research. The prediction and scrutinization of stock price is also a potential area of research due to its vital significance in decision making by financial investors. This paper presents an intelligent and an optimal model for prophecy of stock market price using hybridization of Adaline Neural Network (ANN) and modified Particle Swarm Optimization (PSO). The connoted model hybrid of Adaline and PSO uses fluctuations of stock market as a factor and employs PSO to optimize and update weights of Adaline representation to depict open price of Bombay stock exchange. The prediction performance of the proposed model is compared with different representations like interval measurements, CMS-PSO and Bayesian-ANN. The result indicates that proposed scheme has an edge over all the juxtaposed schemes in terms of mean absolute percentage error.

A Grouping Particle Swarm Optimizer with Personal-Best-Position Guidance for Large Scale Optimization.

PubMed

Guo, Weian; Si, Chengyong; Xue, Yu; Mao, Yanfen; Wang, Lei; Wu, Qidi

2017-05-04

Particle Swarm Optimization (PSO) is a popular algorithm which is widely investigated and well implemented in many areas. However, the canonical PSO does not perform well in population diversity maintenance so that usually leads to a premature convergence or local optima. To address this issue, we propose a variant of PSO named Grouping PSO with Personal- Best-Position (Pbest) Guidance (GPSO-PG) which maintains the population diversity by preserving the diversity of exemplars. On one hand, we adopt uniform random allocation strategy to assign particles into different groups and in each group the losers will learn from the winner. On the other hand, we employ personal historical best position of each particle in social learning rather than the current global best particle. In this way, the exemplars diversity increases and the effect from the global best particle is eliminated. We test the proposed algorithm to the benchmarks in CEC 2008 and CEC 2010, which concern the large scale optimization problems (LSOPs). By comparing several current peer algorithms, GPSO-PG exhibits a competitive performance to maintain population diversity and obtains a satisfactory performance to the problems.

Control strategy of grid-connected photovoltaic generation system based on GMPPT method

NASA Astrophysics Data System (ADS)

Wang, Zhongfeng; Zhang, Xuyang; Hu, Bo; Liu, Jun; Li, Ligang; Gu, Yongqiang; Zhou, Bowen

2018-02-01

There are multiple local maximum power points when photovoltaic (PV) array runs under partial shading condition (PSC).However, the traditional maximum power point tracking (MPPT) algorithm might be easily trapped in local maximum power points (MPPs) and cannot find the global maximum power point (GMPP). To solve such problem, a global maximum power point tracking method (GMPPT) is improved, combined with traditional MPPT method and particle swarm optimization (PSO) algorithm. Under different operating conditions of PV cells, different tracking algorithms are used. When the environment changes, the improved PSO algorithm is adopted to realize the global optimal search, and the variable step incremental conductance (INC) method is adopted to achieve MPPT in optimal local location. Based on the simulation model of the PV grid system built in Matlab/Simulink, comparative analysis of the tracking effect of MPPT by the proposed control algorithm and the traditional MPPT method under the uniform solar condition and PSC, validate the correctness, feasibility and effectiveness of the proposed control strategy.

Design and implementation of intelligent electronic warfare decision making algorithm

NASA Astrophysics Data System (ADS)

Peng, Hsin-Hsien; Chen, Chang-Kuo; Hsueh, Chi-Shun

2017-05-01

Electromagnetic signals and the requirements of timely response have been a rapid growth in modern electronic warfare. Although jammers are limited resources, it is possible to achieve the best electronic warfare efficiency by tactical decisions. This paper proposes the intelligent electronic warfare decision support system. In this work, we develop a novel hybrid algorithm, Digital Pheromone Particle Swarm Optimization, based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Shuffled Frog Leaping Algorithm (SFLA). We use PSO to solve the problem and combine the concept of pheromones in ACO to accumulate more useful information in spatial solving process and speed up finding the optimal solution. The proposed algorithm finds the optimal solution in reasonable computation time by using the method of matrix conversion in SFLA. The results indicated that jammer allocation was more effective. The system based on the hybrid algorithm provides electronic warfare commanders with critical information to assist commanders in effectively managing the complex electromagnetic battlefield.

Color image enhancement based on particle swarm optimization with Gaussian mixture

NASA Astrophysics Data System (ADS)

Kattakkalil Subhashdas, Shibudas; Choi, Bong-Seok; Yoo, Ji-Hoon; Ha, Yeong-Ho

2015-01-01

This paper proposes a Gaussian mixture based image enhancement method which uses particle swarm optimization (PSO) to have an edge over other contemporary methods. The proposed method uses the guassian mixture model to model the lightness histogram of the input image in CIEL*a*b* space. The intersection points of the guassian components in the model are used to partition the lightness histogram. . The enhanced lightness image is generated by transforming the lightness value in each interval to appropriate output interval according to the transformation function that depends on PSO optimized parameters, weight and standard deviation of Gaussian component and cumulative distribution of the input histogram interval. In addition, chroma compensation is applied to the resulting image to reduce washout appearance. Experimental results show that the proposed method produces a better enhanced image compared to the traditional methods. Moreover, the enhanced image is free from several side effects such as washout appearance, information loss and gradation artifacts.

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.

Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.

PubMed

Best, Myron G; Sol, Nik; In 't Veld, Sjors G J G; Vancura, Adrienne; Muller, Mirte; Niemeijer, Anna-Larissa N; Fejes, Aniko V; Tjon Kon Fat, Lee-Ann; Huis In 't Veld, Anna E; Leurs, Cyra; Le Large, Tessa Y; Meijer, Laura L; Kooi, Irsan E; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Wedekind, Laurine E; Bracht, Jillian; Esenkbrink, Michelle; Wils, Leon; Favaro, Francesca; Schoonhoven, Jilian D; Tannous, Jihane; Meijers-Heijboer, Hanne; Kazemier, Geert; Giovannetti, Elisa; Reijneveld, Jaap C; Idema, Sander; Killestein, Joep; Heger, Michal; de Jager, Saskia C; Urbanus, Rolf T; Hoefer, Imo E; Pasterkamp, Gerard; Mannhalter, Christine; Gomez-Arroyo, Jose; Bogaard, Harm-Jan; Noske, David P; Vandertop, W Peter; van den Broek, Daan; Ylstra, Bauke; Nilsson, R Jonas A; Wesseling, Pieter; Karachaliou, Niki; Rosell, Rafael; Lee-Lewandrowski, Elizabeth; Lewandrowski, Kent B; Tannous, Bakhos A; de Langen, Adrianus J; Smit, Egbert F; van den Heuvel, Michel M; Wurdinger, Thomas

2017-08-14

Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83-0.95; p < 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

PubMed

Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

2015-01-01

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

PubMed Central

2015-01-01

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

Estimating of aquifer parameters from the single-well water-level measurements in response to advancing longwall mine by using particle swarm optimization

NASA Astrophysics Data System (ADS)

Buyuk, Ersin; Karaman, Abdullah

2017-04-01

We estimated transmissivity and storage coefficient values from the single well water-level measurements positioned ahead of the mining face by using particle swarm optimization (PSO) technique. The water-level response to the advancing mining face contains an semi-analytical function that is not suitable for conventional inversion shemes because the partial derivative is difficult to calculate . Morever, the logaritmic behaviour of the model create difficulty for obtaining an initial model that may lead to a stable convergence. The PSO appears to obtain a reliable solution that produce a reasonable fit between water-level data and model function response. Optimization methods have been used to find optimum conditions consisting either minimum or maximum of a given objective function with regard to some criteria. Unlike PSO, traditional non-linear optimization methods have been used for many hydrogeologic and geophysical engineering problems. These methods indicate some difficulties such as dependencies to initial model, evolution of the partial derivatives that is required while linearizing the model and trapping at local optimum. Recently, Particle swarm optimization (PSO) became the focus of modern global optimization method that is inspired from the social behaviour of birds of swarms, and appears to be a reliable and powerful algorithms for complex engineering applications. PSO that is not dependent on an initial model, and non-derivative stochastic process appears to be capable of searching all possible solutions in the model space either around local or global optimum points.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

PubMed

Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

PubMed Central

Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software. PMID:28263994

Particle Swarm Optimization With Interswarm Interactive Learning Strategy.

PubMed

Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui

2016-10-01

The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.

Identification of eggs from different production systems based on hyperspectra and CS-SVM.

PubMed

Sun, J; Cong, S L; Mao, H P; Zhou, X; Wu, X H; Zhang, X D

2017-06-01

1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied. 2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables combined with a Savitzky-Golay were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) methods to establish an SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model, while egg category was the output. 3. The SWR-CS-SVM model performed better than the other models, including SWR-GS-SVM, SWR-GA-SVM, SWR-PSO-SVM and others based on full spectral data. The training and test classification accuracy of the SWR-CS-SVM model were respectively 99.3% and 96%. 4. SWR-CS-SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.

T-helper immune phenotype may underlie 'paradoxical' tumour necrosis factor-α inhibitor therapy-related psoriasiform dermatitis.

PubMed

Moy, A P; Murali, M; Kroshinsky, D; Horn, T D; Nazarian, R M

2018-01-01

Therapeutics targeting tumour necrosis factor (TNF)-α are effective for psoriasis; however, in patients treated for other disorders, psoriasis may worsen and psoriasiform dermatitis (PsoD) may arise. T helper (Th) cytokines in psoriasis upregulate keratin (K)17, which modulates TNF-α transduction, leading to vascular adhesion molecule upregulation and lymphocytic extravasation. We investigated Th phenotype and expression of K17, intercellular adhesion molecule (ICAM)-1 and vascular adhesion molecule (VCAM)-1 in psoriasis and anti-TNF-α-related PsoD. Skin biopsies from patients with psoriasis unresponsive to TNF-α inhibitor therapy (n = 11), PsoD-related to TNF-α inhibition (n = 9), untreated psoriasis (n = 9) or atopic dermatitis (AD; n = 9) were immunohistochemically analysed for Th1, Th2, Th17 and Th22. Expression of K17, ICAM-1 and VCAM-1 was also examined. Anti-TNF-α-unresponsive psoriasis and anti-TNF-α-related PsoD showed decreased Th1 : Th2 raio and increased Th17 : Th1 ratio compared with untreated psoriasis. Anti-TNF-α-unresponsive psoriasis had significantly fewer Th1 (4% vs. 12%) and more Th17 (51% vs. 20%) cells than untreated psoriasis. No difference in Th22 cells was identified. K17 was present in all cases of untreated psoriasis and anti-TNF-α-related PsoD, 91% of anti-TNF-α-unresponsive psoriasis, and only 22% of AD. VCAM-1 and ICAM-1 in anti-TNF-α-related PsoD was akin to untreated psoriasis, but decreased in anti-TNF-α-unresponsive psoriasis. These findings further the current understanding of the anti-TNF-α-related psoriasiform phenotype and support a rationale for therapeutic targeting of interleukin-17 and TNF-α in combination. © 2017 British Association of Dermatologists.

Components of a Fanconi-like pathway control Pso2-independent DNA interstrand crosslink repair in yeast.

PubMed

Ward, Thomas A; Dudášová, Zuzana; Sarkar, Sovan; Bhide, Mangesh R; Vlasáková, Danuša; Chovanec, Miroslav; McHugh, Peter J

2012-01-01

Fanconi anemia (FA) is a devastating genetic disease, associated with genomic instability and defects in DNA interstrand cross-link (ICL) repair. The FA repair pathway is not thought to be conserved in budding yeast, and although the yeast Mph1 helicase is a putative homolog of human FANCM, yeast cells disrupted for MPH1 are not sensitive to ICLs. Here, we reveal a key role for Mph1 in ICL repair when the Pso2 exonuclease is inactivated. We find that the yeast FANCM ortholog Mph1 physically and functionally interacts with Mgm101, a protein previously implicated in mitochondrial DNA repair, and the MutSα mismatch repair factor (Msh2-Msh6). Co-disruption of MPH1, MGM101, MSH6, or MSH2 with PSO2 produces a lesion-specific increase in ICL sensitivity, the elevation of ICL-induced chromosomal rearrangements, and persistence of ICL-associated DNA double-strand breaks. We find that Mph1-Mgm101-MutSα directs the ICL-induced recruitment of Exo1 to chromatin, and we propose that Exo1 is an alternative 5'-3' exonuclease utilised for ICL repair in the absence of Pso2. Moreover, ICL-induced Rad51 chromatin loading is delayed when both Pso2 and components of the Mph1-Mgm101-MutSα and Exo1 pathway are inactivated, demonstrating that the homologous recombination stages of ICL repair are inhibited. Finally, the FANCJ- and FANCP-related factors Chl1 and Slx4, respectively, are also components of the genetic pathway controlled by Mph1-Mgm101-MutSα. Together this suggests that a prototypical FA-related ICL repair pathway operates in budding yeast, which acts redundantly with the pathway controlled by Pso2, and is required for the targeting of Exo1 to chromatin to execute ICL repair.

Effects of pomegranate seed oil on oxidant/antioxidant balance in heart and kidney homogenates and mitochondria of diabetic rats and high glucose-treated H9c2 cell line

PubMed Central

Mollazadeh, Hamid; Boroushaki, Mohammad Taher; Soukhtanloo, Mohammad; Afshari, Amir Reza; Vahedi, Mohammad Mahdi

2017-01-01

Objective: Oxidative stress is a major cause of diabetes complications. The present study aimed to investigate the beneficial effects of Pomegranate Seed Oil (PSO) on diabetes-induced changes in oxidant/antioxidant balance of the kidney, heart and mitochondria from rats and H9c2 cell line. Materials and Methods: In these in vivo and in vitro studies, male rats were divided into four groups (twelve each): group 1 served as control, group 2-4 received a single dose of streptozotocin (60 mg/kg, i.p), groups 3 and 4 received PSO (0.36 and 0.72 mg/kg/daily, gavage), respectively. After three weeks, six rats of each group and one week later the remaining animals were anaesthetized and the hearts and kidneys were removed and homogenized. Mitochondrial fractions were separated and enzyme activities were measured in each sample. H9c2 cells were pretreated with high levels of glucose (35 mM), and then, incubated with PSO. Finally, cell viability test, reactive oxygen species production and lipid peroxidation were evaluated. Results: Significant reduction in enzymes activity (Superoxide dismutase, Glutathione S-transferase and Paraoxonase 1), compensatory elevation in Glutathione Reductase, Glutathione Peroxidase and Catalase activity followed by reduction after one week and significant elevation in Oxidative Stress Index (OSI) were observed in diabetic group. PSO treatment resulted in a significant increase in enzymes activity and decreased OSI values compared to diabetic group in both tissue and mitochondrial fractions. PSO remarkably decreased glucose-induced toxicity, ROS level and lipid peroxidation in H9c2 cells. Conclusion: Results suggested that PSO has a protective effect against diabetes-induced alterations in oxidant/antioxidant balance in tissues, mitochondrial and H9c2 cell line. PMID:28884082

Kinematic efficacy of supplemental anterior lumbar interbody fusion at lumbosacral levels in thoracolumbosacral deformity correction with and without pedicle subtraction osteotomy at L3: an in vitro cadaveric study.

PubMed

Dahl, Benny T; Harris, Jonathan A; Gudipally, Manasa; Moldavsky, Mark; Khalil, Saif; Bucklen, Brandon S

2017-11-01

Pedicle subtraction osteotomy (PSO) is performed to treat rigid, sagittal spinal deformities, but high rates of implant failure are reported. Anterior lumbar interbody fusion has been proposed to reduce this risk, but biomechanical investigation is lacking. The goal of this study was to quantify the (1) destabilizing effects of a lumbar osteotomy and (2) contribution of anterior lumbar interbody fusion (ALIF) at the lumbosacral junction as recommended in literature. Fourteen fresh human thoracolumbosacral spines (T12-S1) were tested in flexion-extension (FE), lateral bending (LB), and axial rotation (AR). Bilateral pedicle screws/rods (BPS) were inserted at T12-S1, cross connectors (CC) at T12-L1 and L5-S1, and anterior interbody spacers (S) at L4-5 and L5-S1. In one group, PSO was performed in seven specimens at L3. All specimens were sequentially tested in (1) Intact; (2) BPS; (3) BPS + CC; (4) BPS + S; and (5) BPS + S + CC; a second group of seven spines were tested in the same sequence without PSO. Mixed-model ANOVA with repeated measures was performed (p ≤ 0.05). At the osteotomy site (L2-L4), in FE, BPS, BPS + CC, BPS + S, BPS + CC + S reduced motion to 11.2, 12.9, 10.9, and 11.4%, respectively, with significance only found in BPS and BPS + S construction (p ≤ 0.05). All constructs significantly reduced motion across L2-L4 in the absence of PSO, across all loading modes (p ≤ 0.05). PSO significantly destabilized L2-L4 axial rotational stability, regardless of operative construction (p ≤ 0.05). Across L4-S1 and L2-S1, all instrumented constructs significantly reduced motion, in both PSO- and non-PSO groups, during all loading modes (p ≤ 0.05). These findings suggest anterior interbody fusion minimally immobilizes motion segments, and interbody devices may primarily act to maintain disc height. Additionally, lumbar osteotomy destabilizes axial rotational stability at the osteotomy site, potentially further increasing mechanical demand on posterior instrumentation. Clinical studies are needed to assess the impact of this treatment strategy.

Walking sagittal balance correction by pedicle subtraction osteotomy in adults with fixed sagittal imbalance.

PubMed

Yagi, Mitsuru; Kaneko, Shinjiro; Yato, Yoshiyuki; Asazuma, Takashi; Machida, Masafumi

2016-08-01

Pedicle subtraction osteotomy (PSO) is widely used to treat severe fixed sagittal imbalance. However, the effect of PSO on balance has not been fully documented. The aim of this study was to assess dynamic walking balance after PSO to treat fixed sagittal imbalance. Gait and balance were assessed in 15 consecutive adult female patients who had been treated by PSO for a fixed sagittal imbalance and compare patients' preop and postop dynamic walking balance with that of 15 age- and gender-matched healthy volunteers (HV). Each patient's chart, X-rays, pre and postop SRS22 outcome scores, and ODI were reviewed. Means were compared by Mann-Whitney U test and Chi-square test. The mean age was 66.3 years (51-74 years). The mean follow-up was 2.7 years (2-3.5 years). The C7PL and GL, measured on the force platform, were both improved from 24.2 ± 7.3 cm and 27.6 ± 9.4 to 5.4 ± 2.6 cm and 7.2 ± 3.4 cm, respectively. The baseline hip ROM was significantly smaller in patients compared to HV, whereas no significant difference was observed in the knee or ankle ROM. The pelvic tilt (preop -0.4° ± 1.4°, postop 8.9° ± 1.0°), and maximum hip-extension angle (preop -1.2° ± 14.2°, postop -11.2° ± 7.2°) were also improved after surgery. Cadence (116 s/min), stance-swing ratio (stance 63.2 % vs. swing 36.8 %), and stride (98.0 cm) were all increased after surgery. On the other hand, gait velocity was significantly slower in the PSO group at both pre and postop than in HV (PSO 53.3 m/min at preop and 58.8 m/min at postop vs. HV 71.1 m/min, p = 0.04). Despite a mild residual spinal-pelvic malalignment, PSO restored sagittal alignment and balance satisfactorily and has improved the gait pattern.

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems.

PubMed

Wang, Handing; Jin, Yaochu; Doherty, John

2017-09-01

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm.

PubMed

Wu, Jianfa; Peng, Dahao; Li, Zhuping; Zhao, Li; Ling, Huanzhang

2015-01-01

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data.

Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

NASA Astrophysics Data System (ADS)

Yan, Liang; Zhang, Lu; Zhu, Bo; Zhang, Jingying; Jiao, Zongxia

2017-10-01

Permanent magnet spherical actuator (PMSA) is a multi-variable featured and inter-axis coupled nonlinear system, which unavoidably compromises its motion control implementation. Uncertainties such as external load and friction torque of ball bearing and manufacturing errors also influence motion performance significantly. Therefore, the objective of this paper is to propose a controller based on a single neural adaptive (SNA) algorithm and a neural network (NN) identifier optimized with a particle swarm optimization (PSO) algorithm to improve the motion stability of PMSA with three-dimensional magnet arrays. The dynamic model and computed torque model are formulated for the spherical actuator, and a dynamic decoupling control algorithm is developed. By utilizing the global-optimization property of the PSO algorithm, the NN identifier is trained to avoid locally optimal solution and achieve high-precision compensations to uncertainties. The employment of the SNA controller helps to reduce the effect of compensation errors and convert the system to a stable one, even if there is difference between the compensations and uncertainties due to external disturbances. A simulation model is established, and experiments are conducted on the research prototype to validate the proposed control algorithm. The amplitude of the parameter perturbation is set to 5%, 10%, and 15%, respectively. The strong robustness of the proposed hybrid algorithm is validated by the abundant simulation data. It shows that the proposed algorithm can effectively compensate the influence of uncertainties and eliminate the effect of inter-axis couplings of the spherical actuator.

A Novel Adjustment Method for Shearer Traction Speed through Integration of T-S Cloud Inference Network and Improved PSO

PubMed Central

Si, Lei; Wang, Zhongbin; Yang, Yinwei

2014-01-01

In order to efficiently and accurately adjust the shearer traction speed, a novel approach based on Takagi-Sugeno (T-S) cloud inference network (CIN) and improved particle swarm optimization (IPSO) is proposed. The T-S CIN is built through the combination of cloud model and T-S fuzzy neural network. Moreover, the IPSO algorithm employs parameter automation adjustment strategy and velocity resetting to significantly improve the performance of basic PSO algorithm in global search and fine-tuning of the solutions, and the flowchart of proposed approach is designed. Furthermore, some simulation examples are carried out and comparison results indicate that the proposed method is feasible, efficient, and is outperforming others. Finally, an industrial application example of coal mining face is demonstrated to specify the effect of proposed system. PMID:25506358

Towards the novel reasoning among particles in PSO by the use of RDF and SPARQL.

PubMed

Fister, Iztok; Yang, Xin-She; Ljubič, Karin; Fister, Dušan; Brest, Janez; Fister, Iztok

2014-01-01

The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose. These tools are combined with particle swarm optimization (PSO) and the selection of the best solutions depends on its fitness. Instead of the local best solution, a neighborhood of solutions for each particle can be defined and used for the calculation of the new position, based on the key ideas from semantic web domain. The preliminary results by optimizing ten benchmark functions showed the promising results and thus this method should be investigated further.

P300 Chinese input system based on Bayesian LDA.

PubMed

Jin, Jing; Allison, Brendan Z; Brunner, Clemens; Wang, Bei; Wang, Xingyu; Zhang, Jianhua; Neuper, Christa; Pfurtscheller, Gert

2010-02-01

A brain-computer interface (BCI) is a new communication channel between humans and computers that translates brain activity into recognizable command and control signals. Attended events can evoke P300 potentials in the electroencephalogram. Hence, the P300 has been used in BCI systems to spell, control cursors or robotic devices, and other tasks. This paper introduces a novel P300 BCI to communicate Chinese characters. To improve classification accuracy, an optimization algorithm (particle swarm optimization, PSO) is used for channel selection (i.e., identifying the best electrode configuration). The effects of different electrode configurations on classification accuracy were tested by Bayesian linear discriminant analysis offline. The offline results from 11 subjects show that this new P300 BCI can effectively communicate Chinese characters and that the features extracted from the electrodes obtained by PSO yield good performance.

Energy and operation management of a microgrid using particle swarm optimization

NASA Astrophysics Data System (ADS)

Radosavljević, Jordan; Jevtić, Miroljub; Klimenta, Dardan

2016-05-01

This article presents an efficient algorithm based on particle swarm optimization (PSO) for energy and operation management (EOM) of a microgrid including different distributed generation units and energy storage devices. The proposed approach employs PSO to minimize the total energy and operating cost of the microgrid via optimal adjustment of the control variables of the EOM, while satisfying various operating constraints. Owing to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties and market prices, a probabilistic approach in the EOM is introduced. The proposed method is examined and tested on a typical grid-connected microgrid including fuel cell, gas-fired microturbine, wind turbine, photovoltaic and energy storage devices. The obtained results prove the efficiency of the proposed approach to solve the EOM of the microgrids.

Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

NASA Astrophysics Data System (ADS)

Jude Hemanth, Duraisamy; Umamaheswari, Subramaniyan; Popescu, Daniela Elena; Naaji, Antoanela

2016-01-01

Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.

Physical and nutritional properties of baby food containing added red salmon oil (Oncorhynchus nerka) and microencapsulated red salmon oil

USDA-ARS?s Scientific Manuscript database

Unpurified red salmon oil (UPSO) was purified (PSO) using chitosan. Both unpurified and purified oils were evaluated for peroxide value (PV), free fatty acids (FFA), fatty acid methyl esters (FAME), moisture, and color. An emulsion system containing PSO (EPSO) was prepared: system was analyzed for c...

Problem-Solving Orientation and Attributional Style as Predictors of Depressive Symptoms in Egyptian Adolescents with Visual Impairment

ERIC Educational Resources Information Center

Emam, Mahmoud M.

2013-01-01

The association between attributional style (AS), problem-solving orientation (PSO), and gender on depressive symptoms was investigated in Egyptian adolescents with visual impairment (VI). After being written in Braille, measures of AS, PSO, and depression were administered to 110 adolescents with VI, ages 12-17 years, from a residential school…

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

The Ontario Psychosocial Oncology Framework: a quality improvement tool.

PubMed

Li, Madeline; Green, Esther

2013-05-01

To overview the newly developed Psychosocial Health Care for Cancer Patients and Their Families: A Framework to Guide Practice in Ontario and Guideline Recommendations in the context of Canadian psychosocial oncology care and propose strategies for guideline uptake and implementation. Recommendations from the 2008 Institute of Medicine standard Cancer Care for the Whole Patient: Meeting Psychosocial Health Needs were adapted into the Ontario Psychosocial Oncology (PSO) Framework. Existing practice guidelines developed by the Canadian Partnership against Cancer and Cancer Care Ontario and standards developed by the Canadian Association of Psychosocial Oncology are supporting resources for adopting a quality improvement (QI) approach to the implementation of the framework in Ontario. The developed PSO Framework, including 31 specific actionable recommendations, is intended to improve the quality of comprehensive cancer care at both the provider and system levels. Important QI change management processes are described as Educate - raising awareness among medical teams of the significance of psychosocial needs of patients, Evidence - developing a research evidence base for patient care benefits from psychosocial interventions, and Electronics - using technology to collect patient reported outcomes of both physical and emotional symptoms. The Ontario PSO Framework is unique and valuable in providing actionable recommendations that can be implemented through QI processes. Overall, the result will be improved psychosocial health care for the cancer population. Copyright © 2012 John Wiley & Sons, Ltd.

A method for fast selecting feature wavelengths from the spectral information of crop nitrogen

USDA-ARS?s Scientific Manuscript database

Research on a method for fast selecting feature wavelengths from the nitrogen spectral information is necessary, which can determine the nitrogen content of crops. Based on the uniformity of uniform design, this paper proposed an improved particle swarm optimization (PSO) method. The method can ch...

Sacroiliac joint luxation after pedicle subtraction osteotomy: report of two cases and analysis of failure mechanism.

PubMed

Charles, Yann Philippe; Yu, Bo; Steib, Jean-Paul

2016-05-01

Sagittal decompensation after pedicle subtraction osteotomy (PSO) is considered as late onset complication. Several mechanisms have been suggested, but little attention has been paid to the caudal end of lumbar instrumented fusion, especially sacral iliac joint (SIJ) deterioration. Clinical histories and radiographic sagittal parameters of two patients with SIJ luxation after PSO are presented. The biomechanical failure mechanism and risk factors are analysed. Two patients underwent correction of fixed anterior sagittal imbalance by PSO, followed by pseudarthrosis revision surgery. Both of them sustained persistent sacroiliac pain, progressive recurrence of anterior imbalance and progressive pelvic incidence (PI) increase around 10°. An acute bilateral SIJ luxation occurred in both patients leading to sharp increase or PI around 20°. One patient was treated by SIJ fusion and the other patient was placed on non-weight-bearing crutch ambulation for 1 year. Both patients had a high preoperative PI (95° and 78°). A theoretical match between lumbar lordosis (LL) and PI was not achieved by PSO. Osteopenia was present in both patients. Computed tomography evidenced L5-S1 pseudarthrosis and sacroiliac joint violation by pelvic or sacral ala screws. Patients with high PI might seek for further compensation at their SIJ when lacking LL after PSO. Chronic anterior imbalance might lead to progressive weakening of sacroiliac ligaments. Initial circumferential lumbosacral fusion and accurate iliac screw fixation might reduce stress on implants, risk for pseudarthrosis, implant failure and finally SIJ deterioration. Bone mineral density should further be investigated preoperatively.

Discovery and measurement of an isotopically distinct source of sulfate in Earth's atmosphere

PubMed Central

Dominguez, Gerardo; Jackson, Terri; Brothers, Lauren; Barnett, Burton; Nguyen, Bryan; Thiemens, Mark H.

2008-01-01

Sulfate (SO4) and its precursors are significant components of the atmosphere, with both natural and anthropogenic sources. Recently, our triple-isotope (16O, 17O, 18O) measurements of atmospheric sulfate have provided specific insights into the oxidation pathways leading to sulfate, with important implications for models of the sulfur cycle and global climate change. Using similar isotopic measurements of aerosol sulfate in a polluted marine boundary layer (MBL) and primary sulfate (p-SO4) sampled directly from a ship stack, we quantify the amount of p-SO4 found in the atmosphere from ships. We find that ships contribute between 10% and 44% of the non-sea-salt sulfate found in fine [diameter (D) < 1.5 μm) particulate matter in coastal Southern California. These fractions are surprising, given that p-SO4 constitutes ≈2–7% of total sulfur emissions from combustion sources [Seinfed JH, Pandis SN (2006) Atmospheric Chemistry and Physics (Wiley–Interscience, New York)]. Our findings also suggest that the interaction of SO2 from ship emissions with coarse hydrated sea salt particles may lead to the rapid removal of SO2 in the MBL. When combined with the longer residence time of p-SO4 emissions in the MBL, these findings suggest that the importance of p-SO4 emissions in marine environments may be underappreciated in global chemical models. Given the expected increase of international shipping in the years to come, these findings have clear implications for public health, air quality, international maritime law, and atmospheric chemistry. PMID:18753618

Comparison of ultrasound imaging in transverse median and parasagittal oblique planes for thoracic epidurals: A pilot study.

PubMed

Khemka, Rakhi; Rastogi, Sonal; Desai, Neha; Chakraborty, Arunangshu; Sinha, Subir

2016-06-01

The use of ultrasound (US) scanning to assess the depth of epidural space to prevent neurological complications is established in current practice. In this study, we hypothesised that pre-puncture US scanning for estimating the depth of epidural space for thoracic epidurals is comparable between transverse median (TM) and paramedian sagittal oblique (PSO) planes. We performed pre-puncture US scanning in 32 patients, posted for open abdominal surgeries. The imaging was done to detect the depth of epidural space from skin (ultrasound depth [UD]) and needle insertion point, in parasagittal oblique plane in PSO group and transverse median plane in TM group. Subsequently, epidural space was localised through the predetermined insertion point by 'loss of resistance' technique and needle depth (ND) to the epidural space was marked. Correlation between the UD and actual ND was calculated and concordance correlation coefficient (CCC) was used to determine the degree of agreement between UD and ND in both the planes. The primary outcome, i.e., the comparison between UD and ND, done using Pearson correlation coefficient, was 0.99 in both PSO and TM groups, and the CCC was 0.93 (95% confidence interval [95% CI]: 0.81-0.97) and 0.90 (95% CI: 0.74-0.96) in PSO and TM groups respectively, which shows a strong positive association between UD and ND in both groups. The use of pre-puncture US scanning in both PSO and TM planes for estimating the depth of epidural space at the level of mid- and lower-thoracic spine is comparable.

Discovery and measurement of an isotopically distinct source of sulfate in Earth's atmosphere.

PubMed

Dominguez, Gerardo; Jackson, Terri; Brothers, Lauren; Barnett, Burton; Nguyen, Bryan; Thiemens, Mark H

2008-09-02

Sulfate (SO(4)) and its precursors are significant components of the atmosphere, with both natural and anthropogenic sources. Recently, our triple-isotope ((16)O, (17)O, (18)O) measurements of atmospheric sulfate have provided specific insights into the oxidation pathways leading to sulfate, with important implications for models of the sulfur cycle and global climate change. Using similar isotopic measurements of aerosol sulfate in a polluted marine boundary layer (MBL) and primary sulfate (p-SO(4)) sampled directly from a ship stack, we quantify the amount of p-SO(4) found in the atmosphere from ships. We find that ships contribute between 10% and 44% of the non-sea-salt sulfate found in fine [diameter (D) < 1.5 microm) particulate matter in coastal Southern California. These fractions are surprising, given that p-SO(4) constitutes approximately 2-7% of total sulfur emissions from combustion sources [Seinfed JH, Pandis SN (2006) Atmospheric Chemistry and Physics (Wiley-Interscience, New York)]. Our findings also suggest that the interaction of SO(2) from ship emissions with coarse hydrated sea salt particles may lead to the rapid removal of SO(2) in the MBL. When combined with the longer residence time of p-SO(4) emissions in the MBL, these findings suggest that the importance of p-SO(4) emissions in marine environments may be underappreciated in global chemical models. Given the expected increase of international shipping in the years to come, these findings have clear implications for public health, air quality, international maritime law, and atmospheric chemistry.

PSO algorithm enhanced with Lozi Chaotic Map - Tuning experiment

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

Pluhacek, Michal; Senkerik, Roman; Zelinka, Ivan

2015-03-10

In this paper it is investigated the effect of tuning of control parameters of the Lozi Chaotic Map employed as a chaotic pseudo-random number generator for the particle swarm optimization algorithm. Three different benchmark functions are selected from the IEEE CEC 2013 competition benchmark set. The Lozi map is extensively tuned and the performance of PSO is evaluated.

Comparison of the frying performance of olive oil and palm superolein.

PubMed

Romano, Raffaele; Giordano, Anella; Vitiello, Simona; Grottaglie, Laura Le; Musso, Salvatore Spagna

2012-05-01

Deep-fat frying is an important method of food preparation in which foods are immersed in hot oil. Repeated use of frying oils is a common practice, and in the presence of atmospheric oxygen it produces various undesirable reactions in used oils. Stable frying oils usually require low linolenic acid (LnA < 3%), increased oleic acid (OA > 40%), and decreased linoleic acid (LA < 50%). The aim of this study was to establish the behavior of palm superolein (PSO) (OA 45%; LA 12.5%; LnA 0.2%) and olive oil (OO) during repeated, discontinuous deep frying of French fries. The behavior of the oils under controlled heating conditions was also studied by maintaining all of the process variables the same as those in deep frying, except that there was no food in the oil. The PSO selected to be tested in this study may represent an alternative to OO as a frying medium. Although PSO presented a faster increase in some oxidation indices, such as free fatty acid and total polar compounds, for other indicators, PSO showed better behavior than OO (less formation of C8:0 and lower peroxide value). © 2012 Institute of Food Technologists®

Particle swarm optimization: an alternative in marine propeller optimization?

NASA Astrophysics Data System (ADS)

Vesting, F.; Bensow, R. E.

2018-01-01

This article deals with improving and evaluating the performance of two evolutionary algorithm approaches for automated engineering design optimization. Here a marine propeller design with constraints on cavitation nuisance is the intended application. For this purpose, the particle swarm optimization (PSO) algorithm is adapted for multi-objective optimization and constraint handling for use in propeller design. Three PSO algorithms are developed and tested for the optimization of four commercial propeller designs for different ship types. The results are evaluated by interrogating the generation medians and the Pareto front development. The same propellers are also optimized utilizing the well established NSGA-II genetic algorithm to provide benchmark results. The authors' PSO algorithms deliver comparable results to NSGA-II, but converge earlier and enhance the solution in terms of constraints violation.

Design of online monitoring and forecasting system for electrical equipment temperature of prefabricated substation based on WSN

NASA Astrophysics Data System (ADS)

Qi, Weiran; Miao, Hongxia; Miao, Xuejiao; Xiao, Xuanxuan; Yan, Kuo

2016-10-01

In order to ensure the safe and stable operation of the prefabricated substations, temperature sensing subsystem, temperature remote monitoring and management subsystem, forecast subsystem are designed in the paper. Wireless temperature sensing subsystem which consists of temperature sensor and MCU sends the electrical equipment temperature to the remote monitoring center by wireless sensor network. Remote monitoring center can realize the remote monitoring and prediction by monitoring and management subsystem and forecast subsystem. Real-time monitoring of power equipment temperature, history inquiry database, user management, password settings, etc., were achieved by monitoring and management subsystem. In temperature forecast subsystem, firstly, the chaos of the temperature data was verified and phase space is reconstructed. Then Support Vector Machine - Particle Swarm Optimization (SVM-PSO) was used to predict the temperature of the power equipment in prefabricated substations. The simulation results found that compared with the traditional methods SVM-PSO has higher prediction accuracy.

Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem

PubMed Central

Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

2013-01-01

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem. PMID:23935429

Intelligent control for PMSM based on online PSO considering parameters change

NASA Astrophysics Data System (ADS)

Song, Zhengqiang; Yang, Huiling

2018-03-01

A novel online particle swarm optimization method is proposed to design speed and current controllers of vector controlled interior permanent magnet synchronous motor drives considering stator resistance variation. In the proposed drive system, the space vector modulation technique is employed to generate the switching signals for a two-level voltage-source inverter. The nonlinearity of the inverter is also taken into account due to the dead-time, threshold and voltage drop of the switching devices in order to simulate the system in the practical condition. Speed and PI current controller gains are optimized with PSO online, and the fitness function is changed according to the system dynamic and steady states. The proposed optimization algorithm is compared with conventional PI control method in the condition of step speed change and stator resistance variation, showing that the proposed online optimization method has better robustness and dynamic characteristics compared with conventional PI controller design.

Modeling of urban growth using cellular automata (CA) optimized by Particle Swarm Optimization (PSO)

NASA Astrophysics Data System (ADS)

Khalilnia, M. H.; Ghaemirad, T.; Abbaspour, R. A.

2013-09-01

In this paper, two satellite images of Tehran, the capital city of Iran, which were taken by TM and ETM+ for years 1988 and 2010 are used as the base information layers to study the changes in urban patterns of this metropolis. The patterns of urban growth for the city of Tehran are extracted in a period of twelve years using cellular automata setting the logistic regression functions as transition functions. Furthermore, the weighting coefficients of parameters affecting the urban growth, i.e. distance from urban centers, distance from rural centers, distance from agricultural centers, and neighborhood effects were selected using PSO. In order to evaluate the results of the prediction, the percent correct match index is calculated. According to the results, by combining optimization techniques with cellular automata model, the urban growth patterns can be predicted with accuracy up to 75 %.

Toxin distribution and sphingoid base imbalances in Fusarium verticillioides-infected and fumonisin B1-watered maize seedlings.

PubMed

Arias, Silvina L; Mary, Verónica S; Otaiza, Santiago N; Wunderlin, Daniel A; Rubinstein, Héctor R; Theumer, Martín G

2016-05-01

Fusarium verticillioides is a major maize pathogen and there are susceptible and resistant cultivars to this fungal infection. Recent studies suggest that its main mycotoxin fumonisin B1 (FB1) may be involved in phytopathogenicity, but the underlying mechanisms are mostly still unknown. This work was aimed at assessing whether FB1 disseminates inside the plants, as well as identifying possible correlations between the maize resistant/susceptible phenotype and the unbalances of the FB1-structurally-related sphingoid base sphinganine (Sa) and phytosphingosine (Pso) due to toxin accumulation. Resistant (RH) and susceptible hybrid (SH) maize seedlings grown from seeds inoculated with a FB1-producer F. verticillioides and from uninoculated ones irrigated with FB1 (20 ppm), were harvested at 7, 14 and 21 days after planting (dap), and the FB1, Sa and Pso levels were quantified in roots and aerial parts. The toxin was detected in roots and aerial parts for inoculated and FB1-irrigated plants of both hybrids. However, FB1 levels were overall higher in SH seedlings regardless of the treatment (infection or watering). Sa levels increased substantially in RH lines, peaking at 54-fold in infected roots at 14 dap. In contrast, the main change observed in SH seedlings was an increase of Pso in infected roots at 7 dap. Here, it was found that FB1 disseminates inside seedlings in the absence of FB1-producer fungal infections, perhaps indicating this might condition the fungus-plant interaction before the first contact. Furthermore, the results strongly suggest the existence of at least two ceramide synthase isoforms in maize with different substrate specificities, whose differential expression after FB1 exposure could be closely related to the susceptibility/resistance to F. verticillioides. Copyright © 2016 Elsevier Ltd. All rights reserved.

Network Intrusion Detection Based on a General Regression Neural Network Optimized by an Improved Artificial Immune Algorithm

PubMed Central

Wu, Jianfa; Peng, Dahao; Li, Zhuping; Zhao, Li; Ling, Huanzhang

2015-01-01

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data. PMID:25807466

A Financial Approach as an Alternative Organizational Development Intervention.

DTIC Science & Technology

1979-12-01

Participants: (age group) (#Available) (# Program (#confirmed/ designed for) reserved) Program requested by: Board of Directors PSO Staff Community...Milestone chart approved by: on ’ name date 59 APPENDIX F PSO PROGRAM WORKSHEET INSTRUCTIONS Purpose: The Program Worksheet is designed to collect all inform...11. Grossman, Lee, The Change Agent, Amacom, New York, 1974. 12. Harvey, Donald F., and Brown, Donald R., An Eperimental Approach to Organization

Can acetabular orientation be restored by lumbar pedicle subtraction osteotomy in ankylosing spondylitis patients with thoracolumbar kyphosis?

PubMed

Hu, Jun; Qian, Bang-Ping; Qiu, Yong; Wang, Bin; Yu, Yang; Zhu, Ze-Zhang; Jiang, Jun; Mao, Sai-Hu; Qu, Zhe; Zhang, Yun-Peng

2017-07-01

To evaluate whether acetabular orientation (abduction and anteversion) can be restored by lumbar pedicle subtraction osteotomy (PSO) in ankylosing spondylitis (AS) patients with thoracolumbar kyphosis. A total of 33 consecutive AS patients with thoracolumbar kyphosis undergoing one-level lumbar PSO were retrospectively reviewed. Radiographical measurements included sagittal vertical axis, global kyphosis, thoracic kyphosis, local kyphosis, lumbar lordosis, pelvic incidence, sacral slope, and pelvic tilt. Acetabular abduction and anteversion were measured on CT scans of the pelvis before and after lumbar PSO. The preoperative and postoperative parameters were compared by the paired samples t test. Pearson's correlation analysis was conducted to determine the correlations between the changes in acetabular abduction and anteversion and the changes in sagittal spinopelvic parameters. After lumbar PSO, sagittal vertical axis, global kyphosis, and pelvic tilt were corrected from 15.7 ± 6.7 cm, 66.8° ± 17.5°, and 38.6° ± 9.0° to 2.9 ± 4.9 cm, 21.3° ± 8.2°, and 23.2° ± 8.2°, respectively (p < 0.001). Of note, acetabular abduction and anteversion decreased from 59.6° ± 4.6° to 31.4° ± 6.5° before surgery to 51.4° ± 6.5° and 20.2° ± 4.4° after surgery, respectively (p < 0.001). Moreover, the changes in acetabular abduction and anteversion were observed significantly correlated with the change in pelvic tilt (r = 0.527, p = 0.002; r = 0.586, p < 0.001). Abnormal acetabular abduction and anteversion could be corrected by lumbar PSO in AS patients with thoracolumbar kyphosis. Consequently, a relatively normal acetabular orientation could be achieved after lumbar PSO, which might decrease the potential risk of dislocation in AS patients with spine and hip deformities requiring subsequent THR surgery.

Automatic weight determination in nonlinear model predictive control of wind turbines using swarm optimization technique

NASA Astrophysics Data System (ADS)

Tofighi, Elham; Mahdizadeh, Amin

2016-09-01

This paper addresses the problem of automatic tuning of weighting coefficients for the nonlinear model predictive control (NMPC) of wind turbines. The choice of weighting coefficients in NMPC is critical due to their explicit impact on efficiency of the wind turbine control. Classically, these weights are selected based on intuitive understanding of the system dynamics and control objectives. The empirical methods, however, may not yield optimal solutions especially when the number of parameters to be tuned and the nonlinearity of the system increase. In this paper, the problem of determining weighting coefficients for the cost function of the NMPC controller is formulated as a two-level optimization process in which the upper- level PSO-based optimization computes the weighting coefficients for the lower-level NMPC controller which generates control signals for the wind turbine. The proposed method is implemented to tune the weighting coefficients of a NMPC controller which drives the NREL 5-MW wind turbine. The results are compared with similar simulations for a manually tuned NMPC controller. Comparison verify the improved performance of the controller for weights computed with the PSO-based technique.

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

PubMed Central

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

PubMed

Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

2015-01-01

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.

PubMed

Niu, Ben; Huang, Huali; Tan, Lijing; Duan, Qiqi

2017-01-01

Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.

Direct position determination for digital modulation signals based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yu, Wan-Ting; Yu, Hong-yi; Du, Jian-Ping; Wang, Ding

2018-04-01

The Direct Position Determination (DPD) algorithm has been demonstrated to achieve a better accuracy with known signal waveforms. However, the signal waveform is difficult to be completely known in the actual positioning process. To solve the problem, we proposed a DPD method for digital modulation signals based on improved particle swarm optimization algorithm. First, a DPD model is established for known modulation signals and a cost function is obtained on symbol estimation. Second, as the optimization of the cost function is a nonlinear integer optimization problem, an improved Particle Swarm Optimization (PSO) algorithm is considered for the optimal symbol search. Simulations are carried out to show the higher position accuracy of the proposed DPD method and the convergence of the fitness function under different inertia weight and population size. On the one hand, the proposed algorithm can take full advantage of the signal feature to improve the positioning accuracy. On the other hand, the improved PSO algorithm can improve the efficiency of symbol search by nearly one hundred times to achieve a global optimal solution.

Swarm intelligence application for optimization of CO2 diffusivity in polystyrene-b-polybutadiene-b-polystyrene (SEBS) foaming

NASA Astrophysics Data System (ADS)

Sharudin, Rahida Wati; Ajib, Norshawalina Muhamad; Yusoff, Marina; Ahmad, Mohd Aizad

2017-12-01

Thermoplastic elastomer SEBS foams were prepared by using carbon dioxide (CO2) as a blowing agent and the process is classified as physical foaming method. During the foaming process, the diffusivity of CO2 need to be controlled since it is one of the parameter that will affect the final cellular structure of the foam. Conventionally, the rate of CO2 diffusion was measured experimentally by using a highly sensitive device called magnetic suspension balance (MSB). Besides, this expensive MSB machine is not easily available and measurement of CO2 diffusivity is quite complicated as well as time consuming process. Thus, to overcome these limitations, a computational method was introduced. Particle Swarm Optimization (PSO) is a part of Swarm Intelligence system which acts as a beneficial optimization tool where it can solve most of nonlinear complications. PSO model was developed for predicting the optimum foaming temperature and CO2 diffusion rate in SEBS foam. Results obtained by PSO model are compared with experimental results for CO2 diffusivity at various foaming temperature. It is shown that predicted optimum foaming temperature at 154.6 °C was not represented the best temperature for foaming as the cellular structure of SEBS foamed at corresponding temperature consisted pores with unstable dimension and the structure was not visibly perceived due to foam shrinkage. The predictions were not agreed well with experimental result when single parameter of CO2 diffusivity is considered in PSO model because it is not the only factor that affected the controllability of foam shrinkage. The modification on the PSO model by considering CO2 solubility and rigidity of SEBS as additional parameters needs to be done for obtaining the optimum temperature for SEBS foaming. Hence stable SEBS foam could be prepared.

Will immediate postoperative imbalance improve in patients with thoracolumbar/lumbar degenerative kyphoscoliosis? A comparison between Smith-Petersen osteotomy and pedicle subtraction osteotomy with an average 4 years of follow-up.

PubMed

Bao, Hongda; He, Shouyu; Liu, Zhen; Zhu, Zezhang; Qiu, Yong; Zhu, Feng

2015-03-01

A retrospective radiographical study. To compare compensatory behavior of coronal and sagittal alignment after pedicle subtraction osteotomy (PSO) and Smith-Petersen osteotomy (SPO) for degenerative kyphoscoliosis. There was a paucity of literature paying attention to the postoperative imbalance after PSO or SPO and natural evolution of the imbalance. A retrospective study was performed on 68 consecutive patients with degenerative kyphoscoliosis treated by lumbar PSO (25 patients) or SPO (43 patients) procedures at a single institution. Long-cassette standing radiographs were taken preoperatively, postoperatively, and at the last follow-up and radiographical parameters were measured. The lower instrumented vertebral level and level of osteotomy were compared between the patients with and without improvement. Negative sagittal vertical axis (SVA) was observed in the PSO group postoperatively, implying an overcorrection of SVA. This negative SVA improved spontaneously during follow-up (P < 0.05). Coronal balance was found to worsen immediately postoperatively in the SPO group (P < 0.05). At the last follow-up, spontaneous improvement was observed in 15 patients and the average coronal balance decreased to 16.35 mm. For the 15 patients with improved coronal balance, fusion at L5 or above was more common compared with the 11 patients with persisted postoperative imbalance (P = 0.027), whereas no difference in term of levels of osteotomy was found (P > 0.05). The overcorrection of SVA is more often seen in the PSO group. The coronal imbalance is more likely to occur in the SPO group. The postoperative sagittal imbalance often spontaneously improves with time. Lower instrumented vertebra at S1 or with pelvic fixation should be regarded as potential risk factors for persistent coronal imbalance in patients with SPO. 3.

Effect of pomegranate extracts on brain antioxidant markers and cholinesterase activity in high fat-high fructose diet induced obesity in rat model.

PubMed

Amri, Zahra; Ghorbel, Asma; Turki, Mouna; Akrout, Férièle Messadi; Ayadi, Fatma; Elfeki, Abdelfateh; Hammami, Mohamed

2017-06-27

To investigate beneficial effects of Pomegranate seeds oil (PSO), leaves (PL), juice (PJ) and (PP) on brain cholinesterase activity, brain oxidative stress and lipid profile in high-fat-high fructose diet (HFD) induced-obese rat. In vitro and in vivo cholinesterase activity, brain oxidative status, body and brain weight and plasma lipid profile were measured in control rats, HFD-fed rats and HFD-fed rats treated by PSO, PL, PJ and PP. In vitro study showed that PSO, PL, PP, PJ inhibited cholinesterase activity in dose dependant manner. PL extract displayed the highest inhibitory activity by IC50 of 151.85 mg/ml. For in vivo study, HFD regime induced a significant increase of cholinesterase activity in brain by 17.4% as compared to normal rats. However, the administration of PSO, PL, PJ and PP to HDF-rats decreased cholinesterase activity in brain respectively by 15.48%, 6.4%, 20% and 18.7% as compared to untreated HFD-rats. Moreover, HFD regime caused significant increase in brain stress, brain and body weight, and lipid profile disorders in blood. Furthermore, PSO, PL, PJ and PP modulated lipid profile in blood and prevented accumulation of lipid in brain and body evidenced by the decrease of their weights as compared to untreated HFD-rats. In addition administration of these extract protected brain from stress oxidant, evidenced by the decrease of malondialdehyde (MDA) and Protein carbonylation (PC) levels and the increase in superoxide dismutase (SOD) and glutathione peroxidase (GPx) levels. These findings highlight the neuroprotective effects of pomegranate extracts and one of mechanisms is the inhibition of cholinesterase and the stimulation of antioxidant capacity.

Musculoskeletal ultrasonography for psoriatic arthritis and psoriasis patients: a systematic literature review.

PubMed

Zabotti, Alen; Bandinelli, Francesca; Batticciotto, Alberto; Scirè, Carlo Alberto; Iagnocco, Annamaria; Sakellariou, Garifallia

2017-09-01

To systematically review the role of musculoskeletal US in patients suffering from PsA or psoriasis (Pso) in terms of prevalence, diagnosis, prognosis, monitoring and treatment. A systematic literature review was conducted through medical databases (MEDLINE via PubMed, Embase) and the grey literature up to September 2015 to inform a new study of the Musculoskeletal Ultrasound Study Group of the Italian Society for Rheumatology. All articles reporting data on musculoskeletal US in PsA or Pso were included and extracted according to the underlying clinical question. A total of 86 publications were included. The prevalence of US abnormalities showed a wide range for each examined feature (e.g. 37-95% for entheses thickness of the lower limbs). The performance of US for diagnosis of disease or elementary lesions was variable across studies, but no study evaluated the overall performance of US in addition to clinical findings for diagnosing PsA. Considering US in defining PsA and Pso prognosis, several works focused on US of entheses of lower limbs in Pso, while for the monitoring of PsA activity five different scoring systems were identified. Last, the results of the role of US in guiding intra-articular interventions were controversial for the clinical outcomes, but in favour of US for accuracy. despite the recognized importance of US in the management of PsA and Pso, this review clearly demonstrated the need of pivotal research in order to optimize the use of US in the diagnosis and monitoring of psoriatic disease. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Particle swarm optimization with recombination and dynamic linkage discovery.

PubMed

Chen, Ying-Ping; Peng, Wen-Chih; Jian, Ming-Chung

2007-12-01

In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system.

Fuzzy controller training using particle swarm optimization for nonlinear system control.

PubMed

Karakuzu, Cihan

2008-04-01

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.

Detection of Coal Fires: A Case Study Conducted on Indian Coal Seams Using Neural Network and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Singh, B. B.

2016-12-01

India produces majority of its electricity from coal but a huge quantity of coal burns every day due to coal fires and also poses a threat to the environment as severe pollutants. In the present study we had demonstrated the usage of Neural Network based approach with an integrated Particle Swarm Optimization (PSO) inversion technique. The Self Potential (SP) data set is used for the early detection of coal fires. The study was conducted over the East Basuria colliery, Jharia Coal Field, Jharkhand, India. The causative source was modelled as an inclined sheet like anomaly and the synthetic data was generated. Neural Network scheme consists of an input layer, hidden layers and an output layer. The input layer corresponds to the SP data and the output layer is the estimated depth of the coal fire. A synthetic dataset was modelled with some of the known parameters such as depth, conductivity, inclination angle, half width etc. associated with causative body and gives a very low misfit error of 0.0032%. Therefore, the method was found accurate in predicting the depth of the source body. The technique was applied to the real data set and the model was trained until a very good correlation of determination `R2' value of 0.98 is obtained. The depth of the source body was found to be 12.34m with a misfit error percentage of 0.242%. The inversion results were compared with the lithologs obtained from a nearby well which corresponds to the L3 coal seam. The depth of the coal fire had exactly matched with the half width of the anomaly which suggests that the fire is widely spread. The inclination angle of the anomaly was 135.510 which resembles the development of the geometrically complex fracture planes. These fractures may be developed due to anisotropic weakness of the ground which acts as passage for the air. As a result coal fires spreads along these fracture planes. The results obtained from the Neural Network was compared with PSO inversion results and were found in complete agreement. PSO technique had already been found a well-established technique to model SP anomalies. Therefore for successful control and mitigation, SP surveys coupled with Neural Network and PSO technique proves to be novel and economical approach along with other existing geophysical techniques. Keywords: PSO, Coal fire, Self-Potential, Inversion, Neural Network

The dose-response relationship for hypoxic pulmonary vasoconstriction.

PubMed

Marshall, B E; Clarke, W R; Costarino, A T; Chen, L; Miller, F; Marshall, C

1994-05-01

In 12 pentobarbital anesthetized dogs the lungs were independently ventilated with a double piston ventilator. The right lung was ventilated throughout with 100% oxygen. Blood was drawn from the right atrium and pumped through a bubble oxygenator to a cannula in the ligated left main pulmonary artery. The pressures in the left main pulmonary artery and the left atrium were recorded during constant flow while the oxygen tension in the left lung alveolar gas and the perfusate were varied either to match each other (Protocol 1) or differ (Protocol 2) over the range from "zero" to "100%" oxygen. From the combined data a three dimensional response surface for hypoxic pulmonary vasoconstriction was derived. The maximum increase of pulmonary vascular resistance (r%PVRmax) was defined at a stimulus oxygen tension (PSO2) of 10 mmHg amounting to a 3.15 +/- (0.18)-fold increase of the vascular resistance on "100%" oxygen. The stimulus oxygen tension was shown to be PSO2 = PVO2(0.41) x PAO2(0.59) and the dose-response sigmoid for hypoxic pulmonary vasoconstriction in canine lungs was derived as r%PVRmax = 100 (PSO2(-2.616))/(6.683 x 10(-5) + PSO2(-2.616)) These results appear to reconcile observations from a number of laboratories and to be of quite general application.

Automated Spectroscopic Analysis Using the Particle Swarm Optimization Algorithm: Implementing a Guided Search Algorithm to Autofit

NASA Astrophysics Data System (ADS)

Ervin, Katherine; Shipman, Steven

2017-06-01

While rotational spectra can be rapidly collected, their analysis (especially for complex systems) is seldom straightforward, leading to a bottleneck. The AUTOFIT program was designed to serve that need by quickly matching rotational constants to spectra with little user input and supervision. This program can potentially be improved by incorporating an optimization algorithm in the search for a solution. The Particle Swarm Optimization Algorithm (PSO) was chosen for implementation. PSO is part of a family of optimization algorithms called heuristic algorithms, which seek approximate best answers. This is ideal for rotational spectra, where an exact match will not be found without incorporating distortion constants, etc., which would otherwise greatly increase the size of the search space. PSO was tested for robustness against five standard fitness functions and then applied to a custom fitness function created for rotational spectra. This talk will explain the Particle Swarm Optimization algorithm and how it works, describe how Autofit was modified to use PSO, discuss the fitness function developed to work with spectroscopic data, and show our current results. Seifert, N.A., Finneran, I.A., Perez, C., Zaleski, D.P., Neill, J.L., Steber, A.L., Suenram, R.D., Lesarri, A., Shipman, S.T., Pate, B.H., J. Mol. Spec. 312, 13-21 (2015)

CALIBRATION OF SEMI-ANALYTIC MODELS OF GALAXY FORMATION USING PARTICLE SWARM OPTIMIZATION

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

Ruiz, Andrés N.; Domínguez, Mariano J.; Yaryura, Yamila

2015-03-10

We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observedmore » galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.« less

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

Best, William M. J.; Liu, Michael C.; Magnier, Eugene A.

We present initial results from a wide-field (30,000 deg{sup 2}) search for L/T transition brown dwarfs within 25 pc using the Pan-STARRS1 and Wide-field Infrared Survey Explorer (WISE) surveys. Previous large-area searches have been incomplete for L/T transition dwarfs, because these objects are faint in optical bands and have near-infrared (near-IR) colors that are difficult to distinguish from background stars. To overcome these obstacles, we have cross-matched the Pan-STARRS1 (optical) and WISE (mid-IR) catalogs to produce a unique multi-wavelength database for finding ultracool dwarfs. As part of our initial discoveries, we have identified seven brown dwarfs in the L/T transitionmore » within 9-15 pc of the Sun. The L9.5 dwarf PSO J140.2308+45.6487 and the T1.5 dwarf PSO J307.6784+07.8263 (both independently discovered by Mace et al.) show possible spectroscopic variability at the Y and J bands. Two more objects in our sample show evidence of photometric J-band variability, and two others are candidate unresolved binaries based on their spectra. We expect our full search to yield a well-defined, volume-limited sample of L/T transition dwarfs that will include many new targets for study of this complex regime. PSO J307.6784+07.8263 in particular may be an excellent candidate for in-depth study of variability, given its brightness (J = 14.2 mag) and proximity (11 pc)« less

The Effects of Doctrine on International Security Assistance Force Operations

DTIC Science & Technology

2008-04-04

Article 5 collective defense.34 These operations can be described as: Such operations are normally known as Peace Support Operations ( PSO ). They are...diplomatic and humanitarian agencies. PSO are designed to achieve a long-term political settlement or other specified conditions. They include...December 2006), ix. 42 Gallis. 43 Ann Scott Tyson and Josh White, “Gates Hits NATO Allies’ Role in Afghanistan,” Washington Post, 7 February 2008, sec

FPGA implementation of neuro-fuzzy system with improved PSO learning.

PubMed

Karakuzu, Cihan; Karakaya, Fuat; Çavuşlu, Mehmet Ali

2016-07-01

This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles

NASA Astrophysics Data System (ADS)

Aghababa, Mohammad Pourmahmood; Amrollahi, Mohammad Hossein; Borjkhani, Mehdi

2012-09-01

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a numerical solution of a nonlinear optimal control problem (NOCP). An energy performance index as a cost function, which should be minimized, was defined. The resulting problem was a two-point boundary value problem (TPBVP). A genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Evolutionary Algorithms Approach to the Solution of Damage Detection Problems

NASA Astrophysics Data System (ADS)

Salazar Pinto, Pedro Yoajim; Begambre, Oscar

2010-09-01

In this work is proposed a new Self-Configured Hybrid Algorithm by combining the Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA). The aim of the proposed strategy is to increase the stability and accuracy of the search. The central idea is the concept of Guide Particle, this particle (the best PSO global in each generation) transmits its information to a particle of the following PSO generation, which is controlled by the GA. Thus, the proposed hybrid has an elitism feature that improves its performance and guarantees the convergence of the procedure. In different test carried out in benchmark functions, reported in the international literature, a better performance in stability and accuracy was observed; therefore the new algorithm was used to identify damage in a simple supported beam using modal data. Finally, it is worth noting that the algorithm is independent of the initial definition of heuristic parameters.

Soy-based polymeric surfactants prepared in carbon dioxide media and influence of structure on their surface properties

USDA-ARS?s Scientific Manuscript database

Soybean oil (SO) and epoxidized soybean oil (ESO) were polymerized in the CO2 media (supercritical and sub-supercritical) by BF3•OEt2 catalyst. The resulting polymers (PSO and PESO) were hydrolyzed into polysoaps (HPSO) and (HPESO) with Na+, K+, or TEA+ (triethanolamine, ammonium salt) counter ions....

Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm

NASA Astrophysics Data System (ADS)

Wang, Ershen; Jia, Chaoying; Tong, Gang; Qu, Pingping; Lan, Xiaoyu; Pang, Tao

2018-03-01

The receiver autonomous integrity monitoring (RAIM) is one of the most important parts in an avionic navigation system. Two problems need to be addressed to improve this system, namely, the degeneracy phenomenon and lack of samples for the standard particle filter (PF). However, the number of samples cannot adequately express the real distribution of the probability density function (i.e., sample impoverishment). This study presents a GPS receiver autonomous integrity monitoring (RAIM) method based on a chaos particle swarm optimization particle filter (CPSO-PF) algorithm with a log likelihood ratio. The chaos sequence generates a set of chaotic variables, which are mapped to the interval of optimization variables to improve particle quality. This chaos perturbation overcomes the potential for the search to become trapped in a local optimum in the particle swarm optimization (PSO) algorithm. Test statistics are configured based on a likelihood ratio, and satellite fault detection is then conducted by checking the consistency between the state estimate of the main PF and those of the auxiliary PFs. Based on GPS data, the experimental results demonstrate that the proposed algorithm can effectively detect and isolate satellite faults under conditions of non-Gaussian measurement noise. Moreover, the performance of the proposed novel method is better than that of RAIM based on the PF or PSO-PF algorithm.

Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization

NASA Astrophysics Data System (ADS)

Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Zhu, Zhengqiu; Wang, Yuan; Qiu, Xiaogang

2018-04-01

Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.

A new maximum power tracking in PV system during partially shaded conditions based on shuffled frog leap algorithm

NASA Astrophysics Data System (ADS)

Sridhar, R.; Jeevananthan, S.; Dash, S. S.; Vishnuram, Pradeep

2017-05-01

Maximum Power Point Trackers (MPPTs) are power electronic conditioners used in photovoltaic (PV) system to ensure that PV structures feed maximum power for the given ambient temperature and sun's irradiation. When the PV panels are shaded by a fraction due to any environment hindrances then, conventional MPPT trackers may fail in tracking the appropriate peak power as there will be multi power peaks. In this work, a shuffled frog leap algorithm (SFLA) is proposed and it successfully identifies the global maximum power point among other local maxima. The SFLA MPPT is compared with a well-entrenched conventional perturb and observe (P&O) MPPT algorithm and a global search particle swarm optimisation (PSO) MPPT. The simulation results reveal that the proposed algorithm is highly advantageous than P&O, as it tracks nearly 30% more power for a given shading pattern. The credible nature of the proposed SFLA is ensured when it outplays PSO MPPT in convergence. The whole system is realised in MATLAB/Simulink environment.

Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Wang, Geng; Zhou, Kexin; Zhang, Yeming

2018-04-01

The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

Stable and accurate methods for identification of water bodies from Landsat series imagery using meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Gamshadzaei, Mohammad Hossein; Rahimzadegan, Majid

2017-10-01

Identification of water extents in Landsat images is challenging due to surfaces with similar reflectance to water extents. The objective of this study is to provide stable and accurate methods for identifying water extents in Landsat images based on meta-heuristic algorithms. Then, seven Landsat images were selected from various environmental regions in Iran. Training of the algorithms was performed using 40 water pixels and 40 nonwater pixels in operational land imager images of Chitgar Lake (one of the study regions). Moreover, high-resolution images from Google Earth were digitized to evaluate the results. Two approaches were considered: index-based and artificial intelligence (AI) algorithms. In the first approach, nine common water spectral indices were investigated. AI algorithms were utilized to acquire coefficients of optimal band combinations to extract water extents. Among the AI algorithms, the artificial neural network algorithm and also the ant colony optimization, genetic algorithm, and particle swarm optimization (PSO) meta-heuristic algorithms were implemented. Index-based methods represented different performances in various regions. Among AI methods, PSO had the best performance with average overall accuracy and kappa coefficient of 93% and 98%, respectively. The results indicated the applicability of acquired band combinations to extract accurately and stably water extents in Landsat imagery.

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Particle swarm optimization based artificial neural network model for forecasting groundwater level in Udupi district

NASA Astrophysics Data System (ADS)

Balavalikar, Supreetha; Nayak, Prabhakar; Shenoy, Narayan; Nayak, Krishnamurthy

2018-04-01

The decline in groundwater is a global problem due to increase in population, industries, and environmental aspects such as increase in temperature, decrease in overall rainfall, loss of forests etc. In Udupi district, India, the water source fully depends on the River Swarna for drinking and agriculture purposes. Since the water storage in Bajae dam is declining day-by-day and the people of Udupi district are under immense pressure due to scarcity of drinking water, alternatively depend on ground water. As the groundwater is being heavily used for drinking and agricultural purposes, there is a decline in its water table. Therefore, the groundwater resources must be identified and preserved for human survival. This research proposes a data driven approach for forecasting the groundwater level. The monthly variations in groundwater level and rainfall data in three observation wells located in Brahmavar, Kundapur and Hebri were investigated and the scenarios were examined for 2000-2013. The focus of this research work is to develop an ANN based groundwater level forecasting model and compare with hybrid ANN-PSO forecasting model. The model parameters are tested using different combinations of the data. The results reveal that PSO-ANN based hybrid model gives a better prediction accuracy, than ANN alone.

Permethrin spot-on intoxication of cats Literature review and survey of veterinary practitioners in Australia.

PubMed

Malik, Richard; Ward, Michael P; Seavers, Aine; Fawcett, Anne; Bell, Erin; Govendir, Merran; Page, Stephen

2010-01-01

SURVEY AIMS: A questionnaire was sent to veterinarians in Australia to determine the approximate number of cats presenting for permethrin spot-on (PSO) intoxication over a 2-year period. Of the 269 questionnaires returned, 255 were eligible for analysis. A total of 207 respondents (81%) reported cases of PSO intoxication in cats over the previous 2 years. In total, 750 individual cases were reported, with 166 deaths. While all deaths were generally attributable to intoxication, 39 cats were euthanased because owners were unable to pay the anticipated treatment costs. Brands of PSO implicated included Exelpet Flea (and Tick) Liquidator (Mars Australia) (146 respondents), Bayer Advantix (48), Purina Totalcare Flea Eliminator Line-On (19), Troy Ease-On (six) and Duogard Line-On (Virbac) (four); 67 respondents were not able to identify a specific product. Permethrin spot-on formulations were most commonly obtained from supermarkets (146 respondents), followed by pet stores (43), veterinary practices (16), and a range of other sources including produce stores and friends. The majority of intoxication cases reported involved PSOs labelled for use in dogs with specific label instructions such as 'toxic to cats'. Owners applied these PSO products to their cats accidentally or intentionally. In some cases, exposure was through secondary contact, such as when a PSO product was applied to a dog with which a cat had direct or indirect contact. In the authors' view, because of the likelihood of inappropriate use and toxicity in the non-labelled species, over-the-counter products intended for use in either dogs or cats must have a high margin of safety in all species. Furthermore, PSOs should only be available at points of sale where veterinary advice can be provided and appropriate warnings given. As an interim measure, modified labelling with more explicit warnings may reduce morbidity and mortality. Copyright 2009 ESFM and AAFP. Published by Elsevier Ltd. All rights reserved.

Stimulation of liver IGF-1 expression promotes peak bone mass achievement in growing rats: a study with pomegranate seed oil.

PubMed

Bachagol, Deepa; Joseph, Gilbert Stanley; Ellur, Govindraj; Patel, Kalpana; Aruna, Pamisetty; Mittal, Monika; China, Shyamsundar Pal; Singh, Ravendra Pratap; Sharan, Kunal

2018-02-01

Peak bone mass (PBM) achieved at adulthood is a strong determinant of future onset of osteoporosis, and maximizing it is one of the strategies to combat the disease. Recently, pomegranate seed oil (PSO) has been shown to have bone-sparing effect in ovariectomized mice. However, its effect on growing skeleton and its molecular mechanism remain unclear. In the present study, we evaluated the effect of PSO on PBM in growing rats and associated mechanism of action. PSO was given at various doses to 21-day-old growing rats for 90 days by oral gavage. The changes in bone parameters were assessed by micro-computed tomography and histology. Enzyme-linked immunosorbent assay was performed to analyze the levels of serum insulin-like growth factor type 1 (IGF-1). Western blotting from bone and liver tissues was done. Chromatin immunoprecipitation assay was performed to study the histone acetylation levels at IGF-1 gene. The results of the study show that PSO treatment significantly increases bone length, bone formation rate, biomechanical parameters, bone mineral density and bone microarchitecture along with enhancing muscle and brown fat mass. This effect was due to the increased serum levels of IGF-1 and stimulation of its signaling in the bones. Studies focusing on acetylation of histones in the liver, the major site of IGF-1 synthesis, showed enrichment of acetylated H3K9 and H3K14 at IGF-1 gene promoter and body. Further, the increased acetylation at H3K9 and H3K14 was associated with a reduced HDAC1 protein level. Together, our data suggest that PSO promotes the PBM achievement via increased IGF-1 expression in liver and IGF-1 signaling in bone. Copyright © 2017 Elsevier Inc. All rights reserved.

The effects of tumour necrosis factor inhibitors, methotrexate, non-steroidal anti-inflammatory drugs and corticosteroids on cardiovascular events in rheumatoid arthritis, psoriasis and psoriatic arthritis: a systematic review and meta-analysis

PubMed Central

Roubille, Camille; Richer, Vincent; Starnino, Tara; McCourt, Collette; McFarlane, Alexandra; Fleming, Patrick; Siu, Stephanie; Kraft, John; Lynde, Charles; Pope, Janet; Gulliver, Wayne; Keeling, Stephanie; Dutz, Jan; Bessette, Louis; Bissonnette, Robert; Haraoui, Boulos

2015-01-01

The objective of this systematic literature review was to determine the association between cardiovascular events (CVEs) and antirheumatic drugs in rheumatoid arthritis (RA) and psoriatic arthritis (PsA)/psoriasis (Pso). Systematic searches were performed of MEDLINE, EMBASE and Cochrane databases (1960 to December 2012) and proceedings from major relevant congresses (2010–2012) for controlled studies and randomised trials reporting confirmed CVEs in patients with RA or PsA/Pso treated with antirheumatic drugs. Random-effects meta-analyses were performed on extracted data. Out of 2630 references screened, 34 studies were included: 28 in RA and 6 in PsA/Pso. In RA, a reduced risk of all CVEs was reported with tumour necrosis factor inhibitors (relative risk (RR), 0.70; 95% CI 0.54 to 0.90; p=0.005) and methotrexate (RR, 0.72; 95% CI 0.57 to 0.91; p=0.007). Non-steroidal anti-inflammatory drugs (NSAIDs) increased the risk of all CVEs (RR, 1.18; 95% CI 1.01 to 1.38; p=0.04), which may have been specifically related to the effects of rofecoxib. Corticosteroids increased the risk of all CVEs (RR, 1.47; 95% CI 1.34 to 1.60; p<0.001). In PsA/Pso, systemic therapy decreased the risk of all CVEs (RR, 0.75; 95% CI 0.63 to 0.91; p=0.003). In RA, tumour necrosis factor inhibitors and methotrexate are associated with a decreased risk of all CVEs while corticosteroids and NSAIDs are associated with an increased risk. Targeting inflammation with tumour necrosis factor inhibitors or methotrexate may have positive cardiovascular effects in RA. In PsA/Pso, limited evidence suggests that systemic therapies are associated with a decrease in all CVE risk. PMID:25561362

Particle Swarm Optimization approach to defect detection in armour ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

Particle swarm optimization algorithm for optimizing assignment of blood in blood banking system.

PubMed

Olusanya, Micheal O; Arasomwan, Martins A; Adewumi, Aderemi O

2015-01-01

This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients' blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment.

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

PubMed Central

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site. PMID:29370230

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

PubMed

Illias, Hazlee Azil; Zhao Liang, Wee

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

Manifold absolute pressure estimation using neural network with hybrid training algorithm

PubMed Central

Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

2017-01-01

In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value. PMID:29190779

Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites

PubMed Central

Cao, Rensheng; Ruan, Wenqian; Wu, Xianliang; Wei, Xionghui

2018-01-01

Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training. The ANN-GA model results (with a prediction error of 2.88%) showed a better agreement with the experimental data than the ANN-PSO model results (with a prediction error of 4.63%) and the RSM model results (with a prediction error of 5.56%), thus the ANN-GA model was an ideal choice for modeling and optimizing the Se(IV) removal by the nZVI/rGO composites due to its low prediction error. The analysis of the experimental data illustrates that the removal process of Se(IV) obeyed the Langmuir isotherm and the pseudo-second-order kinetic model. Furthermore, the Se 3d and 3p peaks found in XPS spectra for the nZVI/rGO composites after removing treatment illustrates that the removal of Se(IV) was mainly through the adsorption and reduction mechanisms. PMID:29543753

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

PubMed Central

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms. PMID:28979308

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

PubMed

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.

An application of PSO algorithm for multi-criteria geometry optimization of printed low-pass filters based on conductive periodic structures

NASA Astrophysics Data System (ADS)

Steckiewicz, Adam; Butrylo, Boguslaw

2017-08-01

In this paper we discussed the results of a multi-criteria optimization scheme as well as numerical calculations of periodic conductive structures with selected geometry. Thin printed structures embedded on a flexible dielectric substrate may be applied as simple, cheap, passive low-pass filters with an adjustable cutoff frequency in low (up to 1 MHz) radio frequency range. The analysis of an electromagnetic phenomena in presented structures was realized on the basis of a three-dimensional numerical model of three proposed geometries of periodic elements. The finite element method (FEM) was used to obtain a solution of an electromagnetic harmonic field. Equivalent lumped electrical parameters of printed cells obtained in such manner determine the shape of an amplitude transmission characteristic of a low-pass filter. A nonlinear influence of a printed cell geometry on equivalent parameters of cells electric model, makes it difficult to find the desired optimal solution. Therefore an optimization problem of optimal cell geometry estimation with regard to an approximation of the determined amplitude transmission characteristic with an adjusted cutoff frequency, was obtained by the particle swarm optimization (PSO) algorithm. A dynamically suitable inertia factor was also introduced into the algorithm to improve a convergence to a global extremity of a multimodal objective function. Numerical results as well as PSO simulation results were characterized in terms of approximation accuracy of predefined amplitude characteristics in a pass-band, stop-band and cutoff frequency. Three geometries of varying degrees of complexity were considered and their use in signal processing systems was evaluated.

Uncertainty analysis of hydrological modeling in a tropical area using different algorithms

NASA Astrophysics Data System (ADS)

Rafiei Emam, Ammar; Kappas, Martin; Fassnacht, Steven; Linh, Nguyen Hoang Khanh

2018-01-01

Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and Rfactor, coefficient of determination (R 2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor <0.56 and R 2>0.91, NSE>0.89, and 0.18

Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites.

PubMed

Cao, Rensheng; Fan, Mingyi; Hu, Jiwei; Ruan, Wenqian; Wu, Xianliang; Wei, Xionghui

2018-03-15

Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training. The ANN-GA model results (with a prediction error of 2.88%) showed a better agreement with the experimental data than the ANN-PSO model results (with a prediction error of 4.63%) and the RSM model results (with a prediction error of 5.56%), thus the ANN-GA model was an ideal choice for modeling and optimizing the Se(IV) removal by the nZVI/rGO composites due to its low prediction error. The analysis of the experimental data illustrates that the removal process of Se(IV) obeyed the Langmuir isotherm and the pseudo-second-order kinetic model. Furthermore, the Se 3d and 3p peaks found in XPS spectra for the nZVI/rGO composites after removing treatment illustrates that the removal of Se(IV) was mainly through the adsorption and reduction mechanisms.

Cardiac arrhythmia beat classification using DOST and PSO tuned SVM.

PubMed

Raj, Sandeep; Ray, Kailash Chandra; Shankar, Om

2016-11-01

The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias. The proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan-Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM). The proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based assessment scheme respectively to the state-of-art diagnosis. The results reported are further compared to the existing methodologies in literature. The proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmia beats. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.

PubMed

Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

2015-07-02

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a "data modeler" tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare

PubMed Central

Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

2015-01-01

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a “data modeler” tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets. PMID:26147731

The admissible portfolio selection problem with transaction costs and an improved PSO algorithm

NASA Astrophysics Data System (ADS)

Chen, Wei; Zhang, Wei-Guo

2010-05-01

In this paper, we discuss the portfolio selection problem with transaction costs under the assumption that there exist admissible errors on expected returns and risks of assets. We propose a new admissible efficient portfolio selection model and design an improved particle swarm optimization (PSO) algorithm because traditional optimization algorithms fail to work efficiently for our proposed problem. Finally, we offer a numerical example to illustrate the proposed effective approaches and compare the admissible portfolio efficient frontiers under different constraints.

Non linear predictive control of a LEGO mobile robot

NASA Astrophysics Data System (ADS)

Merabti, H.; Bouchemal, B.; Belarbi, K.; Boucherma, D.; Amouri, A.

2014-10-01

Metaheuristics are general purpose heuristics which have shown a great potential for the solution of difficult optimization problems. In this work, we apply the meta heuristic, namely particle swarm optimization, PSO, for the solution of the optimization problem arising in NLMPC. This algorithm is easy to code and may be considered as alternatives for the more classical solution procedures. The PSO- NLMPC is applied to control a mobile robot for the tracking trajectory and obstacles avoidance. Experimental results show the strength of this approach.

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.

Design of Linear Accelerator (LINAC) tanks for proton therapy via Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) approaches

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

Castellano, T.; De Palma, L.; Laneve, D.

2015-07-01

A homemade computer code for designing a Side- Coupled Linear Accelerator (SCL) is written. It integrates a simplified model of SCL tanks with the Particle Swarm Optimization (PSO) algorithm. The computer code main aim is to obtain useful guidelines for the design of Linear Accelerator (LINAC) resonant cavities. The design procedure, assisted via the aforesaid approach seems very promising, allowing future improvements towards the optimization of actual accelerating geometries. (authors)

Design and implementation of a unified certification management system based on seismic business

NASA Astrophysics Data System (ADS)

Tang, Hongliang

2018-04-01

Many business software for seismic systems are based on web pages, users can simply open a browser and enter their IP address. However, how to achieve unified management and security management of many IP addresses, this paper introduces the design concept based on seismic business and builds a unified authentication management system using ASP technology.

Joint global optimization of tomographic data based on particle swarm optimization and decision theory

NASA Astrophysics Data System (ADS)

Paasche, H.; Tronicke, J.

2012-04-01

In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto optimality of the found solutions can be made. Identification of the leading particle traditionally requires a costly combination of ranking and niching techniques. In our approach, we use a decision rule under uncertainty to identify the currently leading particle of the swarm. In doing so, we consider the different objectives of our optimization problem as competing agents with partially conflicting interests. Analysis of the maximin fitness function allows for robust and cheap identification of the currently leading particle. The final optimization result comprises a set of possible models spread along the Pareto front. For convex Pareto fronts, solution density is expected to be maximal in the region ideally compromising all objectives, i.e. the region of highest curvature.

Application of Modified Particle Swarm Optimization Method for Parameter Extraction of 2-D TEC Mapping

NASA Astrophysics Data System (ADS)

Toker, C.; Gokdag, Y. E.; Arikan, F.; Arikan, O.

2012-04-01

Ionosphere is a very important part of Space Weather. Modeling and monitoring of ionospheric variability is a major part of satellite communication, navigation and positioning systems. Total Electron Content (TEC), which is defined as the line integral of the electron density along a ray path, is one of the parameters to investigate the ionospheric variability. Dual-frequency GPS receivers, with their world wide availability and efficiency in TEC estimation, have become a major source of global and regional TEC modeling. When Global Ionospheric Maps (GIM) of International GPS Service (IGS) centers (http://iono.jpl.nasa.gov/gim.html) are investigated, it can be observed that regional ionosphere along the midlatitude regions can be modeled as a constant, linear or a quadratic surface. Globally, especially around the magnetic equator, the TEC surfaces resemble twisted and dispersed single centered or double centered Gaussian functions. Particle Swarm Optimization (PSO) proved itself as a fast converging and an effective optimization tool in various diverse fields. Yet, in order to apply this optimization technique into TEC modeling, the method has to be modified for higher efficiency and accuracy in extraction of geophysical parameters such as model parameters of TEC surfaces. In this study, a modified PSO (mPSO) method is applied to regional and global synthetic TEC surfaces. The synthetic surfaces that represent the trend and small scale variability of various ionospheric states are necessary to compare the performance of mPSO over number of iterations, accuracy in parameter estimation and overall surface reconstruction. The Cramer-Rao bounds for each surface type and model are also investigated and performance of mPSO are tested with respect to these bounds. For global models, the sample points that are used in optimization are obtained using IGS receiver network. For regional TEC models, regional networks such as Turkish National Permanent GPS Network (TNPGN-Active) receiver sites are used. The regional TEC models are grouped into constant (one parameter), linear (two parameters), and quadratic (six parameters) surfaces which are functions of latitude and longitude. Global models require seven parameters for single centered Gaussian and 13 parameters for double centered Gaussian function. The error criterion is the normalized percentage error for both the surface and the parameters. It is observed that mPSO is very successful in parameter extraction of various regional and global models. The normalized reconstruction error varies from 10-4 for constant surfaces to 10-3 for quadratic surfaces in regional models, sampled with regional networks. Even for the cases of a severe geomagnetic storm that affects measurements globally, with IGS network, the reconstruction error is on the order of 10-1 even though individual parameters have higher normalized errors. The modified PSO technique proved itself to be a useful tool for parameter extraction of more complicated TEC models. This study is supported by TUBITAK EEEAG under Grant No: 109E055.

Traveling Salesman Problem for Surveillance Mission Using Particle Swarm Optimization

DTIC Science & Technology

2001-03-20

design of experiments, results of the experiments, and qualitative and quantitative analysis . Conclusions and recommendations based on the qualitative and...characterize the algorithm. Such analysis and comparison between LK and a non-deterministic algorithm produces claims such as "Lin-Kernighan algorithm takes... based on experiments 5 and 6. All other parameters are the same as the baseline (see 4.2.1.2). 4.2.2.6 Experiment 10 - Fine Tuning PSO AS: 85,95% Global

Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

NASA Astrophysics Data System (ADS)

Sun, Xinyao; Wang, Xue; Wu, Jiangwei; Liu, Youda

2014-05-01

Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufacturing center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Coordinated trajectory planning of dual-arm space robot using constrained particle swarm optimization

NASA Astrophysics Data System (ADS)

Wang, Mingming; Luo, Jianjun; Yuan, Jianping; Walter, Ulrich

2018-05-01

Application of the multi-arm space robot will be more effective than single arm especially when the target is tumbling. This paper investigates the application of particle swarm optimization (PSO) strategy to coordinated trajectory planning of the dual-arm space robot in free-floating mode. In order to overcome the dynamics singularities issue, the direct kinematics equations in conjunction with constrained PSO are employed for coordinated trajectory planning of dual-arm space robot. The joint trajectories are parametrized with Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for coordinated trajectory planning of two kinematically redundant manipulators mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

Development and applications of various optimization algorithms for diesel engine combustion and emissions optimization

NASA Astrophysics Data System (ADS)

Ogren, Ryan M.

For this work, Hybrid PSO-GA and Artificial Bee Colony Optimization (ABC) algorithms are applied to the optimization of experimental diesel engine performance, to meet Environmental Protection Agency, off-road, diesel engine standards. This work is the first to apply ABC optimization to experimental engine testing. All trials were conducted at partial load on a four-cylinder, turbocharged, John Deere engine using neat-Biodiesel for PSO-GA and regular pump diesel for ABC. Key variables were altered throughout the experiments, including, fuel pressure, intake gas temperature, exhaust gas recirculation flow, fuel injection quantity for two injections, pilot injection timing and main injection timing. Both forms of optimization proved effective for optimizing engine operation. The PSO-GA hybrid was able to find a superior solution to that of ABC within fewer engine runs. Both solutions call for high exhaust gas recirculation to reduce oxide of nitrogen (NOx) emissions while also moving pilot and main fuel injections to near top dead center for improved tradeoffs between NOx and particulate matter.

Optimization of ultrasonic-assisted extraction of pomegranate (Punica granatum L.) seed oil.

PubMed

Tian, Yuting; Xu, Zhenbo; Zheng, Baodong; Martin Lo, Y

2013-01-01

The effectiveness of ultrasonic-assisted extraction (UAE) of pomegranate seed oil (PSO) was evaluated using a variety of solvents. Petroleum ether was the most effective for oil extraction, followed by n-hexane, ethyl acetate, diethyl ether, acetone, and isopropanol. Several variables, such as ultrasonic power, extraction temperature, extraction time, and the ratio of solvent volume and seed weight (S/S ratio) were studied for optimization using response surface methodology (RSM). The highest oil yield, 25.11% (w/w), was obtained using petroleum ether under optimal conditions for ultrasonic power, extraction temperature, extraction time, and S/S ratio at 140 W, 40 °C, 36 min, and 10 ml/g, respectively. The PSO yield extracted by UAE was significantly higher than by using Soxhlet extraction (SE; 20.50%) and supercriti cal fluid extraction (SFE; 15.72%). The fatty acid compositions were significantly different among the PSO extracted by Soxhlet extraction, SFE, and UAE, with punicic acid (>65%) being the most dominant using UAE. Copyright © 2012 Elsevier B.V. All rights reserved.

Transparent luminescent bulk nanocomposites of polysiloxane embedded with CdS nanocrystallines by a direct dispersion process.

PubMed

Shen, Zhu-Rui; Li, Ya-Li; Liu, Jian-Bin; Chen, Ming-Xia; Hou, Feng; Wang, Li-Qun

2012-03-07

Transparent luminescent bulk nanocomposites of polysiloxane (PSO) embedded with semiconductor nanocrystals (NCs) have been fabricated by the direct dispersion of CdS NCs in alkyl-(poly)siloxane (APS) followed by co-polymerization. The non-polar characteristics of the APS precursor are compatible with the CdS NC surface (oleylamine), which allows the direct dispersion of the CdS NCs without the need of any surfactant exchange. Chemical crosslinking of the NC-APS dispersion via hydrosilylation between Si-H and the vinyl group in APS immobilizes the CdS NCs in the polysiloxane network. Net-shaped three-dimensional bulk transparent polysiloxane/CdS NC composites were obtained by liquid casting of the NC-precursor dispersion and chemical crosslinking. The PSO/CdS NC composites show visible luminescence under ultraviolet excitation and the luminescent color is tunable from blue to red by controlling the NC concentration in the composite. Photoluminescence spectral analyses reveal the origin of the luminescence as being from the defect emission of the CdS NCs (550-900 nm) and an emission from the PSO matrix (380-550 nm). The luminescent spectra covered a wide range from the ultraviolet to the near-infrared region. The luminescence of the PSO/CdS NC nanocomposites was stable without any apparent degradation after exposure to air for a long time. This simple direct dispersion process is feasible for the fabrication of luminescent nanocomposites with useful optical properties for potential applications in optics and photoelectron devices.

Seminal quality prediction using data mining methods.

PubMed

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of fertility rate. In this paper, eight feature selection methods are applied on fertility dataset to find out a set of good features. The investigational results shows that childish diseases (0.079) and high fever features (0.057) has less impact on fertility rate while age (0.8685), season (0.843), surgical intervention (0.7683), alcohol consumption (0.5992), smoking habit (0.575), number of hours spent on setting (0.4366) and accident (0.5973) features have more impact. It is also observed that feature selection methods increase the accuracy of above mentioned techniques (multilayer perceptron 92%, support vector machine 91%, SVM+PSO 94%, Navie Bayes (Kernel) 89% and decision tree 89%) as compared to without feature selection methods (multilayer perceptron 86%, support vector machine 86%, SVM+PSO 85%, Navie Bayes (Kernel) 83% and decision tree 84%) which shows the applicability of feature selection methods in prediction. This paper lightens the application of artificial techniques in medical domain. From this paper, it can be concluded that data mining methods can be used to predict a person with or without disease based on environmental and lifestyle parameters/features rather than undergoing various medical test. In this paper, five data mining techniques are used to predict the fertility rate and among which SVM+PSO provide more accurate results than support vector machine and decision tree.

Texture Analysis of Recurrence Plots Based on Wavelets and PSO for Laryngeal Pathologies Detection.

PubMed

Souza, Taciana A; Vieira, Vinícius J D; Correia, Suzete E N; Costa, Silvana L N C; de A Costa, Washington C; Souza, Micael A

2015-01-01

This paper deals with the discrimination between healthy and pathological speech signals using recurrence plots and wavelet transform with texture features. Approximation and detail coefficients are obtained from the recurrence plots using Haar wavelet transform, considering one decomposition level. The considered laryngeal pathologies are: paralysis, Reinke's edema and nodules. Accuracy rates above 86% were obtained by means of the employed method.

Unified Research on Network-Based Hard/Soft Information Fusion

DTIC Science & Technology

2016-02-02

types). There are a number of search tree run parameters which must be set depending on the experimental setting. A pilot study was run to identify...Unlimited Final Report: Unified Research on Network-Based Hard/Soft Information Fusion The views, opinions and/or findings contained in this report...Final Report: Unified Research on Network-Based Hard/Soft Information Fusion Report Title The University at Buffalo (UB) Center for Multisource

Maxillary growth after maxillary protraction: Appliance in conjunction with presurgical orthopedics, gingivoperiosteoplasty, and Furlow palatoplasty for complete bilateral cleft lip and palate patients with protruded premaxilla.

PubMed

Kobayashi, Shinji; Hirakawa, Takashi; Fukawa, Toshihiko; Maegawa, Jiro

2015-06-01

In bilateral cleft lip and palate (BCLP) with premaxillary protrusion, a good outcome with adequate maxillary development is difficult to achieve. The purpose of this article is to evaluate the maxillary growth after using presurgical orthopedics (PSO), gingivoperiosteoplasty (GPP), Furlow palatoplasty, and maxillary protraction appliance (MPA) for BCLP with premaxillary protrusion. Seven patients with complete BCLP with premaxillary protrusion were treated by PSO, cheiloplasty, GPP, and Furlow palatoplasty. MPA was used as part of the protocol for 6 months to 1 year for postoperative retardation of maxillary growth cases. Maxillary growth was evaluated by cephalometric analysis at 4 and 10 years of age, and bone formation at the alveolar cleft was evaluated by computed tomography (CT) imaging at 5 years of age. At 4 years of age, three of seven patients had apparent retardation of maxillary growth. The maxillary growth at 10 years of age was equivalent to the average value of normal Japanese after using MPA in three cases. At 5 years of age, only two of seven patients showed sufficient bone formation at the alveolar cleft to avoid alveolar bone grafting (ABG). Subsequently, ABG was performed in five patients. Although three of seven patients had apparent crossbite at 4 years of age, the maxillary growth of all patients at 10 years of age was approximately equivalent to the average value of normal Japanese after using MPA. A treatment protocol based on PSO, GPP, Furlow palatoplasty, and MPA may be an option, but long-term growth is unknown. Copyright © 2015 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges

NASA Astrophysics Data System (ADS)

Ketabchi, Hamed; Ataie-Ashtiani, Behzad

2015-01-01

This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision variables and more complexity. In terms of computational time, PSO and SIMPSA are the fastest. SCE needs the highest computational time, even up to four times in comparison to the fastest EAs. CACO and PSO can be recommended for application in CGMPs, in terms of both abovementioned criteria.

The effects of tumour necrosis factor inhibitors, methotrexate, non-steroidal anti-inflammatory drugs and corticosteroids on cardiovascular events in rheumatoid arthritis, psoriasis and psoriatic arthritis: a systematic review and meta-analysis.

PubMed

Roubille, Camille; Richer, Vincent; Starnino, Tara; McCourt, Collette; McFarlane, Alexandra; Fleming, Patrick; Siu, Stephanie; Kraft, John; Lynde, Charles; Pope, Janet; Gulliver, Wayne; Keeling, Stephanie; Dutz, Jan; Bessette, Louis; Bissonnette, Robert; Haraoui, Boulos

2015-03-01

The objective of this systematic literature review was to determine the association between cardiovascular events (CVEs) and antirheumatic drugs in rheumatoid arthritis (RA) and psoriatic arthritis (PsA)/psoriasis (Pso). Systematic searches were performed of MEDLINE, EMBASE and Cochrane databases (1960 to December 2012) and proceedings from major relevant congresses (2010-2012) for controlled studies and randomised trials reporting confirmed CVEs in patients with RA or PsA/Pso treated with antirheumatic drugs. Random-effects meta-analyses were performed on extracted data. Out of 2630 references screened, 34 studies were included: 28 in RA and 6 in PsA/Pso. In RA, a reduced risk of all CVEs was reported with tumour necrosis factor inhibitors (relative risk (RR), 0.70; 95% CI 0.54 to 0.90; p=0.005) and methotrexate (RR, 0.72; 95% CI 0.57 to 0.91; p=0.007). Non-steroidal anti-inflammatory drugs (NSAIDs) increased the risk of all CVEs (RR, 1.18; 95% CI 1.01 to 1.38; p=0.04), which may have been specifically related to the effects of rofecoxib. Corticosteroids increased the risk of all CVEs (RR, 1.47; 95% CI 1.34 to 1.60; p<0.001). In PsA/Pso, systemic therapy decreased the risk of all CVEs (RR, 0.75; 95% CI 0.63 to 0.91; p=0.003). In RA, tumour necrosis factor inhibitors and methotrexate are associated with a decreased risk of all CVEs while corticosteroids and NSAIDs are associated with an increased risk. Targeting inflammation with tumour necrosis factor inhibitors or methotrexate may have positive cardiovascular effects in RA. In PsA/Pso, limited evidence suggests that systemic therapies are associated with a decrease in all CVE risk. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

THE EXTREMELY RED, YOUNG L DWARF PSO J318.5338–22.8603: A FREE-FLOATING PLANETARY-MASS ANALOG TO DIRECTLY IMAGED YOUNG GAS-GIANT PLANETS

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

Liu, Michael C.; Magnier, Eugene A.; Kotson, Michael C.

2013-11-10

We have discovered using Pan-STARRS1 an extremely red late-L dwarf, which has (J – K){sub MKO} = 2.78 and (J – K){sub 2MASS} = 2.84, making it the reddest known field dwarf and second only to 2MASS J1207–39b among substellar companions. Near-IR spectroscopy shows a spectral type of L7 ± 1 and reveals a triangular H-band continuum and weak alkali (K I and Na I) lines, hallmarks of low surface gravity. Near-IR astrometry from the Hawaii Infrared Parallax Program gives a distance of 24.6 ± 1.4 pc and indicates a much fainter J-band absolute magnitude than field L dwarfs. Themore » position and kinematics of PSO J318.5–22 point to membership in the β Pic moving group. Evolutionary models give a temperature of 1160{sup +30}{sub -40} K and a mass of 6.5{sup +1.3}{sub -1.0} M {sub Jup}, making PSO J318.5–22 one of the lowest mass free-floating objects in the solar neighborhood. This object adds to the growing list of low-gravity field L dwarfs and is the first to be strongly deficient in methane relative to its estimated temperature. Comparing their spectra suggests that young L dwarfs with similar ages and temperatures can have different spectral signatures of youth. For the two objects with well constrained ages (PSO J318.5–22 and 2MASS J0355+11), we find their temperatures are ≈400 K cooler than field objects of similar spectral type but their luminosities are similar, i.e., these young L dwarfs are very red and unusually cool but not 'underluminous'. Altogether, PSO J318.5–22 is the first free-floating object with the colors, magnitudes, spectrum, luminosity, and mass that overlap the young dusty planets around HR 8799 and 2MASS J1207–39.« less

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

PubMed Central

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy. PMID:29768463

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

PubMed

Rani R, Hannah Jessie; Victoire T, Aruldoss Albert

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.

A comparative analysis of swarm intelligence techniques for feature selection in cancer classification.

PubMed

Gunavathi, Chellamuthu; Premalatha, Kandasamy

2014-01-01

Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL.

Optimizing Thermal-Elastic Properties of C/C–SiC Composites Using a Hybrid Approach and PSO Algorithm

PubMed Central

Xu, Yingjie; Gao, Tian

2016-01-01

Carbon fiber-reinforced multi-layered pyrocarbon–silicon carbide matrix (C/C–SiC) composites are widely used in aerospace structures. The complicated spatial architecture and material heterogeneity of C/C–SiC composites constitute the challenge for tailoring their properties. Thus, discovering the intrinsic relations between the properties and the microstructures and sequentially optimizing the microstructures to obtain composites with the best performances becomes the key for practical applications. The objective of this work is to optimize the thermal-elastic properties of unidirectional C/C–SiC composites by controlling the multi-layered matrix thicknesses. A hybrid approach based on micromechanical modeling and back propagation (BP) neural network is proposed to predict the thermal-elastic properties of composites. Then, a particle swarm optimization (PSO) algorithm is interfaced with this hybrid model to achieve the optimal design for minimizing the coefficient of thermal expansion (CTE) of composites with the constraint of elastic modulus. Numerical examples demonstrate the effectiveness of the proposed hybrid model and optimization method. PMID:28773343

Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents.

PubMed

Barba, Lida; Rodríguez, Nibaldo; Montt, Cecilia

2014-01-01

Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD). In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0:26%, followed by MA-ARIMA with a MAPE of 1:12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15:51%.

A Modified MinMax k-Means Algorithm Based on PSO.

PubMed

Wang, Xiaoyan; Bai, Yanping

The MinMax k -means algorithm is widely used to tackle the effect of bad initialization by minimizing the maximum intraclustering errors. Two parameters, including the exponent parameter and memory parameter, are involved in the executive process. Since different parameters have different clustering errors, it is crucial to choose appropriate parameters. In the original algorithm, a practical framework is given. Such framework extends the MinMax k -means to automatically adapt the exponent parameter to the data set. It has been believed that if the maximum exponent parameter has been set, then the programme can reach the lowest intraclustering errors. However, our experiments show that this is not always correct. In this paper, we modified the MinMax k -means algorithm by PSO to determine the proper values of parameters which can subject the algorithm to attain the lowest clustering errors. The proposed clustering method is tested on some favorite data sets in several different initial situations and is compared to the k -means algorithm and the original MinMax k -means algorithm. The experimental results indicate that our proposed algorithm can reach the lowest clustering errors automatically.

Microseismic event location using global optimization algorithms: An integrated and automated workflow

NASA Astrophysics Data System (ADS)

Lagos, Soledad R.; Velis, Danilo R.

2018-02-01

We perform the location of microseismic events generated in hydraulic fracturing monitoring scenarios using two global optimization techniques: Very Fast Simulated Annealing (VFSA) and Particle Swarm Optimization (PSO), and compare them against the classical grid search (GS). To this end, we present an integrated and optimized workflow that concatenates into an automated bash script the different steps that lead to the microseismic events location from raw 3C data. First, we carry out the automatic detection, denoising and identification of the P- and S-waves. Secondly, we estimate their corresponding backazimuths using polarization information, and propose a simple energy-based criterion to automatically decide which is the most reliable estimate. Finally, after taking proper care of the size of the search space using the backazimuth information, we perform the location using the aforementioned algorithms for 2D and 3D usual scenarios of hydraulic fracturing processes. We assess the impact of restricting the search space and show the advantages of using either VFSA or PSO over GS to attain significant speed-ups.

The Effect of CaO on Gas/Slag/Matte/Tridymite Equilibria in Fayalite-Based Copper Smelting Slags at 1473 K (1200 °C) and P(SO2) = 0.25 Atm

NASA Astrophysics Data System (ADS)

Fallah-Mehrjardi, Ata; Hayes, Peter C.; Jak, Evgueni

2018-04-01

Fundamental experimental studies have been undertaken to determine the effect of CaO on the equilibria between the gas phase (CO/CO2/SO2/Ar) and slag/matte/tridymite phases in the Cu-Fe-O-S-Si-Ca system at 1473 K (1200 °C) and P(SO2) = 0.25 atm. The experimental methodology developed in the Pyrometallurgy Innovation Centre was used. New experimental data have been obtained for the four-phase equilibria system for fixed concentrations of CaO (up to 4 wt pct) in the slag phase as a function of copper concentration in matte, including the concentrations of dissolved sulfur and copper in slag, and Fe/SiO2 ratios in slag at tridymite saturation. The new data provided in the present study are of direct relevance to the pyrometallurgical processing of copper and will be used as an input to optimize the thermodynamic database for the copper-containing multi-component multi-phase system.

Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems

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

Kornelakis, Aris

2010-12-15

Particle Swarm Optimization (PSO) is a highly efficient evolutionary optimization algorithm. In this paper a multiobjective optimization algorithm based on PSO applied to the optimal design of photovoltaic grid-connected systems (PVGCSs) is presented. The proposed methodology intends to suggest the optimal number of system devices and the optimal PV module installation details, such that the economic and environmental benefits achieved during the system's operational lifetime period are both maximized. The objective function describing the economic benefit of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Valuemore » (NPV). The second objective function, which corresponds to the environmental benefit, equals to the pollutant gas emissions avoided due to the use of the PVGCS. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. (author)« less

Optimized Hyper Beamforming of Linear Antenna Arrays Using Collective Animal Behaviour

PubMed Central

Ram, Gopi; Mandal, Durbadal; Kar, Rajib; Ghoshal, Sakti Prasad

2013-01-01

A novel optimization technique which is developed on mimicking the collective animal behaviour (CAB) is applied for the optimal design of hyper beamforming of linear antenna arrays. Hyper beamforming is based on sum and difference beam patterns of the array, each raised to the power of a hyperbeam exponent parameter. The optimized hyperbeam is achieved by optimization of current excitation weights and uniform interelement spacing. As compared to conventional hyper beamforming of linear antenna array, real coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE) applied to the hyper beam of the same array can achieve reduction in sidelobe level (SLL) and same or less first null beam width (FNBW), keeping the same value of hyperbeam exponent. Again, further reductions of sidelobe level (SLL) and first null beam width (FNBW) have been achieved by the proposed collective animal behaviour (CAB) algorithm. CAB finds near global optimal solution unlike RGA, PSO, and DE in the present problem. The above comparative optimization is illustrated through 10-, 14-, and 20-element linear antenna arrays to establish the optimization efficacy of CAB. PMID:23970843

Efficacy and Safety Outcomes for Originator TNF Inhibitors and Biosimilars in Rheumatoid Arthritis and Psoriasis Trials: A Systematic Literature Review.

PubMed

Moots, Robert J; Curiale, Cinzia; Petersel, Danielle; Rolland, Catherine; Jones, Heather; Mysler, Eduardo

2018-05-22

Regulatory approval of biosimilar versions of originator biotherapeutics requires that new biological products be highly similar to originator products, with no clinically meaningful differences in safety, purity, and potency. In some trials of biosimilars of tumor necrosis factor inhibitors for the treatment of rheumatoid arthritis (RA) and plaque psoriasis (PsO), pre-specified margins for efficacy and safety have been met, but differences in treatment responses between pivotal originator trials and biosimilar trials have been noted. The objective of this systematic review was to examine these differences. Searches were conducted to identify comparative randomized clinical trials of approved or proposed biosimilars of adalimumab, etanercept, and infliximab. Of 83 publications identified, 16 publications were included for analysis (RA: originators, n = 5; biosimilars, n = 6; PsO: originators, n = 2; biosimilars, n = 3). American College of Rheumatology 20% response rates were higher among patients with RA receiving originator biologics and biosimilars in biosimilar trials than among patients receiving the originator biologics in pivotal trials. In etanercept studies in PsO, a difference was observed in Psoriasis Area and Severity Index 75% response rates between biosimilar and pivotal trials. Insufficient efficacy data were available from adalimumab and infliximab biosimilar studies in PsO to determine any differences in treatment responses between pivotal and biosimilar studies. Observed differences in treatment response rates between pivotal originator trials and trials of originator biologics and their respective biosimilars may be attributable to fundamental differences in study design and/or baseline patient characteristics, which require further analysis.

A novel artificial immune clonal selection classification and rule mining with swarm learning model

NASA Astrophysics Data System (ADS)

Al-Sheshtawi, Khaled A.; Abdul-Kader, Hatem M.; Elsisi, Ashraf B.

2013-06-01

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

Automatic Parameter Tuning for the Morpheus Vehicle Using Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Birge, B.

2013-01-01

A high fidelity simulation using a PC based Trick framework has been developed for Johnson Space Center's Morpheus test bed flight vehicle. There is an iterative development loop of refining and testing the hardware, refining the software, comparing the software simulation to hardware performance and adjusting either or both the hardware and the simulation to extract the best performance from the hardware as well as the most realistic representation of the hardware from the software. A Particle Swarm Optimization (PSO) based technique has been developed that increases speed and accuracy of the iterative development cycle. Parameters in software can be automatically tuned to make the simulation match real world subsystem data from test flights. Special considerations for scale, linearity, discontinuities, can be all but ignored with this technique, allowing fast turnaround both for simulation tune up to match hardware changes as well as during the test and validation phase to help identify hardware issues. Software models with insufficient control authority to match hardware test data can be immediately identified and using this technique requires very little to no specialized knowledge of optimization, freeing model developers to concentrate on spacecraft engineering. Integration of the PSO into the Morpheus development cycle will be discussed as well as a case study highlighting the tool's effectiveness.

A wearable, low-power, health-monitoring instrumentation based on a Programmable System-on-Chip.

PubMed

Massot, Bertrand; Gehin, Claudine; Nocua, Ronald; Dittmar, Andre; McAdams, Eric

2009-01-01

Improvement in quality and efficiency of health and medicine, at home and in hospital, has become of paramount importance. The solution of this problem would require the continuous monitoring of several key patient parameters, including the assessment of autonomic nervous system (ANS) activity using non-invasive sensors, providing information for emotional, sensorial, cognitive and physiological analysis of the patient. Recent advances in embedded systems, microelectronics, sensors and wireless networking enable the design of wearable systems capable of such advanced health monitoring. The subject of this article is an ambulatory system comprising a small wrist device connected to several sensors for the detection of the autonomic nervous system activity. It affords monitoring of skin resistance, skin temperature and heart activity. It is also capable of recording the data on a removable media or sending it to computer via a wireless communication. The wrist device is based on a Programmable System-on-Chip (PSoC) from Cypress: PSoCs are mixed-signal arrays, with dynamic, configurable digital and analogical blocks and an 8-bit Microcontroller unit (MCU) core on a single chip. In this paper we present first of all the hardware and software architecture of the device, and then results obtained from initial experiments.

A comprehensive numerical analysis of the hydraulic behavior of a permeable pavement

NASA Astrophysics Data System (ADS)

Brunetti, Giuseppe; Šimůnek, Jiří; Piro, Patrizia

2016-09-01

The increasing frequency of flooding events in urban catchments related to an increase in impervious surfaces highlights the inadequacy of traditional urban drainage systems. Low Impact Development (LID) techniques have proven to be a viable and effective alternative by reducing stormwater runoff and increasing the infiltration and evapotranspiration capacity of urban areas. However, the lack of adequate modeling tools represents a barrier in designing and constructing such systems. This paper investigates the suitability of a mechanistic model, HYDRUS-1D, to correctly describe the hydraulic behavior of permeable pavement installed at the University of Calabria. Two different scenarios of describing the hydraulic behavior of the permeable pavement system were analyzed: the first one uses a single-porosity model for all layers of the permeable pavement; the second one uses a dual-porosity model for the base and sub-base layers. Measured and modeled month-long hydrographs were compared using the Nash-Sutcliffe efficiency (NSE) index. A Global Sensitivity Analysis (GSA) followed by a Monte Carlo filtering highlighted the influence of the wear layer on the hydraulic behavior of the pavement and identified the ranges of parameters generating behavioral solutions. Reduced ranges were then used in the calibration procedure conducted with the metaheuristic Particle swarm optimization (PSO) algorithm for the estimation of hydraulic parameters. The best fit value for the first scenario was NSE = 0.43; for the second scenario, it was NSE = 0.81, indicating that the dual-porosity approach is more appropriate for describing the variably-saturated flow in the base and sub-base layers. Estimated parameters were validated using an independent, month-long set of measurements, resulting in NSE values of 0.43 and 0.86 for the first and second scenarios, respectively. The improvement in correspondence between measured and modeled hydrographs confirmed the reliability of the combination of GSA and PSO in dealing with highly dimensional optimization problems. Obtained results have demonstrated that PSO, due to its easiness of implementation and effectiveness, can represent a new and viable alternative to traditional optimization algorithms for the inverse estimation of unsaturated hydraulic properties. Finally, the results confirmed the suitability and the accuracy of HYDRUS-1D in correctly describing the hydraulic behavior of permeable pavements.

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

PubMed

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

NASA Astrophysics Data System (ADS)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

The Neurochip-2: An Autonomous Head-Fixed Computer for Recording and Stimulating in Freely Behaving Monkeys

PubMed Central

Zanos, Stavros; Richardson, Andrew G.; Shupe, Larry; Miles, Frank P.; Fetz, Eberhard E.

2011-01-01

The Neurochip-2 is a second generation, battery-powered device for neural recording and stimulating that is small enough to be carried in a chamber on a monkey’s head. It has three recording channels, with user-adjustable gains, filters, and sampling rates, that can be optimized for recording single unit activity, local field potentials, electrocorticography, electromyography, arm acceleration, etc. Recorded data are stored on a removable, flash memory card. The Neurochip-2 also has three separate stimulation channels. Two “programmable-system-on-chips” (PSoCs) control the data acquisition and stimulus output. The PSoCs permit flexible real-time processing of the recorded data, such as digital filtering and time-amplitude window discrimination. The PSoCs can be programmed to deliver stimulation contingent on neural events or deliver preprogrammed stimuli. Access pins to the microcontroller are also available to connect external devices, such as accelerometers. The Neurochip-2 can record and stimulate autonomously for up to several days in freely behaving monkeys, enabling a wide range of novel neurophysiological and neuroengineering experiments. PMID:21632309

Modern Optimization Methods in Minimum Weight Design of Elastic Annular Rotating Disk with Variable Thickness

NASA Astrophysics Data System (ADS)

Jafari, S.; Hojjati, M. H.

2011-12-01

Rotating disks work mostly at high angular velocity and this results a large centrifugal force and consequently induce large stresses and deformations. Minimizing weight of such disks yields to benefits such as low dead weights and lower costs. This paper aims at finding an optimal disk thickness profile for minimum weight design using the simulated annealing (SA) and particle swarm optimization (PSO) as two modern optimization techniques. In using semi-analytical the radial domain of the disk is divided into some virtual sub-domains as rings where the weight of each rings must be minimized. Inequality constrain equation used in optimization is to make sure that maximum von Mises stress is always less than yielding strength of the material of the disk and rotating disk does not fail. The results show that the minimum weight obtained for all two methods is almost identical. The PSO method gives a profile with slightly less weight (6.9% less than SA) while the implementation of both PSO and SA methods are easy and provide more flexibility compared with classical methods.

Enhancing Degradation of Low Density Polyethylene Films by Curvularia lunata SG1 Using Particle Swarm Optimization Strategy.

PubMed

Raut, Sangeeta; Raut, Smita; Sharma, Manisha; Srivastav, Chaitanya; Adhikari, Basudam; Sen, Sudip Kumar

2015-09-01

In the present study, artificial neural network (ANN) modelling coupled with particle swarm optimization (PSO) algorithm was used to optimize the process variables for enhanced low density polyethylene (LDPE) degradation by Curvularia lunata SG1. In the non-linear ANN model, temperature, pH, contact time and agitation were used as input variables and polyethylene bio-degradation as the output variable. Further, on application of PSO to the ANN model, the optimum values of the process parameters were as follows: pH = 7.6, temperature = 37.97 °C, agitation rate = 190.48 rpm and incubation time = 261.95 days. A comparison between the model results and experimental data gave a high correlation coefficient ([Formula: see text]). Significant enhancement of LDPE bio-degradation using C. lunata SG1by about 48 % was achieved under optimum conditions. Thus, the novelty of the work lies in the application of combination of ANN-PSO as optimization strategy to enhance the bio-degradation of LDPE.

Training telescope operators and support astronomers at Paranal

NASA Astrophysics Data System (ADS)

Boffin, Henri M. J.; Gadotti, Dimitri A.; Anderson, Joe; Pino, Andres; de Wit, Willem-Jan; Girard, Julien H. V.

2016-07-01

The operations model of the Paranal Observatory relies on the work of efficient staff to carry out all the daytime and nighttime tasks. This is highly dependent on adequate training. The Paranal Science Operations department (PSO) has a training group that devises a well-defined and continuously evolving training plan for new staff, in addition to broadening and reinforcing courses for the whole department. This paper presents the training activities for and by PSO, including recent astronomical and quality control training for operators, as well as adaptive optics and interferometry training of all staff. We also present some future plans.

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

NASA Astrophysics Data System (ADS)

Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

2018-03-01

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

Image segmentation algorithm based on improved PCNN

NASA Astrophysics Data System (ADS)

Chen, Hong; Wu, Chengdong; Yu, Xiaosheng; Wu, Jiahui

2017-11-01

A modified simplified Pulse Coupled Neural Network (PCNN) model is proposed in this article based on simplified PCNN. Some work have done to enrich this model, such as imposing restrictions items of the inputs, improving linking inputs and internal activity of PCNN. A self-adaptive parameter setting method of linking coefficient and threshold value decay time constant is proposed here, too. At last, we realized image segmentation algorithm for five pictures based on this proposed simplified PCNN model and PSO. Experimental results demonstrate that this image segmentation algorithm is much better than method of SPCNN and OTSU.

A multi-damages identification method for cantilever beam based on mode shape curvatures and Kriging surrogate model

NASA Astrophysics Data System (ADS)

Xie, Fengle; Jiang, Zhansi; Jiang, Hui

2018-05-01

This paper presents a multi-damages identification method for Cantilever Beam. First, the damage location is identified by using the mode shape curvatures. Second, samples of varying damage severities at the damage location and their corresponding natural frequencies are used to construct the initial Kriging surrogate model. Then a particle swarm optimization (PSO) algorithm is employed to identify the damage severities based on Kriging surrogate model. The simulation study of a double-damaged cantilever beam demonstrated that the proposed method is effective.

The Development of Cadastral Domain Model Oriented at Unified Real Estate Registration of China Based on Ontology

NASA Astrophysics Data System (ADS)

Li, M.; Zhu, X.; Shen, C.; Chen, D.; Guo, W.

2012-07-01

With the certain regulation of unified real estate registration taken by the Property Law and the step-by-step advance of simultaneous development in urban and rural in China, it is the premise and foundation to clearly specify property rights and their relations in promoting the integrated management of urban and rural land. This paper aims at developing a cadastral domain model oriented at unified real estate registration of China from the perspective of legal and spatial, which set up the foundation for unified real estate registration, and facilitates the effective interchange of cadastral information and the administration of land use. The legal cadastral model is provided based on the analysis of gap between current model and the demand of unified real estate registration, which implies the restrictions between different rights. Then the new cadastral domain model is constructed based on the legal cadastral domain model and CCDM (van Oosterom et al., 2006), which integrate real estate rights of urban land and rural land. Finally, the model is validated by a prototype system. The results show that the model is applicable for unified real estate registration in China.

The twisted radio structure of PSO J334.2028+01.4075, still a supermassive binary black hole candidate

NASA Astrophysics Data System (ADS)

Mooley, K. P.; Wrobel, J. M.; Anderson, M. M.; Hallinan, G.

2018-01-01

Supermassive binary black holes (BBHs) on sub-parsec scales are prime targets for gravitational wave experiments. They also provide insights on close binary evolution and hierarchical structure formation. Sub-parsec BBHs cannot be spatially resolved but indirect methods can identify candidates. In 2015 Liu et al. reported an optical-continuum periodicity in the quasar PSO J334.2028+01.4075, with the estimated mass and rest-frame period suggesting an orbital separation of about 0.006 pc (0.7 μ arcsec). The persistence of the quasar's optical periodicity has recently been disfavoured over an extended baseline. However, if a radio jet is launched from a sub-parsec BBH, the binary's properties can influence the radio structure on larger scales. Here, we use the Very Long Baseline Array (VLBA) and Karl G. Jansky Very Large Array (VLA) to study the parsec- and kiloparsec-scale emission energized by the quasar's putative BBH. We find two VLBA components separated by 3.6 mas (30 pc), tentatively identifying one as the VLBA 'core' from which the other was ejected. The VLBA components contribute to a point-like, time-variable VLA source that is straddled by lobes spanning 8 arcsec (66 kpc). We classify PSO J334.2028+01.4075 as a lobe-dominated quasar, albeit with an atypically large twist of 39° between its elongation position angles on parsec- and kiloparsec-scales. By analogy with 3C 207, a well-studied lobe-dominated quasar with a similarly-rare twist, we speculate that PSO J334.2028+01.4075 could be ejecting jet components over an inner cone that traces a precessing jet in a BBH system.

Developing and Evaluating an Automated All-Cause Harm Trigger System.

PubMed

Sammer, Christine; Miller, Susanne; Jones, Cason; Nelson, Antoinette; Garrett, Paul; Classen, David; Stockwell, David

2017-04-01

From 2009 through 2012, the Adventist Health System Patient Safety Organization (AHS PSO) used the Global Trigger Tool method for harm identification and demonstrated harm reduction. Although the awareness of harm demonstrated opportunities for improvement across the system, leaders determined that the human and fiscal resources required to continue with a retrospective manual harm identification process were unsustainable. In addition, there was growing concern that the identification of harm after the patient's discharge did not allow for intervention during the hospital stay. Therefore, the AHS PSO decided to seek an alternative method for patient harm identification. The AHS PSO and another PSO jointly developed a novel automated all-cause harm trigger identification system that allowed for real-time bedside intervention, real-time trend analysis affecting patient safety, and continued learning about harm measurement. A sociotechnical approach of people, process, and technology was used at two pilot hospitals sharing the same electronic health record platform. Automated positive harm triggers and work-flow models were developed and evaluated. Combined data from the two hospitals in a period of 11 consecutive months indicated (1) a total of 2,696 harms (combined hospital-acquired and outside-acquired); (2) that hypoglycemia (blood glucose ≤ 40 mg/dL) was the most frequently identified harm; (3) 256 harms related to the Patient Safety Indicator 90 (PSI 90) Composite descriptions versus 77 harms reported to regulatory harm reduction programs; and (4) that almost one third (32%) of total harms were classified as outside-acquired. The automated harm trigger system revealed not only more harm but a broader scope of harm and led to a deeper understanding of patient safety vulnerabilities. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Enhancement web proxy cache performance using Wrapper Feature Selection methods with NB and J48

NASA Astrophysics Data System (ADS)

Mahmoud Al-Qudah, Dua'a.; Funke Olanrewaju, Rashidah; Wong Azman, Amelia

2017-11-01

Web proxy cache technique reduces response time by storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. Due to the limited size and excessive cost of cache compared to the other storages, cache replacement algorithm is used to determine evict page when the cache is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomized Policy etc. may discard important pages just before use. Furthermore, using conventional algorithm cannot be well optimized since it requires some decision to intelligently evict a page before replacement. Hence, most researchers propose an integration among intelligent classifiers and replacement algorithm to improves replacement algorithms performance. This research proposes using automated wrapper feature selection methods to choose the best subset of features that are relevant and influence classifiers prediction accuracy. The result present that using wrapper feature selection methods namely: Best First (BFS), Incremental Wrapper subset selection(IWSS)embedded NB and particle swarm optimization(PSO)reduce number of features and have a good impact on reducing computation time. Using PSO enhance NB classifier accuracy by 1.1%, 0.43% and 0.22% over using NB with all features, using BFS and using IWSS embedded NB respectively. PSO rises J48 accuracy by 0.03%, 1.91 and 0.04% over using J48 classifier with all features, using IWSS-embedded NB and using BFS respectively. While using IWSS embedded NB fastest NB and J48 classifiers much more than BFS and PSO. However, it reduces computation time of NB by 0.1383 and reduce computation time of J48 by 2.998.

Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data.

PubMed

Chatterjee, Sankhadeep; Dey, Nilanjan; Shi, Fuqian; Ashour, Amira S; Fong, Simon James; Sen, Soumya

2018-04-01

Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.

Classification-based quantitative analysis of stable isotope labeling by amino acids in cell culture (SILAC) data.

PubMed

Kim, Seongho; Carruthers, Nicholas; Lee, Joohyoung; Chinni, Sreenivasa; Stemmer, Paul

2016-12-01

Stable isotope labeling by amino acids in cell culture (SILAC) is a practical and powerful approach for quantitative proteomic analysis. A key advantage of SILAC is the ability to simultaneously detect the isotopically labeled peptides in a single instrument run and so guarantee relative quantitation for a large number of peptides without introducing any variation caused by separate experiment. However, there are a few approaches available to assessing protein ratios and none of the existing algorithms pays considerable attention to the proteins having only one peptide hit. We introduce new quantitative approaches to dealing with SILAC protein-level summary using classification-based methodologies, such as Gaussian mixture models with EM algorithms and its Bayesian approach as well as K-means clustering. In addition, a new approach is developed using Gaussian mixture model and a stochastic, metaheuristic global optimization algorithm, particle swarm optimization (PSO), to avoid either a premature convergence or being stuck in a local optimum. Our simulation studies show that the newly developed PSO-based method performs the best among others in terms of F1 score and the proposed methods further demonstrate the ability of detecting potential markers through real SILAC experimental data. No matter how many peptide hits the protein has, the developed approach can be applicable, rescuing many proteins doomed to removal. Furthermore, no additional correction for multiple comparisons is necessary for the developed methods, enabling direct interpretation of the analysis outcomes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Unified Approximations: A New Approach for Monoprotic Weak Acid-Base Equilibria

ERIC Educational Resources Information Center

Pardue, Harry; Odeh, Ihab N.; Tesfai, Teweldemedhin M.

2004-01-01

The unified approximations reduce the conceptual complexity by combining solutions for a relatively large number of different situations into just two similar sets of processes. Processes used to solve problems by either the unified or classical approximations require similar degrees of understanding of the underlying chemical processes.

Premaxillary characteristics in complete bilateral cleft lip and palate: A predictor for treatment outcome

PubMed Central

El-Kassaby, Marwa Abdelwahab; Abdelrahman, Noha Ibrahim; Abbass, Islam Tarek

2013-01-01

Objectives: The aim of the current study was to investigate how bilateral cleft lip and palate (BCLP) cases responded differently to presurgical orthopedics (PSO) and primary lip repair (LR) based on premaxillary characteristics. We suggest a clinically oriented descriptive classification for BCLP based on premaxillary characteristics. Design and Setting: A retrospective longitudinal comparative study where available records of all non-syndromic patients with complete BCLP attending the Cleft Clinic, affiliated to the Oral and Maxillofacial Surgery department, Ain-Shams University, Cairo, Egypt were assessed. Sample Population and Methodology: Twenty-two cases were collected over a 4-years period from 2008 to 2011 (15 boys and 7 girls). Model assessment was performed for serial models representing four stages of treatment; M1: Prior to start of PSO, M2: At the end of PSO, M3: One month after LR, M4: Three months after LR. The premaxillary and vomerine widths were measured on M1. Models (M1-M4) were assessed for changes in anteroposterior projection, anterior arch width, intercanine width and posterior arch width and results were statistically analyzed. Intra-and postoperative surgical findings during and after primary LR were recorded. The sample was divided into two groups based on the premaxillary size and characteristics; Group R: Rudimentary premaxilla and Group P: Prominent premaxilla. Results: There was a highly significant difference in premaxillary width between the two groups (P = 0.00), changes in anteroposterior projection of the premaxilla were significant one and three months after LR. Changes in maxillary anterior arch width, intercanine and posterior arch widths were non-significant between groups. Mean age difference between the two groups was only statistically significant at the stage of LR. Surgical differences were noted between the two groups. Postoperatively as compared to group R; group P showed more premaxillary bulge and show at rest, as well as more prolabial stretching. In addition, facial profile was more convex in group P. Conclusion: The two types of BCLP outlined in this study are different from several aspects, and hence management should be modified according to each case. This descriptive classification provides a useful tool for evaluation and planning of patients with BCLP. PMID:23662253

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability.

PubMed

Hossain, Monowar; Mekhilef, Saad; Afifi, Firdaus; Halabi, Laith M; Olatomiwa, Lanre; Seyedmahmoudian, Mehdi; Horan, Ben; Stojcevski, Alex

2018-01-01

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

FamilyPso - a new questionnaire to assess the impact of psoriasis on partners and family of patients.

PubMed

Mrowietz, U; Hartmann, A; Weißmann, W; Zschocke, I

2017-01-01

Psoriasis is a lifelong disease for which there is no cure. It has been conclusively shown across all ethnicities that patients suffering from psoriasis have a significantly reduced health-related quality of life and a high disease burden. Surprisingly little is known about the impact of a patient's psoriasis on partners or family members. To address this issue a systematic literature search has been conducted and interviews with relatives of psoriasis patients living in the same household were performed. From this collected information, items were generated that were commonly mentioned to affect living and tested in a large group of relatives before the final item selection was done. A first set of 29 items was selected and tested in a study with 96 patient relatives. After adjustment and statistical analysis, the final FamilyPso questionnaire was condensed to 15 items to assess the burden of partners or family members living together with psoriasis patients. The FamilyPso enables physicians to achieve a better understanding of the impact of psoriasis as a lifelong chronic disease on partners and the family environment. © 2016 European Academy of Dermatology and Venereology.

PSO (Particle Swarm Optimization) for Interpretation of Magnetic Anomalies Caused by Simple Geometrical Structures

NASA Astrophysics Data System (ADS)

Essa, Khalid S.; Elhussein, Mahmoud

2018-04-01

A new efficient approach to estimate parameters that controlled the source dimensions from magnetic anomaly profile data in light of PSO algorithm (particle swarm optimization) has been presented. The PSO algorithm has been connected in interpreting the magnetic anomaly profiles data onto a new formula for isolated sources embedded in the subsurface. The model parameters deciphered here are the depth of the body, the amplitude coefficient, the angle of effective magnetization, the shape factor and the horizontal coordinates of the source. The model parameters evaluated by the present technique, generally the depth of the covered structures were observed to be in astounding concurrence with the real parameters. The root mean square (RMS) error is considered as a criterion in estimating the misfit between the observed and computed anomalies. Inversion of noise-free synthetic data, noisy synthetic data which contains different levels of random noise (5, 10, 15 and 20%) as well as multiple structures and in additional two real-field data from USA and Egypt exhibits the viability of the approach. Thus, the final results of the different parameters are matched with those given in the published literature and from geologic results.

The Effect of Microwave Roasting Over the Thermooxidative Degradation of Perah Seed Oil During Heating.

PubMed

Li, Khu Say; Ali, M Abbas; Muhammad, Ida Idayu; Othman, Noor Hidayu; Noor, Ahmadilfitri Md

2018-05-01

The impact of microwave roasting on the thermooxidative degradation of perah seed oil (PSO) was evaluated during heating at a frying temperature (170°C). The roasting resulted significantly lower increment of the values of oxidative indices such as free acidity, peroxide value, p-anisidine, total oxidation (TOTOX), specific extinctions and thiobarbituric acid in oils during heating. The colour L* (lightness) value dropped gradually as the heating time increased up to 12 h, whereas a*(redness) and b* (yellowness) tended to increase. The viscosity and total polar compound in roasted PSO was lower as compared to that in unroasted one at each heating times. The tocol retention was also high in roasted samples throughout the heating period. The relative contents of polyunsaturated fatty acids (PUFAs) were decreased to 94.42% and saturated fatty acids (SFAs) were increased to 110.20% in unroasted sample, after 12 h of heating. On the other hand, in 3 min roasted samples, the relative contents of PUFAs were decreased to 98.08% and of SFAs were increased to 103.41% after 12 h of heating. Outcome from analyses showed that microwave roasting reduced the oxidative deteriorations of PSO during heating.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

PubMed Central

Mekhilef, Saad; Afifi, Firdaus; Halabi, Laith M.; Olatomiwa, Lanre; Seyedmahmoudian, Mehdi; Stojcevski, Alex

2018-01-01

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations. PMID:29702645

Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms.

PubMed

Razavi Termeh, Seyed Vahid; Kornejady, Aiding; Pourghasemi, Hamid Reza; Keesstra, Saskia

2018-02-15

Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses. Copyright © 2017 Elsevier B.V. All rights reserved.

Clinical and radiological outcomes of lumbar posterior subtraction osteotomies are correlated to pelvic incidence and FBI index : Prospective series of 63 cases.

PubMed

Cogniet, A; Aunoble, S; Rigal, J; Demezon, H; Sadikki, R; Le Huec, J C

2016-08-01

Pedicle subtraction osteotomy (PSO) is one of the surgical options for treating alignment disorders of the fused spine (due to post-surgical fusion or related to arthritis). It enables satisfactory sagittal realignment and improved function due to economic sagittal balance. The aim of this study was to analyze clinical and radiological results of PSO after a minimum follow-up of 2 years and demonstrate the benefit of sub-group analysis as a function of pelvic incidence (PI). A descriptive prospective single center study of 63 patients presenting with spinal global malalignment who underwent correction by PSO. Function was assessed by the Oswestry disability index (ODI), a visual analog scale of lumbar pain (VAS) and a SF-36 questionnaire. Radiographic analyses of pre- and post-operative pelvic-spinal parameters were performed on X-rays obtained by EOS(®) imaging after 3D modeling. Global analysis and analysis of sub-groups as a function of pelvic incidence were performed and the full balance integrated index (FBI) was calculated. this series showed a marked clinical improvement and significant progress of functional scores. Global post-operative radiological analysis showed a significant improvement in all pelvic and spinal parameters. The mean correction obtained after PSO was 31.7° ± 8.4°, hence global improvement of lumbar lordosis of 22°. The sagittal vertical angle (SVA) decreased from +9 cm before surgery to +4.3 cm after surgery. Sub-group analysis demonstrated greater improvement in pelvic tilt, sacral slope and spinal parameters of patients with a small or moderate pelvic incidence; all had an FBI index <10°. Most of the pelvic and spinal parameters of patients with a large pelvic incidence were insufficiently corrected and they had an FBI index >10° PSO is a surgical procedure enabling correction of multiplane rigid spinal deformities that require major sagittal correction. It was seen to be highly effective in patients with a small or moderate pelvic incidence (PI <60°) but was sometimes less effective in patients with large pelvic incidence due to insufficient lordosis correction. Clinical results were highly correlated with the value of the FBI index.

Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

PubMed

Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin

2015-12-01

The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.

PubMed

Huang, Shuqiang; Tao, Ming

2017-01-22

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Modeling level change in Lake Urmia using hybrid artificial intelligence approaches

NASA Astrophysics Data System (ADS)

Esbati, M.; Ahmadieh Khanesar, M.; Shahzadi, Ali

2017-06-01

The investigation of water level fluctuations in lakes for protecting them regarding the importance of these water complexes in national and regional scales has found a special place among countries in recent years. The importance of the prediction of water level balance in Lake Urmia is necessary due to several-meter fluctuations in the last decade which help the prevention from possible future losses. For this purpose, in this paper, the performance of adaptive neuro-fuzzy inference system (ANFIS) for predicting the lake water level balance has been studied. In addition, for the training of the adaptive neuro-fuzzy inference system, particle swarm optimization (PSO) and hybrid backpropagation-recursive least square method algorithm have been used. Moreover, a hybrid method based on particle swarm optimization and recursive least square (PSO-RLS) training algorithm for the training of ANFIS structure is introduced. In order to have a more fare comparison, hybrid particle swarm optimization and gradient descent are also applied. The models have been trained, tested, and validated based on lake level data between 1991 and 2014. For performance evaluation, a comparison is made between these methods. Numerical results obtained show that the proposed methods with a reasonable error have a good performance in water level balance prediction. It is also clear that with continuing the current trend, Lake Urmia will experience more drop in the water level balance in the upcoming years.

Authorship attribution of source code by using back propagation neural network based on particle swarm optimization

PubMed Central

Xu, Guoai; Li, Qi; Guo, Yanhui; Zhang, Miao

2017-01-01

Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead. PMID:29095934

Large Scale Multi-area Static/Dynamic Economic Dispatch using Nature Inspired Optimization

NASA Astrophysics Data System (ADS)

Pandit, Manjaree; Jain, Kalpana; Dubey, Hari Mohan; Singh, Rameshwar

2017-04-01

Economic dispatch (ED) ensures that the generation allocation to the power units is carried out such that the total fuel cost is minimized and all the operating equality/inequality constraints are satisfied. Classical ED does not take transmission constraints into consideration, but in the present restructured power systems the tie-line limits play a very important role in deciding operational policies. ED is a dynamic problem which is performed on-line in the central load dispatch centre with changing load scenarios. The dynamic multi-area ED (MAED) problem is more complex due to the additional tie-line, ramp-rate and area-wise power balance constraints. Nature inspired (NI) heuristic optimization methods are gaining popularity over the traditional methods for complex problems. This work presents the modified particle swarm optimization (PSO) based techniques where parameter automation is effectively used for improving the search efficiency by avoiding stagnation to a sub-optimal result. This work validates the performance of the PSO variants with traditional solver GAMS for single as well as multi-area economic dispatch (MAED) on three test cases of a large 140-unit standard test system having complex constraints.

A novel swarm intelligence algorithm for finding DNA motifs.

PubMed

Lei, Chengwei; Ruan, Jianhua

2009-01-01

Discovering DNA motifs from co-expressed or co-regulated genes is an important step towards deciphering complex gene regulatory networks and understanding gene functions. Despite significant improvement in the last decade, it still remains one of the most challenging problems in computational molecular biology. In this work, we propose a novel motif finding algorithm that finds consensus patterns using a population-based stochastic optimisation technique called Particle Swarm Optimisation (PSO), which has been shown to be effective in optimising difficult multidimensional problems in continuous domains. We propose to use a word dissimilarity graph to remap the neighborhood structure of the solution space of DNA motifs, and propose a modification of the naive PSO algorithm to accommodate discrete variables. In order to improve efficiency, we also propose several strategies for escaping from local optima and for automatically determining the termination criteria. Experimental results on simulated challenge problems show that our method is both more efficient and more accurate than several existing algorithms. Applications to several sets of real promoter sequences also show that our approach is able to detect known transcription factor binding sites, and outperforms two of the most popular existing algorithms.

A Modified MinMax k-Means Algorithm Based on PSO

PubMed Central

2016-01-01

The MinMax k-means algorithm is widely used to tackle the effect of bad initialization by minimizing the maximum intraclustering errors. Two parameters, including the exponent parameter and memory parameter, are involved in the executive process. Since different parameters have different clustering errors, it is crucial to choose appropriate parameters. In the original algorithm, a practical framework is given. Such framework extends the MinMax k-means to automatically adapt the exponent parameter to the data set. It has been believed that if the maximum exponent parameter has been set, then the programme can reach the lowest intraclustering errors. However, our experiments show that this is not always correct. In this paper, we modified the MinMax k-means algorithm by PSO to determine the proper values of parameters which can subject the algorithm to attain the lowest clustering errors. The proposed clustering method is tested on some favorite data sets in several different initial situations and is compared to the k-means algorithm and the original MinMax k-means algorithm. The experimental results indicate that our proposed algorithm can reach the lowest clustering errors automatically. PMID:27656201

Swarm intelligence inspired shills and the evolution of cooperation.

PubMed

Duan, Haibin; Sun, Changhao

2014-06-09

Many hostile scenarios exist in real-life situations, where cooperation is disfavored and the collective behavior needs intervention for system efficiency improvement. Towards this end, the framework of soft control provides a powerful tool by introducing controllable agents called shills, who are allowed to follow well-designed updating rules for varying missions. Inspired by swarm intelligence emerging from flocks of birds, we explore here the dependence of the evolution of cooperation on soft control by an evolutionary iterated prisoner's dilemma (IPD) game staged on square lattices, where the shills adopt a particle swarm optimization (PSO) mechanism for strategy updating. We demonstrate that not only can cooperation be promoted by shills effectively seeking for potentially better strategies and spreading them to others, but also the frequency of cooperation could be arbitrarily controlled by choosing appropriate parameter settings. Moreover, we show that adding more shills does not contribute to further cooperation promotion, while assigning higher weights to the collective knowledge for strategy updating proves a efficient way to induce cooperative behavior. Our research provides insights into cooperation evolution in the presence of PSO-inspired shills and we hope it will be inspirational for future studies focusing on swarm intelligence based soft control.

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.

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

PubMed Central

Vázquez, Roberto A.

2015-01-01

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132

Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy.

PubMed

Nouri, S; Hosseini Pooya, S M; Soltani Nabipour, J

2017-03-01

The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO) estimating tumor positions in real-time radiotherapy. One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. The internal target volume (ITV) should be determined based on the applied neural network algorithm on training steps.

Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents

PubMed Central

Rodríguez, Nibaldo

2014-01-01

Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD). In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0 : 26%, followed by MA-ARIMA with a MAPE of 1 : 12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15 : 51%. PMID:25243200

PSO Based PI Controller Design for a Solar Charger System

PubMed Central

Yau, Her-Terng; Lin, Chih-Jer; Liang, Qin-Cheng

2013-01-01

Due to global energy crisis and severe environmental pollution, the photovoltaic (PV) system has become one of the most important renewable energy sources. Many previous studies on solar charger integrated system only focus on load charge control or switching Maximum Power Point Tracking (MPPT) and charge control modes. This study used two-stage system, which allows the overall portable solar energy charging system to implement MPPT and optimal charge control of Li-ion battery simultaneously. First, this study designs a DC/DC boost converter of solar power generation, which uses variable step size incremental conductance method (VSINC) to enable the solar cell to track the maximum power point at any time. The voltage was exported from the DC/DC boost converter to the DC/DC buck converter, so that the voltage dropped to proper voltage for charging the battery. The charging system uses constant current/constant voltage (CC/CV) method to charge the lithium battery. In order to obtain the optimum PI charge controller parameters, this study used intelligent algorithm to determine the optimum parameters. According to the simulation and experimental results, the control parameters resulted from PSO have better performance than genetic algorithms (GAs). PMID:23766713

PSO based PI controller design for a solar charger system.

PubMed

Yau, Her-Terng; Lin, Chih-Jer; Liang, Qin-Cheng

2013-01-01

Due to global energy crisis and severe environmental pollution, the photovoltaic (PV) system has become one of the most important renewable energy sources. Many previous studies on solar charger integrated system only focus on load charge control or switching Maximum Power Point Tracking (MPPT) and charge control modes. This study used two-stage system, which allows the overall portable solar energy charging system to implement MPPT and optimal charge control of Li-ion battery simultaneously. First, this study designs a DC/DC boost converter of solar power generation, which uses variable step size incremental conductance method (VSINC) to enable the solar cell to track the maximum power point at any time. The voltage was exported from the DC/DC boost converter to the DC/DC buck converter, so that the voltage dropped to proper voltage for charging the battery. The charging system uses constant current/constant voltage (CC/CV) method to charge the lithium battery. In order to obtain the optimum PI charge controller parameters, this study used intelligent algorithm to determine the optimum parameters. According to the simulation and experimental results, the control parameters resulted from PSO have better performance than genetic algorithms (GAs).

Effect of Ixekizumab Treatment on Work Productivity for Patients With Moderate-to-Severe Plaque Psoriasis: Analysis of Results From 3 Randomized Phase 3 Clinical Trials.

PubMed

Armstrong, April W; Lynde, Charles W; McBride, Sandy R; Ståhle, Mona; Edson-Heredia, Emily; Zhu, Baojin; Amato, David; Nikaï, Enkeleida; Yang, Fan Emily; Gordon, Kenneth B

2016-06-01

Therapies that reduce psoriasis symptoms may improve work productivity. To assess the effect of ixekizumab therapy on work productivity, measured by the Work Productivity and Activity Impairment-Psoriasis (WPAI-PSO). Three multicenter, randomized double-blind phase 3 trials conducted during the following periods: December 2011 through August 2014 (UNCOVER-1), May 2012 through April 2015 (UNCOVER-2), and August 2012 through July 2014 (UNCOVER-3). Adult outpatients with moderate-to-severe chronic plaque psoriasis were included. In UNCOVER-1, patients were randomized 1:1:1 to subcutaneous placebo or 80 mg ixekizumab every 2 weeks (Q2W) or every 4 weeks (Q4W) for 12 weeks; UNCOVER-2 and UNCOVER-3 also had an etanercept arm (50 mg twice weekly). Maintenance of initial ixekizumab response was evaluated in UNCOVER-1 and UNCOVER-2 during a randomized withdrawal period following week 12 through week 60. The WPAI-PSO questionnaire was administered at baseline and week 12 for all patients and at weeks 24, 36, 52, and 60 for patients in UNCOVER-1 and UNCOVER-2. Change in work productivity from baseline as measured by WPAI-PSO scores. Across trials, 5101 patients consented; 3866 were randomized (mean [SD] age, UNCOVER-1, 45.7 [12.9] y, 68.1% male; UNCOVER-2: 45.0 [13.0] y, 67.1% male; UNCOVER-3: 45.8 [13.1] y, 68.2% male). At week 12 in UNCOVER-1, the ixekizumab Q4W and ixekizumab Q2W groups showed significantly greater improvements in WPAI-PSO scores (least squares mean change from baseline [SE]) relative to placebo: absenteeism (-3.5 [0.87], P < .001; -2.6 [0.84], P = .003, respectively, vs 0.2 [0.88]), presenteeism (-18.8 [1.28], P < .001; -18.3 [1.24], P < .001, vs 0.5 [1.30]), work productivity loss (-20.6 [1.38], P < .001; -19.8 [1.33], P < .001, vs -0.8 [1.40]), and activity impairment (-24.5 [1.18], P < .001; -25.2 [1.15], P < .001, vs 0.8 [1.18]). Similar results were obtained for UNCOVER-2 and UNCOVER-3, with the exception of absenteeism with ixekizumab Q4W in UNCOVER-2. Additionally, ixekizumab-treated patients showed significantly greater improvements in WPAI-PSO scores vs etanercept-treated patients: UNCOVER-2: presenteeism, work productivity loss, activity impairment (P < .001 both doses), UNCOVER-3: activity impairment (ie, regular activities outside of work) (ixekizumab Q2W; P = .009). Improvements in WPAI-PSO scores at week 12 were sustained to at least week 60. Ixekizumab-treated patients reported short- and long-term improvements in work productivity, which could lead to reduced productivity-related cost burden in patients with psoriasis. clinicaltrials.gov Identifiers: NCT01474512, NCT01597245, NCT01646177.

Management of psoriasis patients with hepatitis B or hepatitis C virus infection.

PubMed

Bonifati, Claudio; Lora, Viviana; Graceffa, Dario; Nosotti, Lorenzo

2016-07-28

The systemic therapies available for the management of Psoriasis (PsO) patients who cannot be treated with more conservative options, such as topical agents and/or phototherapy, with the exception of acitretin, can worsen or reactivate a chronic infection. Therefore, before administering immunosuppressive therapies with either conventional disease-modifying drugs (cDMARDs) or biological ones (bDMARDs) it is mandatory to screen patients for some infections, including hepatitis B virus (HBV) and hepatitis C virus (HCV). In particular, the patients eligible to receive an immunosuppressive drug must be screened for the following markers: antibody to hepatitis B core, antibody to hepatitis B surface antigen (anti-HBsAg), HBsAg, and antibody to HCV (anti-HCV). In case HBV or HCV infection is diagnosed, a close collaboration with a consultant hepatologist is needed before and during an immunosuppressive therapy. Concerning therapy with immunosuppressive drugs in PsO patients with HBV or HCV infection, data exist mainly for cyclosporine a (CyA) or bDMARDs (etanercept, adalimumab, infliximab, ustekinumab). The natural history of HBV and HCV infection differs significantly as well as the effect of immunosuppression on the aforementioned infectious diseases. As a rule, in the case of active HBV infection, systemic immunosuppressive antipsoriatic therapies must be deferred until the infection is controlled with an adequate antiviral treatment. Inactive carriers need to receive antiviral prophylaxis 2-4 wk before starting immunosuppressive therapy, to be continued after 6-12 mo from its suspension. Due to the risk of HBV reactivation, these patients should be monitored monthly for the first 3 mo and then every 3 mo for HBV DNA load together with transaminases levels. Concerning the patients who are occult HBV carriers, the risk of HBV reactivation is very low. Therefore, these patients generally do not need antiviral prophylaxis and the sera HBsAg and transaminases dosing can be monitored every 3 mo. Concerning PsO patients with chronic HCV infection their management with immunosuppressive drugs is less problematic as compared to those infected by HBV. In fact, HCV reactivation is an extremely rare event after administration of drugs such as CyA or tumor necrosis factor-α inhibitors. As a rule, these patients can be monitored measuring HCV RNA load, and ALT, aspartate transaminase, gamma-glutamyl-transferase, bilirubin, alkaline phosphatase, albumin and platelet every 3-6 mo. The present article provides an updated overview based on more recently reported data on monitoring and managing PsO patients who need systemic antipsoriatic treatment and have HBV or HCV infection as comorbidity.

Design of static synchronous series compensator based damping controller employing invasive weed optimization algorithm.

PubMed

Ahmed, Ashik; Al-Amin, Rasheduzzaman; Amin, Ruhul

2014-01-01

This paper proposes designing of Static Synchronous Series Compensator (SSSC) based damping controller to enhance the stability of a Single Machine Infinite Bus (SMIB) system by means of Invasive Weed Optimization (IWO) technique. Conventional PI controller is used as the SSSC damping controller which takes rotor speed deviation as the input. The damping controller parameters are tuned based on time integral of absolute error based cost function using IWO. Performance of IWO based controller is compared to that of Particle Swarm Optimization (PSO) based controller. Time domain based simulation results are presented and performance of the controllers under different loading conditions and fault scenarios is studied in order to illustrate the effectiveness of the IWO based design approach.

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

NASA Astrophysics Data System (ADS)

Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

2017-04-01

Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

Power Allocation and Outage Probability Analysis for SDN-based Radio Access Networks

NASA Astrophysics Data System (ADS)

Zhao, Yongxu; Chen, Yueyun; Mai, Zhiyuan

2018-01-01

In this paper, performance of Access network Architecture based SDN (Software Defined Network) is analyzed with respect to the power allocation issue. A power allocation scheme PSO-PA (Particle Swarm Optimization-power allocation) algorithm is proposed, the proposed scheme is subjected to constant total power with the objective of minimizing system outage probability. The entire access network resource configuration is controlled by the SDN controller, then it sends the optimized power distribution factor to the base station source node (SN) and the relay node (RN). Simulation results show that the proposed scheme reduces the system outage probability at a low complexity.

Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM

NASA Astrophysics Data System (ADS)

Sheng, Hanlin; Zhang, Tianhong

2017-08-01

In view of the necessity of highly precise and reliable thrust estimator to achieve direct thrust control of aircraft engine, based on support vector regression (SVR), as well as least square support vector machine (LSSVM) and a new optimization algorithm - gravitational search algorithm (GSA), by performing integrated modelling and parameter optimization, a GSA-LSSVM-based thrust estimator design solution is proposed. The results show that compared to particle swarm optimization (PSO) algorithm, GSA can find unknown optimization parameter better and enables the model developed with better prediction and generalization ability. The model can better predict aircraft engine thrust and thus fulfills the need of direct thrust control of aircraft engine.

TH-EF-BRB-04: 4π Dynamic Conformal Arc Therapy Dynamic Conformal Arc Therapy (DCAT) for SBRT

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

Chiu, T; Long, T; Tian, Z.

2016-06-15

Purpose: To develop an efficient and effective trajectory optimization methodology for 4π dynamic conformal arc treatment (4π DCAT) with synchronized gantry and couch motion; and to investigate potential clinical benefits for stereotactic body radiation therapy (SBRT) to breast, lung, liver and spine tumors. Methods: The entire optimization framework for 4π DCAT inverse planning consists of two parts: 1) integer programming algorithm and 2) particle swarm optimization (PSO) algorithm. The integer programming is designed to find an optimal solution for arc delivery trajectory with both couch and gantry rotation, while PSO minimize a non-convex objective function based on the selected trajectorymore » and dose-volume constraints. In this study, control point interaction is explicitly taken into account. Beam trajectory was modeled as a series of control points connected together to form a deliverable path. With linear treatment planning objectives, a mixed-integer program (MIP) was formulated. Under mild assumptions, the MIP is tractable. Assigning monitor units to control points along the path can be integrated into the model and done by PSO. The developed 4π DCAT inverse planning strategy is evaluated on SBRT cases and compared to clinically treated plans. Results: The resultant dose distribution of this technique was evaluated between 3D conformal treatment plan generated by Pinnacle treatment planning system and 4π DCAT on a lung SBRT patient case. Both plans share the same scale of MU, 3038 and 2822 correspondingly to 3D conformal plan and 4π DCAT. The mean doses for most of OARs were greatly reduced at 32% (cord), 70% (esophagus), 2.8% (lung) and 42.4% (stomach). Conclusion: Initial results in this study show the proposed 4π DCAT treatment technique can achieve better OAR sparing and lower MUs, which indicates that the developed technique is promising for high dose SBRT to reduce the risk of secondary cancer.« less

Toward improved mechanical, tribological, corrosion and in-vitro bioactivity properties of mixed oxide nanotubes on Ti-6Al-7Nb implant using multi-objective PSO.

PubMed

Rafieerad, A R; Bushroa, A R; Nasiri-Tabrizi, B; Kaboli, S H A; Khanahmadi, S; Amiri, Ahmad; Vadivelu, J; Yusof, F; Basirun, W J; Wasa, K

2017-05-01

Recently, the robust optimization and prediction models have been highly noticed in district of surface engineering and coating techniques to obtain the highest possible output values through least trial and error experiments. Besides, due to necessity of finding the optimum value of dependent variables, the multi-objective metaheuristic models have been proposed to optimize various processes. Herein, oriented mixed oxide nanotubular arrays were grown on Ti-6Al-7Nb (Ti67) implant using physical vapor deposition magnetron sputtering (PVDMS) designed by Taguchi and following electrochemical anodization. The obtained adhesion strength and hardness of Ti67/Nb were modeled by particle swarm optimization (PSO) to predict the outputs performance. According to developed models, multi-objective PSO (MOPSO) run aimed at finding PVDMS inputs to maximize current outputs simultaneously. The provided sputtering parameters were applied as validation experiment and resulted in higher adhesion strength and hardness of interfaced layer with Ti67. The as-deposited Nb layer before and after optimization were anodized in fluoride-base electrolyte for 300min. To crystallize the coatings, the anodically grown mixed oxide TiO 2 -Nb 2 O 5 -Al 2 O 3 nanotubes were annealed at 440°C for 30min. From the FESEM observations, the optimized adhesive Nb interlayer led to further homogeneity of mixed nanotube arrays. As a result of this surface modification, the anodized sample after annealing showed the highest mechanical, tribological, corrosion resistant and in-vitro bioactivity properties, where a thick bone-like apatite layer was formed on the mixed oxide nanotubes surface within 10 days immersion in simulated body fluid (SBF) after applied MOPSO. The novel results of this study can be effective in optimizing a variety of the surface properties of the nanostructured implants. Copyright © 2016 Elsevier Ltd. All rights reserved.

Unified Phase Diagram for Iron-Based Superconductors.

PubMed

Gu, Yanhong; Liu, Zhaoyu; Xie, Tao; Zhang, Wenliang; Gong, Dongliang; Hu, Ding; Ma, Xiaoyan; Li, Chunhong; Zhao, Lingxiao; Lin, Lifang; Xu, Zhuang; Tan, Guotai; Chen, Genfu; Meng, Zi Yang; Yang, Yi-Feng; Luo, Huiqian; Li, Shiliang

2017-10-13

High-temperature superconductivity is closely adjacent to a long-range antiferromagnet, which is called a parent compound. In cuprates, all parent compounds are alike and carrier doping leads to superconductivity, so a unified phase diagram can be drawn. However, the properties of parent compounds for iron-based superconductors show significant diversity and both carrier and isovalent dopings can cause superconductivity, which casts doubt on the idea that there exists a unified phase diagram for them. Here we show that the ordered moments in a variety of iron pnictides are inversely proportional to the effective Curie constants of their nematic susceptibility. This unexpected scaling behavior suggests that the magnetic ground states of iron pnictides can be achieved by tuning the strength of nematic fluctuations. Therefore, a unified phase diagram can be established where superconductivity emerges from a hypothetical parent compound with a large ordered moment but weak nematic fluctuations, which suggests that iron-based superconductors are strongly correlated electron systems.

Phase noise suppression for coherent optical block transmission systems: a unified framework.

PubMed

Yang, Chuanchuan; Yang, Feng; Wang, Ziyu

2011-08-29

A unified framework for phase noise suppression is proposed in this paper, which could be applied in any coherent optical block transmission systems, including coherent optical orthogonal frequency-division multiplexing (CO-OFDM), coherent optical single-carrier frequency-domain equalization block transmission (CO-SCFDE), etc. Based on adaptive modeling of phase noise, unified observation equations for different coherent optical block transmission systems are constructed, which lead to unified phase noise estimation and suppression. Numerical results demonstrate that the proposal is powerful in mitigating laser phase noise.

Simultaneous reconstruction of temperature distribution and radiative properties in participating media using a hybrid LSQR-PSO algorithm

NASA Astrophysics Data System (ADS)

Niu, Chun-Yang; Qi, Hong; Huang, Xing; Ruan, Li-Ming; Wang, Wei; Tan, He-Ping

2015-11-01

A hybrid least-square QR decomposition (LSQR)-particle swarm optimization (LSQR-PSO) algorithm was developed to estimate the three-dimensional (3D) temperature distributions and absorption coefficients simultaneously. The outgoing radiative intensities at the boundary surface of the absorbing media were simulated by the line-of-sight (LOS) method, which served as the input for the inverse analysis. The retrieval results showed that the 3D temperature distributions of the participating media with known radiative properties could be retrieved accurately using the LSQR algorithm, even with noisy data. For the participating media with unknown radiative properties, the 3D temperature distributions and absorption coefficients could be retrieved accurately using the LSQR-PSO algorithm even with measurement errors. It was also found that the temperature field could be estimated more accurately than the absorption coefficients. In order to gain insight into the effects on the accuracy of temperature distribution reconstruction, the selection of the detection direction and the angle between two detection directions was also analyzed. Project supported by the Major National Scientific Instruments and Equipment Development Special Foundation of China (Grant No. 51327803), the National Natural Science Foundation of China (Grant No. 51476043), and the Fund of Tianjin Key Laboratory of Civil Aircraft Airworthiness and Maintenance in Civil Aviation University of China.

Cat Swarm Optimization algorithm for optimal linear phase FIR filter design.

PubMed

Saha, Suman Kumar; Ghoshal, Sakti Prasad; Kar, Rajib; Mandal, Durbadal

2013-11-01

In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics. CSO is generated by observing the behaviour of cats and composed of two sub-models. In CSO, one can decide how many cats are used in the iteration. Every cat has its' own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode. The final solution would be the best position of one of the cats. CSO keeps the best solution until it reaches the end of the iteration. The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE). The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems. The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE. The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Particle Swarm Optimization for Programming Deep Brain Stimulation Arrays

PubMed Central

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.

2017-01-01

Objective Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main Results The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. Significance The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts. PMID:28068291

Particle swarm optimization for programming deep brain stimulation arrays

NASA Astrophysics Data System (ADS)

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.

2017-02-01

Objective. Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main results. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n  =  3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of  <1% between approaches. Significance. The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.

The role of plasmalogen in the oxidative stability of neutral lipids and phospholipids.

PubMed

Wang, Guang; Wang, Tong

2010-02-24

The role of ethanolamine plasmalogen extracted from bovine brain (BBEP) in maintaining oxidative stability of bulk soybean oil and liposome made with egg phospholipids (PL) was studied. In a purified soybean oil (PSO), the addition of 200 and 1000 ppm of BBEP promoted lipid oxidation at rates of 0.037 and 0.071 (all rates in ln (PV) h(-1), and PV stands for peroxide value), whereas soy lecithin (SL) added in the same amount showed a trend similar to the PSO blank, which had an oxidation rate of 0.025. The PSO with BBEP was susceptible to cupric ion catalyzed oxidation, in that the oil was oxidized much more quickly than the PSO with SL and cupric ion. In commercial soybean oil (CSO) with the presence of tocopherols, SL at 1000 ppm acted synergistically as an antioxidant with the natural tocopherols, but addition of BBEP accelerated lipid oxidation, as evidenced by the oxidative stability index (OSI) test. In the egg PL liposome, the BBEP caused a fast breakdown of the lipid hydroperoxides and consequently promoted more thiobarbituric acid reactive substance (TBARS) formation. The PL oxidation in the presence of copper in the liposome was not affected by the BBEP, which indicates that the hypothesis of ethanolamine plasmalogen (EthPm) chelating cupric ion as the antioxidation mechanism was not supported. The addition of cumene hydroperoxide to the egg PL liposome promoted lipid oxidation, as indicated by a fast development of PV and TBARS. However, the result with cumene hydroperoxide failed to differentiate the effect of BBEP and SL and their concentration on lipid oxidation. On the basis of the observations from this study, we conclude that EthPm is not an antioxidant but rather a pro-oxidant in a bulk lipid system, and it has no significant antioxidant effect for PL oxidation in the liposome.

Multi-GPU configuration of 4D intensity modulated radiation therapy inverse planning using global optimization

NASA Astrophysics Data System (ADS)

Hagan, Aaron; Sawant, Amit; Folkerts, Michael; Modiri, Arezoo

2018-01-01

We report on the design, implementation and characterization of a multi-graphic processing unit (GPU) computational platform for higher-order optimization in radiotherapy treatment planning. In collaboration with a commercial vendor (Varian Medical Systems, Palo Alto, CA), a research prototype GPU-enabled Eclipse (V13.6) workstation was configured. The hardware consisted of dual 8-core Xeon processors, 256 GB RAM and four NVIDIA Tesla K80 general purpose GPUs. We demonstrate the utility of this platform for large radiotherapy optimization problems through the development and characterization of a parallelized particle swarm optimization (PSO) four dimensional (4D) intensity modulated radiation therapy (IMRT) technique. The PSO engine was coupled to the Eclipse treatment planning system via a vendor-provided scripting interface. Specific challenges addressed in this implementation were (i) data management and (ii) non-uniform memory access (NUMA). For the former, we alternated between parameters over which the computation process was parallelized. For the latter, we reduced the amount of data required to be transferred over the NUMA bridge. The datasets examined in this study were approximately 300 GB in size, including 4D computed tomography images, anatomical structure contours and dose deposition matrices. For evaluation, we created a 4D-IMRT treatment plan for one lung cancer patient and analyzed computation speed while varying several parameters (number of respiratory phases, GPUs, PSO particles, and data matrix sizes). The optimized 4D-IMRT plan enhanced sparing of organs at risk by an average reduction of 26% in maximum dose, compared to the clinical optimized IMRT plan, where the internal target volume was used. We validated our computation time analyses in two additional cases. The computation speed in our implementation did not monotonically increase with the number of GPUs. The optimal number of GPUs (five, in our study) is directly related to the hardware specifications. The optimization process took 35 min using 50 PSO particles, 25 iterations and 5 GPUs.

Multi-GPU configuration of 4D intensity modulated radiation therapy inverse planning using global optimization.

PubMed

Hagan, Aaron; Sawant, Amit; Folkerts, Michael; Modiri, Arezoo

2018-01-16

We report on the design, implementation and characterization of a multi-graphic processing unit (GPU) computational platform for higher-order optimization in radiotherapy treatment planning. In collaboration with a commercial vendor (Varian Medical Systems, Palo Alto, CA), a research prototype GPU-enabled Eclipse (V13.6) workstation was configured. The hardware consisted of dual 8-core Xeon processors, 256 GB RAM and four NVIDIA Tesla K80 general purpose GPUs. We demonstrate the utility of this platform for large radiotherapy optimization problems through the development and characterization of a parallelized particle swarm optimization (PSO) four dimensional (4D) intensity modulated radiation therapy (IMRT) technique. The PSO engine was coupled to the Eclipse treatment planning system via a vendor-provided scripting interface. Specific challenges addressed in this implementation were (i) data management and (ii) non-uniform memory access (NUMA). For the former, we alternated between parameters over which the computation process was parallelized. For the latter, we reduced the amount of data required to be transferred over the NUMA bridge. The datasets examined in this study were approximately 300 GB in size, including 4D computed tomography images, anatomical structure contours and dose deposition matrices. For evaluation, we created a 4D-IMRT treatment plan for one lung cancer patient and analyzed computation speed while varying several parameters (number of respiratory phases, GPUs, PSO particles, and data matrix sizes). The optimized 4D-IMRT plan enhanced sparing of organs at risk by an average reduction of [Formula: see text] in maximum dose, compared to the clinical optimized IMRT plan, where the internal target volume was used. We validated our computation time analyses in two additional cases. The computation speed in our implementation did not monotonically increase with the number of GPUs. The optimal number of GPUs (five, in our study) is directly related to the hardware specifications. The optimization process took 35 min using 50 PSO particles, 25 iterations and 5 GPUs.

Multicountry Prospective Clinical Evaluation of Two Enzyme-Linked Immunosorbent Assays and Two Rapid Diagnostic Tests for Diagnosing Dengue Fever

PubMed Central

Dauner, Allison L.; Valks, Andrea; Forshey, Brett M.; Long, Kanya C.; Thaisomboonsuk, Butsaya; Sierra, Gloria; Picos, Victor; Talmage, Sara; Morrison, Amy C.; Halsey, Eric S.; Comach, Guillermo; Yasuda, Chadwick; Loeffelholz, Michael; Jarman, Richard G.; Fernandez, Stefan; An, Ung Sam; Kochel, Tadeusz J.; Jasper, Louis E.; Wu, Shuenn-Jue L.

2015-01-01

We evaluated four dengue diagnostic devices from Alere, including the SD Bioline Dengue Duo (nonstructural [NS] 1 Ag and IgG/IgM), the Panbio Dengue Duo Cassette (IgM/IgG) rapid diagnostic tests (RDTs), and the Panbio dengue IgM and IgG capture enzyme-linked immunosorbent assays (ELISAs) in a prospective, controlled, multicenter study in Peru, Venezuela, Cambodia, and the United States, using samples from 1,021 febrile individuals. Archived, well-characterized samples from an additional 135 febrile individuals from Thailand were also used. Reference testing was performed on all samples using an algorithm involving virus isolation, in-house IgM and IgG capture ELISAs, and plaque reduction neutralization tests (PRNT) to determine the infection status of the individual. The primary endpoints were the clinical sensitivities and specificities of these devices. The SD Bioline Dengue Duo had an overall sensitivity of 87.3% (95% confidence interval [CI], 84.1 to 90.2%) and specificity of 86.8% (95% CI, 83.9 to 89.3%) during the first 14 days post-symptom onset (p.s.o.). The Panbio Dengue Duo Cassette demonstrated a sensitivity of 92.1% (87.8 to 95.2%) and specificity of 62.2% (54.5 to 69.5%) during days 4 to 14 p.s.o. The Panbio IgM capture ELISA had a sensitivity of 87.6% (82.7 to 91.4%) and specificity of 88.1% (82.2 to 92.6%) during days 4 to 14 p.s.o. Finally, the Panbio IgG capture ELISA had a sensitivity of 69.6% (62.1 to 76.4%) and a specificity of 88.4% (82.6 to 92.8%) during days 4 to 14 p.s.o. for identification of secondary dengue infections. This multicountry prospective study resulted in reliable real-world performance data that will facilitate data-driven laboratory test choices for managing patient care during dengue outbreaks. PMID:25588659

Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints.

PubMed

Kuldeep, B; Singh, V K; Kumar, A; Singh, G K

2015-01-01

In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L2 norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (ϕp), stopband error (ϕs), transition band error (ϕt), peak reconstruction error (PRE), stopband attenuation (As) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

PubMed Central

Huang, Shuqiang; Tao, Ming

2017-01-01

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms. PMID:28117735

A Swarm Optimization approach for clinical knowledge mining.

PubMed

Christopher, J Jabez; Nehemiah, H Khanna; Kannan, A

2015-10-01

Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

PubMed Central

Alexandridis, Alex; Stogiannos, Marios; Papaioannou, Nikolaos; Zois, Elias; Sarimveis, Haralambos

2018-01-01

This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM) algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS) stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC) motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses. PMID:29361781

Comparison of evolutionary algorithms for LPDA antenna optimization

NASA Astrophysics Data System (ADS)

Lazaridis, Pavlos I.; Tziris, Emmanouil N.; Zaharis, Zaharias D.; Xenos, Thomas D.; Cosmas, John P.; Gallion, Philippe B.; Holmes, Violeta; Glover, Ian A.

2016-08-01

A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front-to-rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.

Particle swarm optimization and its application in MEG source localization using single time sliced data

NASA Astrophysics Data System (ADS)

Lin, Juan; Liu, Chenglian; Guo, Yongning

2014-10-01

The estimation of neural active sources from the magnetoencephalography (MEG) data is a very critical issue for both clinical neurology and brain functions research. A widely accepted source-modeling technique for MEG involves calculating a set of equivalent current dipoles (ECDs). Depth in the brain is one of difficulties in MEG source localization. Particle swarm optimization(PSO) is widely used to solve various optimization problems. In this paper we discuss its ability and robustness to find the global optimum in different depths of the brain when using single equivalent current dipole (sECD) model and single time sliced data. The results show that PSO is an effective global optimization to MEG source localization when given one dipole in different depths.

Feature weighting using particle swarm optimization for learning vector quantization classifier

NASA Astrophysics Data System (ADS)

Dongoran, A.; Rahmadani, S.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses and proposes a method of feature weighting in classification assignments on competitive learning artificial neural network LVQ. The weighting feature method is the search for the weight of an attribute using the PSO so as to give effect to the resulting output. This method is then applied to the LVQ-Classifier and tested on the 3 datasets obtained from the UCI Machine Learning repository. Then an accuracy analysis will be generated by two approaches. The first approach using LVQ1, referred to as LVQ-Classifier and the second approach referred to as PSOFW-LVQ, is a proposed model. The result shows that the PSO algorithm is capable of finding attribute weights that increase LVQ-classifier accuracy.

Multi-period project portfolio selection under risk considerations and stochastic income

NASA Astrophysics Data System (ADS)

Tofighian, Ali Asghar; Moezzi, Hamid; Khakzar Barfuei, Morteza; Shafiee, Mahmood

2018-02-01

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably.

Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

PubMed

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.

An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

PubMed Central

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928

An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

PubMed

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

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.

Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization

PubMed Central

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345

Chronic Inflammatory Disease, Lifestyle and Risk of Disease

ClinicalTrials.gov

2018-04-06

Autoimmune Diseases; Inflammatory Bowel Diseases; Crohn Disease (CD); Ulcerative Colitis (UC); Arthritis, Rheumatoid (RA); Spondylarthropathies; Arthritis, Psoriatic (PsA); Psoriasis (PsO); Multiple Sclerosis (MS)

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.

PubMed

Nuryani, Nuryani; Ling, Steve S H; Nguyen, H T

2012-04-01

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

Development and application of unified algorithms for problems in computational science

NASA Technical Reports Server (NTRS)

Shankar, Vijaya; Chakravarthy, Sukumar

1987-01-01

A framework is presented for developing computationally unified numerical algorithms for solving nonlinear equations that arise in modeling various problems in mathematical physics. The concept of computational unification is an attempt to encompass efficient solution procedures for computing various nonlinear phenomena that may occur in a given problem. For example, in Computational Fluid Dynamics (CFD), a unified algorithm will be one that allows for solutions to subsonic (elliptic), transonic (mixed elliptic-hyperbolic), and supersonic (hyperbolic) flows for both steady and unsteady problems. The objectives are: development of superior unified algorithms emphasizing accuracy and efficiency aspects; development of codes based on selected algorithms leading to validation; application of mature codes to realistic problems; and extension/application of CFD-based algorithms to problems in other areas of mathematical physics. The ultimate objective is to achieve integration of multidisciplinary technologies to enhance synergism in the design process through computational simulation. Specific unified algorithms for a hierarchy of gas dynamics equations and their applications to two other areas: electromagnetic scattering, and laser-materials interaction accounting for melting.

Research on logistics scheduling based on PSO

NASA Astrophysics Data System (ADS)

Bao, Huifang; Zhou, Linli; Liu, Lei

2017-08-01

With the rapid development of e-commerce based on the network, the logistics distribution support of e-commerce is becoming more and more obvious. The optimization of vehicle distribution routing can improve the economic benefit and realize the scientific of logistics [1]. Therefore, the study of logistics distribution vehicle routing optimization problem is not only of great theoretical significance, but also of considerable value of value. Particle swarm optimization algorithm is a kind of evolutionary algorithm, which is based on the random solution and the optimal solution by iteration, and the quality of the solution is evaluated through fitness. In order to obtain a more ideal logistics scheduling scheme, this paper proposes a logistics model based on particle swarm optimization algorithm.

Integrative modeling and novel particle swarm-based optimal design of wind farms

NASA Astrophysics Data System (ADS)

Chowdhury, Souma

To meet the energy needs of the future, while seeking to decrease our carbon footprint, a greater penetration of sustainable energy resources such as wind energy is necessary. However, a consistent growth of wind energy (especially in the wake of unfortunate policy changes and reported under-performance of existing projects) calls for a paradigm shift in wind power generation technologies. This dissertation develops a comprehensive methodology to explore, analyze and define the interactions between the key elements of wind farm development, and establish the foundation for designing high-performing wind farms. The primary contribution of this research is the effective quantification of the complex combined influence of wind turbine features, turbine placement, farm-land configuration, nameplate capacity, and wind resource variations on the energy output of the wind farm. A new Particle Swarm Optimization (PSO) algorithm, uniquely capable of preserving population diversity while addressing discrete variables, is also developed to provide powerful solutions towards optimizing wind farm configurations. In conventional wind farm design, the major elements that influence the farm performance are often addressed individually. The failure to fully capture the critical interactions among these factors introduces important inaccuracies in the projected farm performance and leads to suboptimal wind farm planning. In this dissertation, we develop the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology to model and optimize the performance of wind farms. The UWFLO method obviates traditional assumptions regarding (i) turbine placement, (ii) turbine-wind flow interactions, (iii) variation of wind conditions, and (iv) types of turbines (single/multiple) to be installed. The allowance of multiple turbines, which demands complex modeling, is rare in the existing literature. The UWFLO method also significantly advances the state of the art in wind farm optimization by allowing simultaneous optimization of the type and the location of the turbines. Layout optimization (using UWFLO) of a hypothetical 25-turbine commercial-scale wind farm provides a remarkable 4.4% increase in capacity factor compared to a conventional array layout. A further 2% increase in capacity factor is accomplished when the types of turbines are also optimally selected. The scope of turbine selection and placement however depends on the land configuration and the nameplate capacity of the farm. Such dependencies are not clearly defined in the existing literature. We develop response surface-based models, which implicitly employ UWFLO, to quantify and analyze the roles of these other crucial design factors in optimal wind farm planning. The wind pattern at a site can vary significantly from year to year, which is not adequately captured by conventional wind distribution models. The resulting ill-predictability of the annual distribution of wind conditions introduces significant uncertainties in the estimated energy output of the wind farm. A new method is developed to characterize these wind resource uncertainties and model the propagation of these uncertainties into the estimated farm output. The overall wind pattern/regime also varies from one region to another, which demands turbines with capabilities uniquely suited for different wind regimes. Using the UWFLO method, we model the performance potential of currently available turbines for different wind regimes, and quantify their feature-based expected market suitability. Such models can initiate an understanding of the product variation that current turbine manufacturers should pursue, to adequately satisfy the needs of the naturally diverse wind energy market. The wind farm design problems formulated in this dissertation involve highly multimodal objective and constraint functions and a large number of continuous and discrete variables. An effective modification of the PSO algorithm is developed to address such challenging problems. Continuous search, as in conventional PSO, is implemented as the primary search strategy; discrete variables are then updated using a nearest-allowed-discrete-point criterion. Premature stagnation of particles due to loss of population diversity is one of the primary drawbacks of the basic PSO dynamics. A new measure of population diversity is formulated, which unlike existing metrics capture both the overall spread and the distribution of particles in the variable space. This diversity metric is then used to apply (i) an adaptive repulsion away from the best global solution in the case of continuous variables, and (ii) a stochastic update of the discrete variables. The new PSO algorithm provides competitive performance compared to a popular genetic algorithm, when applied to solve a comprehensive set of 98 mixed-integer nonlinear programming problems.

Meta-heuristic CRPS minimization for the calibration of short-range probabilistic forecasts

NASA Astrophysics Data System (ADS)

Mohammadi, Seyedeh Atefeh; Rahmani, Morteza; Azadi, Majid

2016-08-01

This paper deals with the probabilistic short-range temperature forecasts over synoptic meteorological stations across Iran using non-homogeneous Gaussian regression (NGR). NGR creates a Gaussian forecast probability density function (PDF) from the ensemble output. The mean of the normal predictive PDF is a bias-corrected weighted average of the ensemble members and its variance is a linear function of the raw ensemble variance. The coefficients for the mean and variance are estimated by minimizing the continuous ranked probability score (CRPS) during a training period. CRPS is a scoring rule for distributional forecasts. In the paper of Gneiting et al. (Mon Weather Rev 133:1098-1118, 2005), Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used to minimize the CRPS. Since BFGS is a conventional optimization method with its own limitations, we suggest using the particle swarm optimization (PSO), a robust meta-heuristic method, to minimize the CRPS. The ensemble prediction system used in this study consists of nine different configurations of the weather research and forecasting model for 48-h forecasts of temperature during autumn and winter 2011 and 2012. The probabilistic forecasts were evaluated using several common verification scores including Brier score, attribute diagram and rank histogram. Results show that both BFGS and PSO find the optimal solution and show the same evaluation scores, but PSO can do this with a feasible random first guess and much less computational complexity.

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

Determining residual reduction algorithm kinematic tracking weights for a sidestep cut via numerical optimization.

PubMed

Samaan, Michael A; Weinhandl, Joshua T; Bawab, Sebastian Y; Ringleb, Stacie I

2016-12-01

Musculoskeletal modeling allows for the determination of various parameters during dynamic maneuvers by using in vivo kinematic and ground reaction force (GRF) data as inputs. Differences between experimental and model marker data and inconsistencies in the GRFs applied to these musculoskeletal models may not produce accurate simulations. Therefore, residual forces and moments are applied to these models in order to reduce these differences. Numerical optimization techniques can be used to determine optimal tracking weights of each degree of freedom of a musculoskeletal model in order to reduce differences between the experimental and model marker data as well as residual forces and moments. In this study, the particle swarm optimization (PSO) and simplex simulated annealing (SIMPSA) algorithms were used to determine optimal tracking weights for the simulation of a sidestep cut. The PSO and SIMPSA algorithms were able to produce model kinematics that were within 1.4° of experimental kinematics with residual forces and moments of less than 10 N and 18 Nm, respectively. The PSO algorithm was able to replicate the experimental kinematic data more closely and produce more dynamically consistent kinematic data for a sidestep cut compared to the SIMPSA algorithm. Future studies should use external optimization routines to determine dynamically consistent kinematic data and report the differences between experimental and model data for these musculoskeletal simulations.