Sample records for algorithm differential evolution

  1. Solving SAT Problem Based on Hybrid Differential Evolution Algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Kunqi; Zhang, Jingmin; Liu, Gang; Kang, Lishan

    Satisfiability (SAT) problem is an NP-complete problem. Based on the analysis about it, SAT problem is translated equally into an optimization problem on the minimum of objective function. A hybrid differential evolution algorithm is proposed to solve the Satisfiability problem. It makes full use of strong local search capacity of hill-climbing algorithm and strong global search capability of differential evolution algorithm, which makes up their disadvantages, improves the efficiency of algorithm and avoids the stagnation phenomenon. The experiment results show that the hybrid algorithm is efficient in solving SAT problem.

  2. Shape Optimization of Rubber Bushing Using Differential Evolution Algorithm

    PubMed Central

    2014-01-01

    The objective of this study is to design rubber bushing at desired level of stiffness characteristics in order to achieve the ride quality of the vehicle. A differential evolution algorithm based approach is developed to optimize the rubber bushing through integrating a finite element code running in batch mode to compute the objective function values for each generation. Two case studies were given to illustrate the application of proposed approach. Optimum shape parameters of 2D bushing model were determined by shape optimization using differential evolution algorithm. PMID:25276848

  3. Cloud computing task scheduling strategy based on improved differential evolution algorithm

    NASA Astrophysics Data System (ADS)

    Ge, Junwei; He, Qian; Fang, Yiqiu

    2017-04-01

    In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

  4. Differential Evolution algorithm applied to FSW model calibration

    NASA Astrophysics Data System (ADS)

    Idagawa, H. S.; Santos, T. F. A.; Ramirez, A. J.

    2014-03-01

    Friction Stir Welding (FSW) is a solid state welding process that can be modelled using a Computational Fluid Dynamics (CFD) approach. These models use adjustable parameters to control the heat transfer and the heat input to the weld. These parameters are used to calibrate the model and they are generally determined using the conventional trial and error approach. Since this method is not very efficient, we used the Differential Evolution (DE) algorithm to successfully determine these parameters. In order to improve the success rate and to reduce the computational cost of the method, this work studied different characteristics of the DE algorithm, such as the evolution strategy, the objective function, the mutation scaling factor and the crossover rate. The DE algorithm was tested using a friction stir weld performed on a UNS S32205 Duplex Stainless Steel.

  5. Parameter optimization of differential evolution algorithm for automatic playlist generation problem

    NASA Astrophysics Data System (ADS)

    Alamag, Kaye Melina Natividad B.; Addawe, Joel M.

    2017-11-01

    With the digitalization of music, the number of collection of music increased largely and there is a need to create lists of music that filter the collection according to user preferences, thus giving rise to the Automatic Playlist Generation Problem (APGP). Previous attempts to solve this problem include the use of search and optimization algorithms. If a music database is very large, the algorithm to be used must be able to search the lists thoroughly taking into account the quality of the playlist given a set of user constraints. In this paper we perform an evolutionary meta-heuristic optimization algorithm, Differential Evolution (DE) using different combination of parameter values and select the best performing set when used to solve four standard test functions. Performance of the proposed algorithm is then compared with normal Genetic Algorithm (GA) and a hybrid GA with Tabu Search. Numerical simulations are carried out to show better results from Differential Evolution approach with the optimized parameter values.

  6. Application of differential evolution algorithm on self-potential data.

    PubMed

    Li, Xiangtao; Yin, Minghao

    2012-01-01

    Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods.

  7. Application of Differential Evolution Algorithm on Self-Potential Data

    PubMed Central

    Li, Xiangtao; Yin, Minghao

    2012-01-01

    Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods. PMID:23240004

  8. Pulse retrieval algorithm for interferometric frequency-resolved optical gating based on differential evolution.

    PubMed

    Hyyti, Janne; Escoto, Esmerando; Steinmeyer, Günter

    2017-10-01

    A novel algorithm for the ultrashort laser pulse characterization method of interferometric frequency-resolved optical gating (iFROG) is presented. Based on a genetic method, namely, differential evolution, the algorithm can exploit all available information of an iFROG measurement to retrieve the complex electric field of a pulse. The retrieval is subjected to a series of numerical tests to prove the robustness of the algorithm against experimental artifacts and noise. These tests show that the integrated error-correction mechanisms of the iFROG method can be successfully used to remove the effect from timing errors and spectrally varying efficiency in the detection. Moreover, the accuracy and noise resilience of the new algorithm are shown to outperform retrieval based on the generalized projections algorithm, which is widely used as the standard method in FROG retrieval. The differential evolution algorithm is further validated with experimental data, measured with unamplified three-cycle pulses from a mode-locked Ti:sapphire laser. Additionally introducing group delay dispersion in the beam path, the retrieval results show excellent agreement with independent measurements with a commercial pulse measurement device based on spectral phase interferometry for direct electric-field retrieval. Further experimental tests with strongly attenuated pulses indicate resilience of differential-evolution-based retrieval against massive measurement noise.

  9. A Self Adaptive Differential Evolution Algorithm for Global Optimization

    NASA Astrophysics Data System (ADS)

    Kumar, Pravesh; Pant, Millie

    This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

  10. An optimized digital watermarking algorithm in wavelet domain based on differential evolution for color image.

    PubMed

    Cui, Xinchun; Niu, Yuying; Zheng, Xiangwei; Han, Yingshuai

    2018-01-01

    In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness.

  11. An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies.

    PubMed

    Xiang, Wan-li; Meng, Xue-lei; An, Mei-qing; Li, Yin-zhen; Gao, Ming-xia

    2015-01-01

    Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

  12. A multi-populations multi-strategies differential evolution algorithm for structural optimization of metal nanoclusters

    NASA Astrophysics Data System (ADS)

    Fan, Tian-E.; Shao, Gui-Fang; Ji, Qing-Shuang; Zheng, Ji-Wen; Liu, Tun-dong; Wen, Yu-Hua

    2016-11-01

    Theoretically, the determination of the structure of a cluster is to search the global minimum on its potential energy surface. The global minimization problem is often nondeterministic-polynomial-time (NP) hard and the number of local minima grows exponentially with the cluster size. In this article, a multi-populations multi-strategies differential evolution algorithm has been proposed to search the globally stable structure of Fe and Cr nanoclusters. The algorithm combines a multi-populations differential evolution with an elite pool scheme to keep the diversity of the solutions and avoid prematurely trapping into local optima. Moreover, multi-strategies such as growing method in initialization and three differential strategies in mutation are introduced to improve the convergence speed and lower the computational cost. The accuracy and effectiveness of our algorithm have been verified by comparing the results of Fe clusters with Cambridge Cluster Database. Meanwhile, the performance of our algorithm has been analyzed by comparing the convergence rate and energy evaluations with the classical DE algorithm. The multi-populations, multi-strategies mutation and growing method in initialization in our algorithm have been considered respectively. Furthermore, the structural growth pattern of Cr clusters has been predicted by this algorithm. The results show that the lowest-energy structure of Cr clusters contains many icosahedra, and the number of the icosahedral rings rises with increasing size.

  13. An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

    PubMed

    Liang, Ying; Liao, Bo; Zhu, Wen

    2017-01-01

    Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

  14. Multiobjective Optimization Using a Pareto Differential Evolution Approach

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

    2002-01-01

    Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.

  15. Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN

    PubMed Central

    2017-01-01

    Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms. PMID:28761438

  16. An implementation of differential evolution algorithm for inversion of geoelectrical data

    NASA Astrophysics Data System (ADS)

    Balkaya, Çağlayan

    2013-11-01

    Differential evolution (DE), a population-based evolutionary algorithm (EA) has been implemented to invert self-potential (SP) and vertical electrical sounding (VES) data sets. The algorithm uses three operators including mutation, crossover and selection similar to genetic algorithm (GA). Mutation is the most important operator for the success of DE. Three commonly used mutation strategies including DE/best/1 (strategy 1), DE/rand/1 (strategy 2) and DE/rand-to-best/1 (strategy 3) were applied together with a binomial type crossover. Evolution cycle of DE was realized without boundary constraints. For the test studies performed with SP data, in addition to both noise-free and noisy synthetic data sets two field data sets observed over the sulfide ore body in the Malachite mine (Colorado) and over the ore bodies in the Neem-Ka Thana cooper belt (India) were considered. VES test studies were carried out using synthetically produced resistivity data representing a three-layered earth model and a field data set example from Gökçeada (Turkey), which displays a seawater infiltration problem. Mutation strategies mentioned above were also extensively tested on both synthetic and field data sets in consideration. Of these, strategy 1 was found to be the most effective strategy for the parameter estimation by providing less computational cost together with a good accuracy. The solutions obtained by DE for the synthetic cases of SP were quite consistent with particle swarm optimization (PSO) which is a more widely used population-based optimization algorithm than DE in geophysics. Estimated parameters of SP and VES data were also compared with those obtained from Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing (SA) without cooling to clarify uncertainties in the solutions. Comparison to the M-H algorithm shows that DE performs a fast approximate posterior sampling for the case of low-dimensional inverse geophysical problems.

  17. Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

    PubMed Central

    Kok, Kai Yit; Rajendran, Parvathy

    2016-01-01

    The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. PMID:26943630

  18. A Differential Evolution-Based Routing Algorithm for Environmental Monitoring Wireless Sensor Networks

    PubMed Central

    Li, Xiaofang; Xu, Lizhong; Wang, Huibin; Song, Jie; Yang, Simon X.

    2010-01-01

    The traditional Low Energy Adaptive Cluster Hierarchy (LEACH) routing protocol is a clustering-based protocol. The uneven selection of cluster heads results in premature death of cluster heads and premature blind nodes inside the clusters, thus reducing the overall lifetime of the network. With a full consideration of information on energy and distance distribution of neighboring nodes inside the clusters, this paper proposes a new routing algorithm based on differential evolution (DE) to improve the LEACH routing protocol. To meet the requirements of monitoring applications in outdoor environments such as the meteorological, hydrological and wetland ecological environments, the proposed algorithm uses the simple and fast search features of DE to optimize the multi-objective selection of cluster heads and prevent blind nodes for improved energy efficiency and system stability. Simulation results show that the proposed new LEACH routing algorithm has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless sensor networks. PMID:22219670

  19. Estimating Traffic Accidents in Turkey Using Differential Evolution Algorithm

    NASA Astrophysics Data System (ADS)

    Akgüngör, Ali Payıdar; Korkmaz, Ersin

    2017-06-01

    Estimating traffic accidents play a vital role to apply road safety procedures. This study proposes Differential Evolution Algorithm (DEA) models to estimate the number of accidents in Turkey. In the model development, population (P) and the number of vehicles (N) are selected as model parameters. Three model forms, linear, exponential and semi-quadratic models, are developed using DEA with the data covering from 2000 to 2014. Developed models are statistically compared to select the best fit model. The results of the DE models show that the linear model form is suitable to estimate the number of accidents. The statistics of this form is better than other forms in terms of performance criteria which are the Mean Absolute Percentage Errors (MAPE) and the Root Mean Square Errors (RMSE). To investigate the performance of linear DE model for future estimations, a ten-year period from 2015 to 2024 is considered. The results obtained from future estimations reveal the suitability of DE method for road safety applications.

  20. CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution

    NASA Astrophysics Data System (ADS)

    Mallick, S.; Kar, R.; Mandal, D.; Ghoshal, S. P.

    2016-07-01

    This paper proposes a novel hybrid optimisation algorithm which combines the recently proposed evolutionary algorithm Backtracking Search Algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE). The proposed algorithm called BSA-DE is employed for the optimal designs of two commonly used analogue circuits, namely Complementary Metal Oxide Semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier (op-amp) circuit. BSA has a simple structure that is effective, fast and capable of solving multimodal problems. DE is a stochastic, population-based heuristic approach, having the capability to solve global optimisation problems. In this paper, the transistors' sizes are optimised using the proposed BSA-DE to minimise the areas occupied by the circuits and to improve the performances of the circuits. The simulation results justify the superiority of BSA-DE in global convergence properties and fine tuning ability, and prove it to be a promising candidate for the optimal design of the analogue CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the effectiveness of the proposed BSA-DE-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters of the optimal design of the analogue CMOS amplifier circuits. It is shown that BSA-DE-based design technique for each amplifier circuit yields the least MOS transistor area, and each designed circuit is shown to have the best performance parameters such as gain, power dissipation, etc., as compared with those of other recently reported literature.

  1. A Differential Evolution Algorithm Based on Nikaido-Isoda Function for Solving Nash Equilibrium in Nonlinear Continuous Games

    PubMed Central

    He, Feng; Zhang, Wei; Zhang, Guoqiang

    2016-01-01

    A differential evolution algorithm for solving Nash equilibrium in nonlinear continuous games is presented in this paper, called NIDE (Nikaido-Isoda differential evolution). At each generation, parent and child strategy profiles are compared one by one pairwisely, adapting Nikaido-Isoda function as fitness function. In practice, the NE of nonlinear game model with cubic cost function and quadratic demand function is solved, and this method could also be applied to non-concave payoff functions. Moreover, the NIDE is compared with the existing Nash Domination Evolutionary Multiplayer Optimization (NDEMO), the result showed that NIDE was significantly better than NDEMO with less iterations and shorter running time. These numerical examples suggested that the NIDE method is potentially useful. PMID:27589229

  2. An interactive approach based on a discrete differential evolution algorithm for a class of integer bilevel programming problems

    NASA Astrophysics Data System (ADS)

    Li, Hong; Zhang, Li; Jiao, Yong-Chang

    2016-07-01

    This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn-Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.

  3. Automatic Clustering Using FSDE-Forced Strategy Differential Evolution

    NASA Astrophysics Data System (ADS)

    Yasid, A.

    2018-01-01

    Clustering analysis is important in datamining for unsupervised data, cause no adequate prior knowledge. One of the important tasks is defining the number of clusters without user involvement that is known as automatic clustering. This study intends on acquiring cluster number automatically utilizing forced strategy differential evolution (AC-FSDE). Two mutation parameters, namely: constant parameter and variable parameter are employed to boost differential evolution performance. Four well-known benchmark datasets were used to evaluate the algorithm. Moreover, the result is compared with other state of the art automatic clustering methods. The experiment results evidence that AC-FSDE is better or competitive with other existing automatic clustering algorithm.

  4. Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease.

    PubMed

    Vivekanandan, T; Sriman Narayana Iyengar, N Ch

    2017-11-01

    Enormous data growth in multiple domains has posed a great challenge for data processing and analysis techniques. In particular, the traditional record maintenance strategy has been replaced in the healthcare system. It is vital to develop a model that is able to handle the huge amount of e-healthcare data efficiently. In this paper, the challenging tasks of selecting critical features from the enormous set of available features and diagnosing heart disease are carried out. Feature selection is one of the most widely used pre-processing steps in classification problems. A modified differential evolution (DE) algorithm is used to perform feature selection for cardiovascular disease and optimization of selected features. Of the 10 available strategies for the traditional DE algorithm, the seventh strategy, which is represented by DE/rand/2/exp, is considered for comparative study. The performance analysis of the developed modified DE strategy is given in this paper. With the selected critical features, prediction of heart disease is carried out using fuzzy AHP and a feed-forward neural network. Various performance measures of integrating the modified differential evolution algorithm with fuzzy AHP and a feed-forward neural network in the prediction of heart disease are evaluated in this paper. The accuracy of the proposed hybrid model is 83%, which is higher than that of some other existing models. In addition, the prediction time of the proposed hybrid model is also evaluated and has shown promising results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. A Novel Discrete Differential Evolution Algorithm for the Vehicle Routing Problem in B2C E-Commerce

    NASA Astrophysics Data System (ADS)

    Xia, Chao; Sheng, Ying; Jiang, Zhong-Zhong; Tan, Chunqiao; Huang, Min; He, Yuanjian

    2015-12-01

    In this paper, a novel discrete differential evolution (DDE) algorithm is proposed to solve the vehicle routing problems (VRP) in B2C e-commerce, in which VRP is modeled by the incomplete graph based on the actual urban road system. First, a variant of classical VRP is described and a mathematical programming model for the variant is given. Second, the DDE is presented, where individuals are represented as the sequential encoding scheme, and a novel reparation operator is employed to repair the infeasible solutions. Furthermore, a FLOYD operator for dealing with the shortest route is embedded in the proposed DDE. Finally, an extensive computational study is carried out in comparison with the predatory search algorithm and genetic algorithm, and the results show that the proposed DDE is an effective algorithm for VRP in B2C e-commerce.

  6. Turbomachinery Airfoil Design Optimization Using Differential Evolution

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

    2002-01-01

    An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.

  7. Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

    NASA Astrophysics Data System (ADS)

    Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

    2015-10-01

    The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

  8. A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking

    NASA Astrophysics Data System (ADS)

    Han, Yu-Yan; Gong, Dunwei; Sun, Xiaoyan

    2015-07-01

    A flow-shop scheduling problem with blocking has important applications in a variety of industrial systems but is underrepresented in the research literature. In this study, a novel discrete artificial bee colony (ABC) algorithm is presented to solve the above scheduling problem with a makespan criterion by incorporating the ABC with differential evolution (DE). The proposed algorithm (DE-ABC) contains three key operators. One is related to the employed bee operator (i.e. adopting mutation and crossover operators of discrete DE to generate solutions with good quality); the second is concerned with the onlooker bee operator, which modifies the selected solutions using insert or swap operators based on the self-adaptive strategy; and the last is for the local search, that is, the insert-neighbourhood-based local search with a small probability is adopted to improve the algorithm's capability in exploitation. The performance of the proposed DE-ABC algorithm is empirically evaluated by applying it to well-known benchmark problems. The experimental results show that the proposed algorithm is superior to the compared algorithms in minimizing the makespan criterion.

  9. Differential evolution-simulated annealing for multiple sequence alignment

    NASA Astrophysics Data System (ADS)

    Addawe, R. C.; Addawe, J. M.; Sueño, M. R. K.; Magadia, J. C.

    2017-10-01

    Multiple sequence alignments (MSA) are used in the analysis of molecular evolution and sequence structure relationships. In this paper, a hybrid algorithm, Differential Evolution - Simulated Annealing (DESA) is applied in optimizing multiple sequence alignments (MSAs) based on structural information, non-gaps percentage and totally conserved columns. DESA is a robust algorithm characterized by self-organization, mutation, crossover, and SA-like selection scheme of the strategy parameters. Here, the MSA problem is treated as a multi-objective optimization problem of the hybrid evolutionary algorithm, DESA. Thus, we name the algorithm as DESA-MSA. Simulated sequences and alignments were generated to evaluate the accuracy and efficiency of DESA-MSA using different indel sizes, sequence lengths, deletion rates and insertion rates. The proposed hybrid algorithm obtained acceptable solutions particularly for the MSA problem evaluated based on the three objectives.

  10. Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

    PubMed Central

    Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

    2014-01-01

    Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately. PMID:24936949

  11. A combined NLP-differential evolution algorithm approach for the optimization of looped water distribution systems

    NASA Astrophysics Data System (ADS)

    Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

    2011-08-01

    This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques.

  12. Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

    NASA Astrophysics Data System (ADS)

    Guo, Zhan; Yan, Xuefeng

    2018-04-01

    Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.

  13. A new design approach based on differential evolution algorithm for geometric optimization of magnetorheological brakes

    NASA Astrophysics Data System (ADS)

    Le-Duc, Thang; Ho-Huu, Vinh; Nguyen-Thoi, Trung; Nguyen-Quoc, Hung

    2016-12-01

    In recent years, various types of magnetorheological brakes (MRBs) have been proposed and optimized by different optimization algorithms that are integrated in commercial software such as ANSYS and Comsol Multiphysics. However, many of these optimization algorithms often possess some noteworthy shortcomings such as the trap of solutions at local extremes, or the limited number of design variables or the difficulty of dealing with discrete design variables. Thus, to overcome these limitations and develop an efficient computation tool for optimal design of the MRBs, an optimization procedure that combines differential evolution (DE), a gradient-free global optimization method with finite element analysis (FEA) is proposed in this paper. The proposed approach is then applied to the optimal design of MRBs with different configurations including conventional MRBs and MRBs with coils placed on the side housings. Moreover, to approach a real-life design, some necessary design variables of MRBs are considered as discrete variables in the optimization process. The obtained optimal design results are compared with those of available optimal designs in the literature. The results reveal that the proposed method outperforms some traditional approaches.

  14. Amplitude inversion of the 2D analytic signal of magnetic anomalies through the differential evolution algorithm

    NASA Astrophysics Data System (ADS)

    Ekinci, Yunus Levent; Özyalın, Şenol; Sındırgı, Petek; Balkaya, Çağlayan; Göktürkler, Gökhan

    2017-12-01

    In this work, analytic signal amplitude (ASA) inversion of total field magnetic anomalies has been achieved by differential evolution (DE) which is a population-based evolutionary metaheuristic algorithm. Using an elitist strategy, the applicability and effectiveness of the proposed inversion algorithm have been evaluated through the anomalies due to both hypothetical model bodies and real isolated geological structures. Some parameter tuning studies relying mainly on choosing the optimum control parameters of the algorithm have also been performed to enhance the performance of the proposed metaheuristic. Since ASAs of magnetic anomalies are independent of both ambient field direction and the direction of magnetization of the causative sources in a two-dimensional (2D) case, inversions of synthetic noise-free and noisy single model anomalies have produced satisfactory solutions showing the practical applicability of the algorithm. Moreover, hypothetical studies using multiple model bodies have clearly showed that the DE algorithm is able to cope with complicated anomalies and some interferences from neighbouring sources. The proposed algorithm has then been used to invert small- (120 m) and large-scale (40 km) magnetic profile anomalies of an iron deposit (Kesikköprü-Bala, Turkey) and a deep-seated magnetized structure (Sea of Marmara, Turkey), respectively to determine depths, geometries and exact origins of the source bodies. Inversion studies have yielded geologically reasonable solutions which are also in good accordance with the results of normalized full gradient and Euler deconvolution techniques. Thus, we propose the use of DE not only for the amplitude inversion of 2D analytical signals of magnetic profile anomalies having induced or remanent magnetization effects but also the low-dimensional data inversions in geophysics. A part of this paper was presented as an abstract at the 2nd International Conference on Civil and Environmental Engineering, 8

  15. Differential evolution algorithm based photonic structure design: numerical and experimental verification of subwavelength λ/5 focusing of light.

    PubMed

    Bor, E; Turduev, M; Kurt, H

    2016-08-01

    Photonic structure designs based on optimization algorithms provide superior properties compared to those using intuition-based approaches. In the present study, we numerically and experimentally demonstrate subwavelength focusing of light using wavelength scale absorption-free dielectric scattering objects embedded in an air background. An optimization algorithm based on differential evolution integrated into the finite-difference time-domain method was applied to determine the locations of each circular dielectric object with a constant radius and refractive index. The multiobjective cost function defined inside the algorithm ensures strong focusing of light with low intensity side lobes. The temporal and spectral responses of the designed compact photonic structure provided a beam spot size in air with a full width at half maximum value of 0.19λ, where λ is the wavelength of light. The experiments were carried out in the microwave region to verify numerical findings, and very good agreement between the two approaches was found. The subwavelength light focusing is associated with a strong interference effect due to nonuniformly arranged scatterers and an irregular index gradient. Improving the focusing capability of optical elements by surpassing the diffraction limit of light is of paramount importance in optical imaging, lithography, data storage, and strong light-matter interaction.

  16. Differential evolution algorithm based photonic structure design: numerical and experimental verification of subwavelength λ/5 focusing of light

    PubMed Central

    Bor, E.; Turduev, M.; Kurt, H.

    2016-01-01

    Photonic structure designs based on optimization algorithms provide superior properties compared to those using intuition-based approaches. In the present study, we numerically and experimentally demonstrate subwavelength focusing of light using wavelength scale absorption-free dielectric scattering objects embedded in an air background. An optimization algorithm based on differential evolution integrated into the finite-difference time-domain method was applied to determine the locations of each circular dielectric object with a constant radius and refractive index. The multiobjective cost function defined inside the algorithm ensures strong focusing of light with low intensity side lobes. The temporal and spectral responses of the designed compact photonic structure provided a beam spot size in air with a full width at half maximum value of 0.19λ, where λ is the wavelength of light. The experiments were carried out in the microwave region to verify numerical findings, and very good agreement between the two approaches was found. The subwavelength light focusing is associated with a strong interference effect due to nonuniformly arranged scatterers and an irregular index gradient. Improving the focusing capability of optical elements by surpassing the diffraction limit of light is of paramount importance in optical imaging, lithography, data storage, and strong light-matter interaction. PMID:27477060

  17. Performance Review of Harmony Search, Differential Evolution and Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Mohan Pandey, Hari

    2017-08-01

    Metaheuristic algorithms are effective in the design of an intelligent system. These algorithms are widely applied to solve complex optimization problems, including image processing, big data analytics, language processing, pattern recognition and others. This paper presents a performance comparison of three meta-heuristic algorithms, namely Harmony Search, Differential Evolution, and Particle Swarm Optimization. These algorithms are originated altogether from different fields of meta-heuristics yet share a common objective. The standard benchmark functions are used for the simulation. Statistical tests are conducted to derive a conclusion on the performance. The key motivation to conduct this research is to categorize the computational capabilities, which might be useful to the researchers.

  18. Multi Sensor Fusion Using Fitness Adaptive Differential Evolution

    NASA Astrophysics Data System (ADS)

    Giri, Ritwik; Ghosh, Arnob; Chowdhury, Aritra; Das, Swagatam

    The rising popularity of multi-source, multi-sensor networks supports real-life applications calls for an efficient and intelligent approach to information fusion. Traditional optimization techniques often fail to meet the demands. The evolutionary approach provides a valuable alternative due to its inherent parallel nature and its ability to deal with difficult problems. We present a new evolutionary approach based on a modified version of Differential Evolution (DE), called Fitness Adaptive Differential Evolution (FiADE). FiADE treats sensors in the network as distributed intelligent agents with various degrees of autonomy. Existing approaches based on intelligent agents cannot completely answer the question of how their agents could coordinate their decisions in a complex environment. The proposed approach is formulated to produce good result for the problems that are high-dimensional, highly nonlinear, and random. The proposed approach gives better result in case of optimal allocation of sensors. The performance of the proposed approach is compared with an evolutionary algorithm coordination generalized particle model (C-GPM).

  19. Model parameter estimations from residual gravity anomalies due to simple-shaped sources using Differential Evolution Algorithm

    NASA Astrophysics Data System (ADS)

    Ekinci, Yunus Levent; Balkaya, Çağlayan; Göktürkler, Gökhan; Turan, Seçil

    2016-06-01

    An efficient approach to estimate model parameters from residual gravity data based on differential evolution (DE), a stochastic vector-based metaheuristic algorithm, has been presented. We have showed the applicability and effectiveness of this algorithm on both synthetic and field anomalies. According to our knowledge, this is a first attempt of applying DE for the parameter estimations of residual gravity anomalies due to isolated causative sources embedded in the subsurface. The model parameters dealt with here are the amplitude coefficient (A), the depth and exact origin of causative source (zo and xo, respectively) and the shape factors (q and ƞ). The error energy maps generated for some parameter pairs have successfully revealed the nature of the parameter estimation problem under consideration. Noise-free and noisy synthetic single gravity anomalies have been evaluated with success via DE/best/1/bin, which is a widely used strategy in DE. Additionally some complicated gravity anomalies caused by multiple source bodies have been considered, and the results obtained have showed the efficiency of the algorithm. Then using the strategy applied in synthetic examples some field anomalies observed for various mineral explorations such as a chromite deposit (Camaguey district, Cuba), a manganese deposit (Nagpur, India) and a base metal sulphide deposit (Quebec, Canada) have been considered to estimate the model parameters of the ore bodies. Applications have exhibited that the obtained results such as the depths and shapes of the ore bodies are quite consistent with those published in the literature. Uncertainty in the solutions obtained from DE algorithm has been also investigated by Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing without cooling schedule. Based on the resulting histogram reconstructions of both synthetic and field data examples the algorithm has provided reliable parameter estimations being within the sampling limits of

  20. The Ground Flash Fraction Retrieval Algorithm Employing Differential Evolution: Simulations and Applications

    NASA Technical Reports Server (NTRS)

    Koshak, William; Solakiewicz, Richard

    2012-01-01

    The ability to estimate the fraction of ground flashes in a set of flashes observed by a satellite lightning imager, such as the future GOES-R Geostationary Lightning Mapper (GLM), would likely improve operational and scientific applications (e.g., severe weather warnings, lightning nitrogen oxides studies, and global electric circuit analyses). A Bayesian inversion method, called the Ground Flash Fraction Retrieval Algorithm (GoFFRA), was recently developed for estimating the ground flash fraction. The method uses a constrained mixed exponential distribution model to describe a particular lightning optical measurement called the Maximum Group Area (MGA). To obtain the optimum model parameters (one of which is the desired ground flash fraction), a scalar function must be minimized. This minimization is difficult because of two problems: (1) Label Switching (LS), and (2) Parameter Identity Theft (PIT). The LS problem is well known in the literature on mixed exponential distributions, and the PIT problem was discovered in this study. Each problem occurs when one allows the numerical minimizer to freely roam through the parameter search space; this allows certain solution parameters to interchange roles which leads to fundamental ambiguities, and solution error. A major accomplishment of this study is that we have employed a state-of-the-art genetic-based global optimization algorithm called Differential Evolution (DE) that constrains the parameter search in such a way as to remove both the LS and PIT problems. To test the performance of the GoFFRA when DE is employed, we applied it to analyze simulated MGA datasets that we generated from known mixed exponential distributions. Moreover, we evaluated the GoFFRA/DE method by applying it to analyze actual MGAs derived from low-Earth orbiting lightning imaging sensor data; the actual MGA data were classified as either ground or cloud flash MGAs using National Lightning Detection Network[TM] (NLDN) data. Solution error

  1. Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

    NASA Astrophysics Data System (ADS)

    Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.

    2016-10-01

    Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.

  2. A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal Programming Model with a Novel Differential Evolution Algorithm.

    PubMed

    El-Qulity, Said Ali; Mohamed, Ali Wagdy

    2016-01-01

    This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness.

  3. An effective hybrid self-adapting differential evolution algorithm for the joint replenishment and location-inventory problem in a three-level supply chain.

    PubMed

    Wang, Lin; Qu, Hui; Chen, Tao; Yan, Fang-Ping

    2013-01-01

    The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP). The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs), the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP's difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE) is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA) and hybrid DE (HDE) are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem.

  4. An Effective Hybrid Self-Adapting Differential Evolution Algorithm for the Joint Replenishment and Location-Inventory Problem in a Three-Level Supply Chain

    PubMed Central

    Chen, Tao; Yan, Fang-Ping

    2013-01-01

    The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP). The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs), the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP's difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE) is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA) and hybrid DE (HDE) are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem. PMID:24453822

  5. Algorithms For Integrating Nonlinear Differential Equations

    NASA Technical Reports Server (NTRS)

    Freed, A. D.; Walker, K. P.

    1994-01-01

    Improved algorithms developed for use in numerical integration of systems of nonhomogenous, nonlinear, first-order, ordinary differential equations. In comparison with integration algorithms, these algorithms offer greater stability and accuracy. Several asymptotically correct, thereby enabling retention of stability and accuracy when large increments of independent variable used. Accuracies attainable demonstrated by applying them to systems of nonlinear, first-order, differential equations that arise in study of viscoplastic behavior, spread of acquired immune-deficiency syndrome (AIDS) virus and predator/prey populations.

  6. 3D non-linear inversion of magnetic anomalies caused by prismatic bodies using differential evolution algorithm

    NASA Astrophysics Data System (ADS)

    Balkaya, Çağlayan; Ekinci, Yunus Levent; Göktürkler, Gökhan; Turan, Seçil

    2017-01-01

    3D non-linear inversion of total field magnetic anomalies caused by vertical-sided prismatic bodies has been achieved by differential evolution (DE), which is one of the population-based evolutionary algorithms. We have demonstrated the efficiency of the algorithm on both synthetic and field magnetic anomalies by estimating horizontal distances from the origin in both north and east directions, depths to the top and bottom of the bodies, inclination and declination angles of the magnetization, and intensity of magnetization of the causative bodies. In the synthetic anomaly case, we have considered both noise-free and noisy data sets due to two vertical-sided prismatic bodies in a non-magnetic medium. For the field case, airborne magnetic anomalies originated from intrusive granitoids at the eastern part of the Biga Peninsula (NW Turkey) which is composed of various kinds of sedimentary, metamorphic and igneous rocks, have been inverted and interpreted. Since the granitoids are the outcropped rocks in the field, the estimations for the top depths of two prisms representing the magnetic bodies were excluded during inversion studies. Estimated bottom depths are in good agreement with the ones obtained by a different approach based on 3D modelling of pseudogravity anomalies. Accuracy of the estimated parameters from both cases has been also investigated via probability density functions. Based on the tests in the present study, it can be concluded that DE is a useful tool for the parameter estimation of source bodies using magnetic anomalies.

  7. Study of the fractional order proportional integral controller for the permanent magnet synchronous motor based on the differential evolution algorithm.

    PubMed

    Zheng, Weijia; Pi, Youguo

    2016-07-01

    A tuning method of the fractional order proportional integral speed controller for a permanent magnet synchronous motor is proposed in this paper. Taking the combination of the integral of time and absolute error and the phase margin as the optimization index, the robustness specification as the constraint condition, the differential evolution algorithm is applied to search the optimal controller parameters. The dynamic response performance and robustness of the obtained optimal controller are verified by motor speed-tracking experiments on the motor speed control platform. Experimental results show that the proposed tuning method can enable the obtained control system to achieve both the optimal dynamic response performance and the robustness to gain variations. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Multiobjective Aerodynamic Shape Optimization Using Pareto Differential Evolution and Generalized Response Surface Metamodels

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.

    2004-01-01

    Differential Evolution (DE) is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. The DE algorithm has been recently extended to multiobjective optimization problem by using a Pareto-based approach. In this paper, a Pareto DE algorithm is applied to multiobjective aerodynamic shape optimization problems that are characterized by computationally expensive objective function evaluations. To improve computational expensive the algorithm is coupled with generalized response surface meta-models based on artificial neural networks. Results are presented for some test optimization problems from the literature to demonstrate the capabilities of the method.

  9. A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal Programming Model with a Novel Differential Evolution Algorithm

    PubMed Central

    El-Qulity, Said Ali; Mohamed, Ali Wagdy

    2016-01-01

    This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness. PMID:26819583

  10. Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds.

    PubMed

    Uher, Vojtěch; Gajdoš, Petr; Radecký, Michal; Snášel, Václav

    2016-01-01

    The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.

  11. Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

    PubMed Central

    Radecký, Michal; Snášel, Václav

    2016-01-01

    The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds. PMID:27974884

  12. Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization.

    PubMed

    Wong, Ieong; Liu, Wenjia; Ho, Chih-Ming; Ding, Xianting

    2017-06-01

    Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction-based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.

  13. Battery parameterisation based on differential evolution via a boundary evolution strategy

    NASA Astrophysics Data System (ADS)

    Yang, Guangya

    2014-01-01

    Attention has been given to the battery modelling in the electric engineering field following the current development of renewable energy and electrification of transportation. The establishment of the equivalent circuit model of the battery requires data preparation and parameterisation. Besides, as the equivalent circuit model is an abstract map of the battery electric characteristics, the determination of the possible ranges of parameters can be a challenging task. In this paper, an efficient yet easy to implement method is proposed to parameterise the equivalent circuit model of batteries utilising the advances of evolutionary algorithms (EAs). Differential evolution (DE) is selected and modified to parameterise an equivalent circuit model of lithium-ion batteries. A boundary evolution strategy (BES) is developed and incorporated into the DE to update the parameter boundaries during the parameterisation. The method can parameterise the model without extensive data preparation. In addition, the approach can also estimate the initial SOC and the available capacity. The efficiency of the approach is verified through two battery packs, one is an 8-cell battery module and one from an electrical vehicle.

  14. Adaptive cockroach swarm algorithm

    NASA Astrophysics Data System (ADS)

    Obagbuwa, Ibidun C.; Abidoye, Ademola P.

    2017-07-01

    An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.

  15. NARMAX model identification of a palm oil biodiesel engine using multi-objective optimization differential evolution

    NASA Astrophysics Data System (ADS)

    Mansor, Zakwan; Zakaria, Mohd Zakimi; Nor, Azuwir Mohd; Saad, Mohd Sazli; Ahmad, Robiah; Jamaluddin, Hishamuddin

    2017-09-01

    This paper presents the black-box modelling of palm oil biodiesel engine (POB) using multi-objective optimization differential evolution (MOODE) algorithm. Two objective functions are considered in the algorithm for optimization; minimizing the number of term of a model structure and minimizing the mean square error between actual and predicted outputs. The mathematical model used in this study to represent the POB system is nonlinear auto-regressive moving average with exogenous input (NARMAX) model. Finally, model validity tests are applied in order to validate the possible models that was obtained from MOODE algorithm and lead to select an optimal model.

  16. Parallel Algorithm Solves Coupled Differential Equations

    NASA Technical Reports Server (NTRS)

    Hayashi, A.

    1987-01-01

    Numerical methods adapted to concurrent processing. Algorithm solves set of coupled partial differential equations by numerical integration. Adapted to run on hypercube computer, algorithm separates problem into smaller problems solved concurrently. Increase in computing speed with concurrent processing over that achievable with conventional sequential processing appreciable, especially for large problems.

  17. Experimental implementation of local adiabatic evolution algorithms by an NMR quantum information processor.

    PubMed

    Mitra, Avik; Ghosh, Arindam; Das, Ranabir; Patel, Apoorva; Kumar, Anil

    2005-12-01

    Quantum adiabatic algorithm is a method of solving computational problems by evolving the ground state of a slowly varying Hamiltonian. The technique uses evolution of the ground state of a slowly varying Hamiltonian to reach the required output state. In some cases, such as the adiabatic versions of Grover's search algorithm and Deutsch-Jozsa algorithm, applying the global adiabatic evolution yields a complexity similar to their classical algorithms. However, using the local adiabatic evolution, the algorithms given by J. Roland and N.J. Cerf for Grover's search [J. Roland, N.J. Cerf, Quantum search by local adiabatic evolution, Phys. Rev. A 65 (2002) 042308] and by Saurya Das, Randy Kobes, and Gabor Kunstatter for the Deutsch-Jozsa algorithm [S. Das, R. Kobes, G. Kunstatter, Adiabatic quantum computation and Deutsh's algorithm, Phys. Rev. A 65 (2002) 062301], yield a complexity of order N (where N=2(n) and n is the number of qubits). In this paper, we report the experimental implementation of these local adiabatic evolution algorithms on a 2-qubit quantum information processor, by Nuclear Magnetic Resonance.

  18. Algorithms for Differential Games with Bounded Control and States.

    DTIC Science & Technology

    1982-03-01

    D-R124 642 ALGORITHMS FOR DIFFERENTIAL GAMES WI1TH BOUNDED CONTROL 1/2 AND STATES(U) CALIFORNIA UNIV LOS ANGELES SCHOOL OF ENGINEERING AND APPLIED...RECIPILNT’S CATALOG NUMBER None ~_________ TITLE (end Subtitle) S. TYPE OF REPORT P ERIOD COVERED ALGORITHMS FOR DIFFERENTIAL GAMES WITH Final, 11/29/79-11/28...problems are probably the most natural application of differential game theory and have been treated by many authors as such. Very few problems of this

  19. Modelling Evolutionary Algorithms with Stochastic Differential Equations.

    PubMed

    Heredia, Jorge Pérez

    2017-11-20

    There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogues of the additive and multiplicative drift theorems from runtime analysis. In addition, we derive a new more general multiplicative drift theorem that also covers non-elitist EAs. This theorem simultaneously allows for positive and negative results, providing information on the algorithm's progress even when the problem cannot be optimised efficiently. Finally, we provide results for some well-known heuristics namely Random Walk (RW), Random Local Search (RLS), the (1+1) EA, the Metropolis Algorithm (MA), and the Strong Selection Weak Mutation (SSWM) algorithm.

  20. Optimization of a mirror-based neutron source using differential evolution algorithm

    NASA Astrophysics Data System (ADS)

    Yurov, D. V.; Prikhodko, V. V.

    2016-12-01

    This study is dedicated to the assessment of capabilities of gas-dynamic trap (GDT) and gas-dynamic multiple-mirror trap (GDMT) as potential neutron sources for subcritical hybrids. In mathematical terms the problem of the study has been formulated as determining the global maximum of fusion gain (Q pl), the latter represented as a function of trap parameters. A differential evolution method has been applied to perform the search. Considered in all calculations has been a configuration of the neutron source with 20 m long distance between the mirrors and 100 MW heating power. It is important to mention that the numerical study has also taken into account a number of constraints on plasma characteristics so as to provide physical credibility of searched-for trap configurations. According to the results obtained the traps considered have demonstrated fusion gain up to 0.2, depending on the constraints applied. This enables them to be used either as neutron sources within subcritical reactors for minor actinides incineration or as material-testing facilities.

  1. Differential evolution enhanced with multiobjective sorting-based mutation operators.

    PubMed

    Wang, Jiahai; Liao, Jianjun; Zhou, Ying; Cai, Yiqiao

    2014-12-01

    Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The salient feature of DE lies in its mutation mechanism. Generally, the parents in the mutation operator of DE are randomly selected from the population. Hence, all vectors are equally likely to be selected as parents without selective pressure at all. Additionally, the diversity information is always ignored. In order to fully exploit the fitness and diversity information of the population, this paper presents a DE framework with multiobjective sorting-based mutation operator. In the proposed mutation operator, individuals in the current population are firstly sorted according to their fitness and diversity contribution by nondominated sorting. Then parents in the mutation operators are proportionally selected according to their rankings based on fitness and diversity, thus, the promising individuals with better fitness and diversity have more opportunity to be selected as parents. Since fitness and diversity information is simultaneously considered for parent selection, a good balance between exploration and exploitation can be achieved. The proposed operator is applied to original DE algorithms, as well as several advanced DE variants. Experimental results on 48 benchmark functions and 12 real-world application problems show that the proposed operator is an effective approach to enhance the performance of most DE algorithms studied.

  2. Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

    NASA Astrophysics Data System (ADS)

    Ebtehaj, Isa; Bonakdari, Hossein

    2017-12-01

    Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number ( Fr) parameters includes the volumetric sediment concentration ( C V ), ratio of median particle diameter to hydraulic radius ( d/R), ratio of median particle diameter to pipe diameter ( d/D) and overall friction factor of sediment ( λ s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy ( R 2 = 0.965) than the ANFIS model and regression-based equations.

  3. Color separation in forensic image processing using interactive differential evolution.

    PubMed

    Mushtaq, Harris; Rahnamayan, Shahryar; Siddiqi, Areeb

    2015-01-01

    Color separation is an image processing technique that has often been used in forensic applications to differentiate among variant colors and to remove unwanted image interference. This process can reveal important information such as covered text or fingerprints in forensic investigation procedures. However, several limitations prevent users from selecting the appropriate parameters pertaining to the desired and undesired colors. This study proposes the hybridization of an interactive differential evolution (IDE) and a color separation technique that no longer requires users to guess required control parameters. The IDE algorithm optimizes these parameters in an interactive manner by utilizing human visual judgment to uncover desired objects. A comprehensive experimental verification has been conducted on various sample test images, including heavily obscured texts, texts with subtle color variations, and fingerprint smudges. The advantage of IDE is apparent as it effectively optimizes the color separation parameters at a level indiscernible to the naked eyes. © 2014 American Academy of Forensic Sciences.

  4. PSEMA: An Algorithm for Pattern Stimulated Evolution of Music

    NASA Astrophysics Data System (ADS)

    Mavrogianni, A. N.; Vlachos, D. S.; Harvalias, G.

    2008-11-01

    An algorithm for pattern stimulating evolution of music is presented in this work (PSEMA). The system combines a pattern with a genetic algorithm for automatic music composition in order to create a musical phrase uniquely characterizing the pattern. As an example a musical portrait is presented. The initialization of the musical phrases is done with a Markov Chain process. The evolution is dominated by an arbitrary correspondence between the pattern (feature extraction of the pattern may be used in this step) and the esthetic result of the musical phrase.

  5. Aerodynamic Shape Optimization Using Hybridized Differential Evolution

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.

    2003-01-01

    An aerodynamic shape optimization method that uses an evolutionary algorithm known at Differential Evolution (DE) in conjunction with various hybridization strategies is described. DE is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Various hybridization strategies for DE are explored, including the use of neural networks as well as traditional local search methods. A Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the hybrid DE optimizer. The method is implemented on distributed parallel computers so that new designs can be obtained within reasonable turnaround times. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. (The final paper will include at least one other aerodynamic design application). The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated.

  6. Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU

    NASA Astrophysics Data System (ADS)

    Aissa, Mohamed Hasanine; Verstraete, Tom; Vuik, Cornelis

    2016-06-01

    Differential Evolution (DE) is a powerful stochastic optimization method. Compared to gradient-based algorithms, DE is able to avoid local minima but requires at the same time more function evaluations. In turbomachinery applications, function evaluations are performed with time-consuming CFD simulation, which results in a long, non affordable, design cycle. Modern High Performance Computing systems, especially Graphic Processing Units (GPUs), are able to alleviate this inconvenience by accelerating the design evaluation itself. In this work we present a validated CFD Solver running on GPUs, able to accelerate the design evaluation and thus the entire design process. An achieved speedup of 20x to 30x enabled the DE algorithm to run on a high-end computer instead of a costly large cluster. The GPU-enhanced DE was used to optimize the aerodynamics of a supersonic compressor cascade, achieving an aerodynamic loss minimization of 20%.

  7. Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU

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

    Aissa, Mohamed Hasanine; Verstraete, Tom; Vuik, Cornelis

    Differential Evolution (DE) is a powerful stochastic optimization method. Compared to gradient-based algorithms, DE is able to avoid local minima but requires at the same time more function evaluations. In turbomachinery applications, function evaluations are performed with time-consuming CFD simulation, which results in a long, non affordable, design cycle. Modern High Performance Computing systems, especially Graphic Processing Units (GPUs), are able to alleviate this inconvenience by accelerating the design evaluation itself. In this work we present a validated CFD Solver running on GPUs, able to accelerate the design evaluation and thus the entire design process. An achieved speedup of 20xmore » to 30x enabled the DE algorithm to run on a high-end computer instead of a costly large cluster. The GPU-enhanced DE was used to optimize the aerodynamics of a supersonic compressor cascade, achieving an aerodynamic loss minimization of 20%.« less

  8. Low dose reconstruction algorithm for differential phase contrast imaging.

    PubMed

    Wang, Zhentian; Huang, Zhifeng; Zhang, Li; Chen, Zhiqiang; Kang, Kejun; Yin, Hongxia; Wang, Zhenchang; Marco, Stampanoni

    2011-01-01

    Differential phase contrast imaging computed tomography (DPCI-CT) is a novel x-ray inspection method to reconstruct the distribution of refraction index rather than the attenuation coefficient in weakly absorbing samples. In this paper, we propose an iterative reconstruction algorithm for DPCI-CT which benefits from the new compressed sensing theory. We first realize a differential algebraic reconstruction technique (DART) by discretizing the projection process of the differential phase contrast imaging into a linear partial derivative matrix. In this way the compressed sensing reconstruction problem of DPCI reconstruction can be transformed to a resolved problem in the transmission imaging CT. Our algorithm has the potential to reconstruct the refraction index distribution of the sample from highly undersampled projection data. Thus it can significantly reduce the dose and inspection time. The proposed algorithm has been validated by numerical simulations and actual experiments.

  9. Gradient Evolution-based Support Vector Machine Algorithm for Classification

    NASA Astrophysics Data System (ADS)

    Zulvia, Ferani E.; Kuo, R. J.

    2018-03-01

    This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

  10. A Novel Hybrid Firefly Algorithm for Global Optimization.

    PubMed

    Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

    Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

  11. A Novel Hybrid Firefly Algorithm for Global Optimization

    PubMed Central

    Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

    2016-01-01

    Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869

  12. Enhanced differential evolution to combine optical mouse sensor with image structural patches for robust endoscopic navigation.

    PubMed

    Luo, Xiongbiao; Jayarathne, Uditha L; McLeod, A Jonathan; Mori, Kensaku

    2014-01-01

    Endoscopic navigation generally integrates different modalities of sensory information in order to continuously locate an endoscope relative to suspicious tissues in the body during interventions. Current electromagnetic tracking techniques for endoscopic navigation have limited accuracy due to tissue deformation and magnetic field distortion. To avoid these limitations and improve the endoscopic localization accuracy, this paper proposes a new endoscopic navigation framework that uses an optical mouse sensor to measure the endoscope movements along its viewing direction. We then enhance the differential evolution algorithm by modifying its mutation operation. Based on the enhanced differential evolution method, these movement measurements and image structural patches in endoscopic videos are fused to accurately determine the endoscope position. An evaluation on a dynamic phantom demonstrated that our method provides a more accurate navigation framework. Compared to state-of-the-art methods, it improved the navigation accuracy from 2.4 to 1.6 mm and reduced the processing time from 2.8 to 0.9 seconds.

  13. CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies.

    PubMed

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

    2017-08-01

    Detecting epistatic interactions in genome-wide association studies (GWAS) is a computational challenge. Such huge numbers of single-nucleotide polymorphism (SNP) combinations limit the some of the powerful algorithms to be applied to detect the potential epistasis in large-scale SNP datasets. We propose a new algorithm which combines the differential evolution (DE) algorithm with a classification based multifactor-dimensionality reduction (CMDR), termed DECMDR. DECMDR uses the CMDR as a fitness measure to evaluate values of solutions in DE process for scanning the potential statistical epistasis in GWAS. The results indicated that DECMDR outperforms the existing algorithms in terms of detection success rate by the large simulation and real data obtained from the Wellcome Trust Case Control Consortium. For running time comparison, DECMDR can efficient to apply the CMDR to detect the significant association between cases and controls amongst all possible SNP combinations in GWAS. DECMDR is freely available at https://goo.gl/p9sLuJ . chuang@isu.edu.tw or e0955767257@yahoo.com.tw. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  14. New Parallel Algorithms for Landscape Evolution Model

    NASA Astrophysics Data System (ADS)

    Jin, Y.; Zhang, H.; Shi, Y.

    2017-12-01

    Most landscape evolution models (LEM) developed in the last two decades solve the diffusion equation to simulate the transportation of surface sediments. This numerical approach is difficult to parallelize due to the computation of drainage area for each node, which needs huge amount of communication if run in parallel. In order to overcome this difficulty, we developed two parallel algorithms for LEM with a stream net. One algorithm handles the partition of grid with traditional methods and applies an efficient global reduction algorithm to do the computation of drainage areas and transport rates for the stream net; the other algorithm is based on a new partition algorithm, which partitions the nodes in catchments between processes first, and then partitions the cells according to the partition of nodes. Both methods focus on decreasing communication between processes and take the advantage of massive computing techniques, and numerical experiments show that they are both adequate to handle large scale problems with millions of cells. We implemented the two algorithms in our program based on the widely used finite element library deal.II, so that it can be easily coupled with ASPECT.

  15. Prediction of chemical biodegradability using support vector classifier optimized with differential evolution.

    PubMed

    Cao, Qi; Leung, K M

    2014-09-22

    Reliable computer models for the prediction of chemical biodegradability from molecular descriptors and fingerprints are very important for making health and environmental decisions. Coupling of the differential evolution (DE) algorithm with the support vector classifier (SVC) in order to optimize the main parameters of the classifier resulted in an improved classifier called the DE-SVC, which is introduced in this paper for use in chemical biodegradability studies. The DE-SVC was applied to predict the biodegradation of chemicals on the basis of extensive sample data sets and known structural features of molecules. Our optimization experiments showed that DE can efficiently find the proper parameters of the SVC. The resulting classifier possesses strong robustness and reliability compared with grid search, genetic algorithm, and particle swarm optimization methods. The classification experiments conducted here showed that the DE-SVC exhibits better classification performance than models previously used for such studies. It is a more effective and efficient prediction model for chemical biodegradability.

  16. Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

    PubMed Central

    Huang, Lei; Liao, Li; Wu, Cathy H.

    2016-01-01

    Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273

  17. A Differential Evolution Based Approach to Estimate the Shape and Size of Complex Shaped Anomalies Using EIT Measurements

    NASA Astrophysics Data System (ADS)

    Rashid, Ahmar; Khambampati, Anil Kumar; Kim, Bong Seok; Liu, Dong; Kim, Sin; Kim, Kyung Youn

    EIT image reconstruction is an ill-posed problem, the spatial resolution of the estimated conductivity distribution is usually poor and the external voltage measurements are subject to variable noise. Therefore, EIT conductivity estimation cannot be used in the raw form to correctly estimate the shape and size of complex shaped regional anomalies. An efficient algorithm employing a shape based estimation scheme is needed. The performance of traditional inverse algorithms, such as the Newton Raphson method, used for this purpose is below par and depends upon the initial guess and the gradient of the cost functional. This paper presents the application of differential evolution (DE) algorithm to estimate complex shaped region boundaries, expressed as coefficients of truncated Fourier series, using EIT. DE is a simple yet powerful population-based, heuristic algorithm with the desired features to solve global optimization problems under realistic conditions. The performance of the algorithm has been tested through numerical simulations, comparing its results with that of the traditional modified Newton Raphson (mNR) method.

  18. Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Mingming; Luo, Jianjun; Fang, Jing; Yuan, Jianping

    2018-03-01

    The existence of the path dependent dynamic singularities limits the volume of available workspace of free-floating space robot and induces enormous joint velocities when such singularities are met. In order to overcome this demerit, this paper presents an optimal joint trajectory planning method using forward kinematics equations of free-floating space robot, while joint motion laws are delineated with application of the concept of reaction null-space. Bézier curve, in conjunction with the null-space column vectors, are applied to describe the joint trajectories. Considering the forward kinematics equations of the free-floating space robot, the trajectory planning issue is consequently transferred to an optimization issue while the control points to construct the Bézier curve are the design variables. A constrained differential evolution (DE) scheme with premature handling strategy is implemented to find the optimal solution of the design variables while specific objectives and imposed constraints are satisfied. Differ from traditional methods, we synthesize null-space and specialized curve to provide a novel viewpoint for trajectory planning of free-floating space robot. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a free-floating spacecraft and demonstrate the feasibility and effectiveness of the proposed method.

  19. Cloud computing task scheduling strategy based on differential evolution and ant colony optimization

    NASA Astrophysics Data System (ADS)

    Ge, Junwei; Cai, Yu; Fang, Yiqiu

    2018-05-01

    This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost, and load.

  20. Quasi-Newton methods for parameter estimation in functional differential equations

    NASA Technical Reports Server (NTRS)

    Brewer, Dennis W.

    1988-01-01

    A state-space approach to parameter estimation in linear functional differential equations is developed using the theory of linear evolution equations. A locally convergent quasi-Newton type algorithm is applied to distributed systems with particular emphasis on parameters that induce unbounded perturbations of the state. The algorithm is computationally implemented on several functional differential equations, including coefficient and delay estimation in linear delay-differential equations.

  1. Use of artificial bee colonies algorithm as numerical approximation of differential equations solution

    NASA Astrophysics Data System (ADS)

    Fikri, Fariz Fahmi; Nuraini, Nuning

    2018-03-01

    The differential equation is one of the branches in mathematics which is closely related to human life problems. Some problems that occur in our life can be modeled into differential equations as well as systems of differential equations such as the Lotka-Volterra model and SIR model. Therefore, solving a problem of differential equations is very important. Some differential equations are difficult to solve, so numerical methods are needed to solve that problems. Some numerical methods for solving differential equations that have been widely used are Euler Method, Heun Method, Runge-Kutta and others. However, some of these methods still have some restrictions that cause the method cannot be used to solve more complex problems such as an evaluation interval that we cannot change freely. New methods are needed to improve that problems. One of the method that can be used is the artificial bees colony algorithm. This algorithm is one of metaheuristic algorithm method, which can come out from local search space and do exploration in solution search space so that will get better solution than other method.

  2. An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution.

    PubMed

    Biswas, Subhodip; Kundu, Souvik; Das, Swagatam

    2014-10-01

    In real life, we often need to find multiple optimally sustainable solutions of an optimization problem. Evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework. Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces. This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule. The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter. The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods. Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm.

  3. Algorithms for network-based identification of differential regulators from transcriptome data: a systematic evaluation

    PubMed Central

    Hui, YU; Ramkrishna, MITRA; Jing, YANG; YuanYuan, LI; ZhongMing, ZHAO

    2016-01-01

    Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases. Several computational algorithms have recently been developed for this purpose by using transcriptome and network data. However, it remains largely unclear which algorithm performs better under a specific condition. Such knowledge is important for both appropriate application and future enhancement of these algorithms. Here, we systematically evaluated seven main algorithms (TED, TDD, TFactS, RIF1, RIF2, dCSA_t2t, and dCSA_r2t), using both simulated and real datasets. In our simulation evaluation, we artificially inactivated either a single regulator or multiple regulators and examined how well each algorithm detected known gold standard regulators. We found that all these algorithms could effectively discern signals arising from regulatory network differences, indicating the validity of our simulation schema. Among the seven tested algorithms, TED and TFactS were placed first and second when both discrimination accuracy and robustness against data variation were considered. When applied to two independent lung cancer datasets, both TED and TFactS replicated a substantial fraction of their respective differential regulators. Since TED and TFactS rely on two distinct features of transcriptome data, namely differential co-expression and differential expression, both may be applied as mutual references during practical application. PMID:25326829

  4. Algorithm refinement for stochastic partial differential equations: II. Correlated systems

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

    Alexander, Francis J.; Garcia, Alejandro L.; Tartakovsky, Daniel M.

    2005-08-10

    We analyze a hybrid particle/continuum algorithm for a hydrodynamic system with long ranged correlations. Specifically, we consider the so-called train model for viscous transport in gases, which is based on a generalization of the random walk process for the diffusion of momentum. This discrete model is coupled with its continuous counterpart, given by a pair of stochastic partial differential equations. At the interface between the particle and continuum computations the coupling is by flux matching, giving exact mass and momentum conservation. This methodology is an extension of our stochastic Algorithm Refinement (AR) hybrid for simple diffusion [F. Alexander, A. Garcia,more » D. Tartakovsky, Algorithm refinement for stochastic partial differential equations: I. Linear diffusion, J. Comput. Phys. 182 (2002) 47-66]. Results from a variety of numerical experiments are presented for steady-state scenarios. In all cases the mean and variance of density and velocity are captured correctly by the stochastic hybrid algorithm. For a non-stochastic version (i.e., using only deterministic continuum fluxes) the long-range correlations of velocity fluctuations are qualitatively preserved but at reduced magnitude.« less

  5. On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.

    2004-01-01

    Differential Evolution (DE) is a simple and robust evolutionary strategy that has been provEn effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. Several approaches that have proven effective for other evolutionary algorithms are modified and implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for standard test optimization problems and for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

  6. Explicit Filtering Based Low-Dose Differential Phase Reconstruction Algorithm with the Grating Interferometry.

    PubMed

    Jiang, Xiaolei; Zhang, Li; Zhang, Ran; Yin, Hongxia; Wang, Zhenchang

    2015-01-01

    X-ray grating interferometry offers a novel framework for the study of weakly absorbing samples. Three kinds of information, that is, the attenuation, differential phase contrast (DPC), and dark-field images, can be obtained after a single scanning, providing additional and complementary information to the conventional attenuation image. Phase shifts of X-rays are measured by the DPC method; hence, DPC-CT reconstructs refraction indexes rather than attenuation coefficients. In this work, we propose an explicit filtering based low-dose differential phase reconstruction algorithm, which enables reconstruction from reduced scanning without artifacts. The algorithm adopts a differential algebraic reconstruction technique (DART) with the explicit filtering based sparse regularization rather than the commonly used total variation (TV) method. Both the numerical simulation and the biological sample experiment demonstrate the feasibility of the proposed algorithm.

  7. Explicit Filtering Based Low-Dose Differential Phase Reconstruction Algorithm with the Grating Interferometry

    PubMed Central

    Zhang, Li; Zhang, Ran; Yin, Hongxia; Wang, Zhenchang

    2015-01-01

    X-ray grating interferometry offers a novel framework for the study of weakly absorbing samples. Three kinds of information, that is, the attenuation, differential phase contrast (DPC), and dark-field images, can be obtained after a single scanning, providing additional and complementary information to the conventional attenuation image. Phase shifts of X-rays are measured by the DPC method; hence, DPC-CT reconstructs refraction indexes rather than attenuation coefficients. In this work, we propose an explicit filtering based low-dose differential phase reconstruction algorithm, which enables reconstruction from reduced scanning without artifacts. The algorithm adopts a differential algebraic reconstruction technique (DART) with the explicit filtering based sparse regularization rather than the commonly used total variation (TV) method. Both the numerical simulation and the biological sample experiment demonstrate the feasibility of the proposed algorithm. PMID:26089971

  8. An algorithm for the numerical solution of linear differential games

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

    Polovinkin, E S; Ivanov, G E; Balashov, M V

    2001-10-31

    A numerical algorithm for the construction of stable Krasovskii bridges, Pontryagin alternating sets, and also of piecewise program strategies solving two-person linear differential (pursuit or evasion) games on a fixed time interval is developed on the basis of a general theory. The aim of the first player (the pursuer) is to hit a prescribed target (terminal) set by the phase vector of the control system at the prescribed time. The aim of the second player (the evader) is the opposite. A description of numerical algorithms used in the solution of differential games of the type under consideration is presented andmore » estimates of the errors resulting from the approximation of the game sets by polyhedra are presented.« less

  9. Differences in Cell Division Rates Drive the Evolution of Terminal Differentiation in Microbes

    PubMed Central

    Matias Rodrigues, João F.; Rankin, Daniel J.; Rossetti, Valentina; Wagner, Andreas; Bagheri, Homayoun C.

    2012-01-01

    Multicellular differentiated organisms are composed of cells that begin by developing from a single pluripotent germ cell. In many organisms, a proportion of cells differentiate into specialized somatic cells. Whether these cells lose their pluripotency or are able to reverse their differentiated state has important consequences. Reversibly differentiated cells can potentially regenerate parts of an organism and allow reproduction through fragmentation. In many organisms, however, somatic differentiation is terminal, thereby restricting the developmental paths to reproduction. The reason why terminal differentiation is a common developmental strategy remains unexplored. To understand the conditions that affect the evolution of terminal versus reversible differentiation, we developed a computational model inspired by differentiating cyanobacteria. We simulated the evolution of a population of two cell types –nitrogen fixing or photosynthetic– that exchange resources. The traits that control differentiation rates between cell types are allowed to evolve in the model. Although the topology of cell interactions and differentiation costs play a role in the evolution of terminal and reversible differentiation, the most important factor is the difference in division rates between cell types. Faster dividing cells always evolve to become the germ line. Our results explain why most multicellular differentiated cyanobacteria have terminally differentiated cells, while some have reversibly differentiated cells. We further observed that symbioses involving two cooperating lineages can evolve under conditions where aggregate size, connectivity, and differentiation costs are high. This may explain why plants engage in symbiotic interactions with diazotrophic bacteria. PMID:22511858

  10. Asymptotic integration algorithms for nonhomogeneous, nonlinear, first order, ordinary differential equations

    NASA Technical Reports Server (NTRS)

    Walker, K. P.; Freed, A. D.

    1991-01-01

    New methods for integrating systems of stiff, nonlinear, first order, ordinary differential equations are developed by casting the differential equations into integral form. Nonlinear recursive relations are obtained that allow the solution to a system of equations at time t plus delta t to be obtained in terms of the solution at time t in explicit and implicit forms. Examples of accuracy obtained with the new technique are given by considering systems of nonlinear, first order equations which arise in the study of unified models of viscoplastic behaviors, the spread of the AIDS virus, and predator-prey populations. In general, the new implicit algorithm is unconditionally stable, and has a Jacobian of smaller dimension than that which is acquired by current implicit methods, such as the Euler backward difference algorithm; yet, it gives superior accuracy. The asymptotic explicit and implicit algorithms are suitable for solutions that are of the growing and decaying exponential kinds, respectively, whilst the implicit Euler-Maclaurin algorithm is superior when the solution oscillates, i.e., when there are regions in which both growing and decaying exponential solutions exist.

  11. Brain-Inspired Constructive Learning Algorithms with Evolutionally Additive Nonlinear Neurons

    NASA Astrophysics Data System (ADS)

    Fang, Le-Heng; Lin, Wei; Luo, Qiang

    In this article, inspired partially by the physiological evidence of brain’s growth and development, we developed a new type of constructive learning algorithm with evolutionally additive nonlinear neurons. The new algorithms have remarkable ability in effective regression and accurate classification. In particular, the algorithms are able to sustain a certain reduction of the loss function when the dynamics of the trained network are bogged down in the vicinity of the local minima. The algorithm augments the neural network by adding only a few connections as well as neurons whose activation functions are nonlinear, nonmonotonic, and self-adapted to the dynamics of the loss functions. Indeed, we analytically demonstrate the reduction dynamics of the algorithm for different problems, and further modify the algorithms so as to obtain an improved generalization capability for the augmented neural networks. Finally, through comparing with the classical algorithm and architecture for neural network construction, we show that our constructive learning algorithms as well as their modified versions have better performances, such as faster training speed and smaller network size, on several representative benchmark datasets including the MNIST dataset for handwriting digits.

  12. Efficient receiver tuning using differential evolution strategies

    NASA Astrophysics Data System (ADS)

    Wheeler, Caleb H.; Toland, Trevor G.

    2016-08-01

    Differential evolution (DE) is a powerful and computationally inexpensive optimization strategy that can be used to search an entire parameter space or to converge quickly on a solution. The Kilopixel Array Pathfinder Project (KAPPa) is a heterodyne receiver system delivering 5 GHz of instantaneous bandwidth in the tuning range of 645-695 GHz. The fully automated KAPPa receiver test system finds optimal receiver tuning using performance feedback and DE. We present an adaptation of DE for use in rapid receiver characterization. The KAPPa DE algorithm is written in Python 2.7 and is fully integrated with the KAPPa instrument control, data processing, and visualization code. KAPPa develops the technologies needed to realize heterodyne focal plane arrays containing 1000 pixels. Finding optimal receiver tuning by investigating large parameter spaces is one of many challenges facing the characterization phase of KAPPa. This is a difficult task via by-hand techniques. Characterizing or tuning in an automated fashion without need for human intervention is desirable for future large scale arrays. While many optimization strategies exist, DE is ideal for time and performance constraints because it can be set to converge to a solution rapidly with minimal computational overhead. We discuss how DE is utilized in the KAPPa system and discuss its performance and look toward the future of 1000 pixel array receivers and consider how the KAPPa DE system might be applied.

  13. Wrinkling pattern evolution of cylindrical biological tissues with differential growth.

    PubMed

    Jia, Fei; Li, Bo; Cao, Yan-Ping; Xie, Wei-Hua; Feng, Xi-Qiao

    2015-01-01

    Three-dimensional surface wrinkling of soft cylindrical tissues induced by differential growth is explored. Differential volumetric growth can cause their morphological stability, leading to the formation of hexagonal and labyrinth wrinkles. During postbuckling, multiple bifurcations and morphological transitions may occur as a consequence of continuous growth in the surface layer. The physical mechanisms underpinning the morphological evolution are examined from the viewpoint of energy. Surface curvature is found to play a regulatory role in the pattern evolution. This study may not only help understand the morphogenesis of soft biological tissues, but also inspire novel routes for creating desired surface patterns of soft materials.

  14. Comparative Analysis of Particle Swarm and Differential Evolution via Tuning on Ultrasmall Titanium Oxide Nanoclusters

    NASA Astrophysics Data System (ADS)

    Inclan, Eric; Lassester, Jack; Geohegan, David; Yoon, Mina

    Optimization algorithms (OA) coupled with numerical methods enable researchers to identify and study (meta) stable nanoclusters without the control restrictions of empirical methods. An algorithm's performance is governed by two factors: (1) its compatibility with an objective function, (2) the dimension of a design space, which increases with cluster size. Although researchers often tune an algorithm's user-defined parameters (UDP), tuning is not guaranteed to improve performance. In this research, Particle Swarm (PSO) and Differential Evolution (DE), are compared by tuning their UDP in a multi-objective optimization environment (MOE). Combined with a Kolmogorov Smirnov test for statistical significance, the MOE enables the study of the Pareto Front (PF), made of the UDP settings that trade-off between best performance in energy minimization (``effectiveness'') based on force-field potential energy, and best convergence rate (``efficiency''). By studying the PF, this research finds that UDP values frequently suggested in the literature do not provide best effectiveness for these methods. Additionally, monotonic convergence is found to significantly improve efficiency without sacrificing effectiveness for very small systems, suggesting better compatibility. Work is supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division.

  15. The knowledge instinct, cognitive algorithms, modeling of language and cultural evolution

    NASA Astrophysics Data System (ADS)

    Perlovsky, Leonid I.

    2008-04-01

    The talk discusses mechanisms of the mind and their engineering applications. The past attempts at designing "intelligent systems" encountered mathematical difficulties related to algorithmic complexity. The culprit turned out to be logic, which in one way or another was used not only in logic rule systems, but also in statistical, neural, and fuzzy systems. Algorithmic complexity is related to Godel's theory, a most fundamental mathematical result. These difficulties were overcome by replacing logic with a dynamic process "from vague to crisp," dynamic logic. It leads to algorithms overcoming combinatorial complexity, and resulting in orders of magnitude improvement in classical problems of detection, tracking, fusion, and prediction in noise. I present engineering applications to pattern recognition, detection, tracking, fusion, financial predictions, and Internet search engines. Mathematical and engineering efficiency of dynamic logic can also be understood as cognitive algorithm, which describes fundamental property of the mind, the knowledge instinct responsible for all our higher cognitive functions: concepts, perception, cognition, instincts, imaginations, intuitions, emotions, including emotions of the beautiful. I present our latest results in modeling evolution of languages and cultures, their interactions in these processes, and role of music in cultural evolution. Experimental data is presented that support the theory. Future directions are outlined.

  16. Arterial cannula shape optimization by means of the rotational firefly algorithm

    NASA Astrophysics Data System (ADS)

    Tesch, K.; Kaczorowska, K.

    2016-03-01

    This article presents global optimization results of arterial cannula shapes by means of the newly modified firefly algorithm. The search for the optimal arterial cannula shape is necessary in order to minimize losses and prepare the flow that leaves the circulatory support system of a ventricle (i.e. blood pump) before it reaches the heart. A modification of the standard firefly algorithm, the so-called rotational firefly algorithm, is introduced. It is shown that the rotational firefly algorithm allows for better exploration of search spaces which results in faster convergence and better solutions in comparison with its standard version. This is particularly pronounced for smaller population sizes. Furthermore, it maintains greater diversity of populations for a longer time. A small population size and a low number of iterations are necessary to keep to a minimum the computational cost of the objective function of the problem, which comes from numerical solution of the nonlinear partial differential equations. Moreover, both versions of the firefly algorithm are compared to the state of the art, namely the differential evolution and covariance matrix adaptation evolution strategies.

  17. On the adaptivity and complexity embedded into differential evolution

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

    Senkerik, Roman; Pluhacek, Michal; Jasek, Roman

    2016-06-08

    This research deals with the comparison of the two modern approaches for evolutionary algorithms, which are the adaptivity and complex chaotic dynamics. This paper aims on the investigations on the chaos-driven Differential Evolution (DE) concept. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of chaotic pseudo random number generators for the DE and comparing the influence to the performance with the state of the art adaptive representative jDE. This research is focused mainly on the possible disadvantages and advantages of both compared approaches. Repeated simulations for Lozi map driving chaotic systems were performedmore » on the simple benchmark functions set, which are more close to the real optimization problems. Obtained results are compared with the canonical not-chaotic and not adaptive DE. Results show that with used simple test functions, the performance of ChaosDE is better in the most cases than jDE and Canonical DE, furthermore due to the unique sequencing in CPRNG given by the hidden chaotic dynamics, thus better and faster selection of unique individuals from population, ChaosDE is faster.« less

  18. Fast wavelet based algorithms for linear evolution equations

    NASA Technical Reports Server (NTRS)

    Engquist, Bjorn; Osher, Stanley; Zhong, Sifen

    1992-01-01

    A class was devised of fast wavelet based algorithms for linear evolution equations whose coefficients are time independent. The method draws on the work of Beylkin, Coifman, and Rokhlin which they applied to general Calderon-Zygmund type integral operators. A modification of their idea is applied to linear hyperbolic and parabolic equations, with spatially varying coefficients. A significant speedup over standard methods is obtained when applied to hyperbolic equations in one space dimension and parabolic equations in multidimensions.

  19. Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning

    NASA Technical Reports Server (NTRS)

    Smelyanskiy, V. N.; Toussaint, U. V.; Timucin, D. A.

    2002-01-01

    We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum excitation gap. g min, = O(n 2(exp -n/2), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to 'the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.

  20. Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning

    NASA Technical Reports Server (NTRS)

    Smelyanskiy, Vadius; vonToussaint, Udo V.; Timucin, Dogan A.; Clancy, Daniel (Technical Monitor)

    2002-01-01

    We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum exitation gap, gmin = O(n2(sup -n/2)), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.

  1. Contemporary evolution during invasion: evidence for differentiation, natural selection, and local adaptation.

    PubMed

    Colautti, Robert I; Lau, Jennifer A

    2015-05-01

    Biological invasions are 'natural' experiments that can improve our understanding of contemporary evolution. We evaluate evidence for population differentiation, natural selection and adaptive evolution of invading plants and animals at two nested spatial scales: (i) among introduced populations (ii) between native and introduced genotypes. Evolution during invasion is frequently inferred, but rarely confirmed as adaptive. In common garden studies, quantitative trait differentiation is only marginally lower (~3.5%) among introduced relative to native populations, despite genetic bottlenecks and shorter timescales (i.e. millennia vs. decades). However, differentiation between genotypes from the native vs. introduced range is less clear and confounded by nonrandom geographic sampling; simulations suggest this causes a high false-positive discovery rate (>50%) in geographically structured populations. Selection differentials (¦s¦) are stronger in introduced than in native species, although selection gradients (¦β¦) are not, consistent with introduced species experiencing weaker genetic constraints. This could facilitate rapid adaptation, but evidence is limited. For example, rapid phenotypic evolution often manifests as geographical clines, but simulations demonstrate that nonadaptive trait clines can evolve frequently during colonization (~two-thirds of simulations). Additionally, QST-FST studies may often misrepresent the strength and form of natural selection acting during invasion. Instead, classic approaches in evolutionary ecology (e.g. selection analysis, reciprocal transplant, artificial selection) are necessary to determine the frequency of adaptive evolution during invasion and its influence on establishment, spread and impact of invasive species. These studies are rare but crucial for managing biological invasions in the context of global change. © 2015 John Wiley & Sons Ltd.

  2. An algorithmic framework for multiobjective optimization.

    PubMed

    Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P

    2013-01-01

    Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

  3. Multiple-try differential evolution adaptive Metropolis for efficient solution of highly parameterized models

    NASA Astrophysics Data System (ADS)

    Eric, L.; Vrugt, J. A.

    2010-12-01

    Spatially distributed hydrologic models potentially contain hundreds of parameters that need to be derived by calibration against a historical record of input-output data. The quality of this calibration strongly determines the predictive capability of the model and thus its usefulness for science-based decision making and forecasting. Unfortunately, high-dimensional optimization problems are typically difficult to solve. Here we present our recent developments to the Differential Evolution Adaptive Metropolis (DREAM) algorithm (Vrugt et al., 2009) to warrant efficient solution of high-dimensional parameter estimation problems. The algorithm samples from an archive of past states (Ter Braak and Vrugt, 2008), and uses multiple-try Metropolis sampling (Liu et al., 2000) to decrease the required burn-in time for each individual chain and increase efficiency of posterior sampling. This approach is hereafter referred to as MT-DREAM. We present results for 2 synthetic mathematical case studies, and 2 real-world examples involving from 10 to 240 parameters. Results for those cases show that our multiple-try sampler, MT-DREAM, can consistently find better solutions than other Bayesian MCMC methods. Moreover, MT-DREAM is admirably suited to be implemented and ran on a parallel machine and is therefore a powerful method for posterior inference.

  4. Evolution of music score watermarking algorithm

    NASA Astrophysics Data System (ADS)

    Busch, Christoph; Nesi, Paolo; Schmucker, Martin; Spinu, Marius B.

    2002-04-01

    Content protection for multimedia data is widely recognized especially for data types that are frequently distributed, sold or shared using the Internet. Particularly music industry dealing with audio files realized the necessity for content protection. Distribution of music sheets will face the same problems. Digital watermarking techniques provide a certain level of protection for these music sheets. But classical raster-oriented watermarking algorithms for images suffer several drawbacks when directly applied to image representations of music sheets. Therefore new solutions have been developed which are designed regarding the content of the music sheets. In Comparison to other media types the development for watermarking of music scores is a rather young art. The paper reviews the evolution of the early approaches and describes the current state of the art in the field.

  5. A Modified Differential Coherent Bit Synchronization Algorithm for BeiDou Weak Signals with Large Frequency Deviation.

    PubMed

    Han, Zhifeng; Liu, Jianye; Li, Rongbing; Zeng, Qinghua; Wang, Yi

    2017-07-04

    BeiDou system navigation messages are modulated with a secondary NH (Neumann-Hoffman) code of 1 kbps, where frequent bit transitions limit the coherent integration time to 1 millisecond. Therefore, a bit synchronization algorithm is necessary to obtain bit edges and NH code phases. In order to realize bit synchronization for BeiDou weak signals with large frequency deviation, a bit synchronization algorithm based on differential coherent and maximum likelihood is proposed. Firstly, a differential coherent approach is used to remove the effect of frequency deviation, and the differential delay time is set to be a multiple of bit cycle to remove the influence of NH code. Secondly, the maximum likelihood function detection is used to improve the detection probability of weak signals. Finally, Monte Carlo simulations are conducted to analyze the detection performance of the proposed algorithm compared with a traditional algorithm under the CN0s of 20~40 dB-Hz and different frequency deviations. The results show that the proposed algorithm outperforms the traditional method with a frequency deviation of 50 Hz. This algorithm can remove the effect of BeiDou NH code effectively and weaken the influence of frequency deviation. To confirm the feasibility of the proposed algorithm, real data tests are conducted. The proposed algorithm is suitable for BeiDou weak signal bit synchronization with large frequency deviation.

  6. Algorithmic framework for group analysis of differential equations and its application to generalized Zakharov-Kuznetsov equations

    NASA Astrophysics Data System (ADS)

    Huang, Ding-jiang; Ivanova, Nataliya M.

    2016-02-01

    In this paper, we explain in more details the modern treatment of the problem of group classification of (systems of) partial differential equations (PDEs) from the algorithmic point of view. More precisely, we revise the classical Lie algorithm of construction of symmetries of differential equations, describe the group classification algorithm and discuss the process of reduction of (systems of) PDEs to (systems of) equations with smaller number of independent variables in order to construct invariant solutions. The group classification algorithm and reduction process are illustrated by the example of the generalized Zakharov-Kuznetsov (GZK) equations of form ut +(F (u)) xxx +(G (u)) xyy +(H (u)) x = 0. As a result, a complete group classification of the GZK equations is performed and a number of new interesting nonlinear invariant models which have non-trivial invariance algebras are obtained. Lie symmetry reductions and exact solutions for two important invariant models, i.e., the classical and modified Zakharov-Kuznetsov equations, are constructed. The algorithmic framework for group analysis of differential equations presented in this paper can also be applied to other nonlinear PDEs.

  7. [Algorithm for the differential diagnosis of precancerous and regenerative changes in the cervix uteri].

    PubMed

    Sazonova, V Iu; Fedorova, V E; Danilova, N V

    2013-01-01

    Pretumoral changes in the epithelium of the cervix uteri include cervical intraepithelial neoplasia (CIN). CIN III should be differentiated with regenerative changes during epidermization of endocervicoses. Epidermization is proliferation of undifferentiated reserve cells that differentiate towards the squamous epithelium, by superseding the ectopic endocervical glandular epithelium. This process was called immature squamous metaplasia (ISM). The objective of the investigation was to define the significance of different morphological signs in the differential diagnosis of CIN III and ISM. One hundred and twelve cervical, CIN III, and immature squamous metaplasia biopsies were selected for examination. The selected cervical specimens were divided into 2 groups according to the presence or absence of p16 and CK17 expression. The p16+, CK17- cases were taken as true CIN III and the pl 6-, CK17+ as a regenerative process. The basis for this investigation is the signs included by O.K. Khmelnitsky into an algorithm for the differential diagnosis of epidermizing pseudoerosion and intraepithelial cancer of the cervix uteri. The algorithm was reconsidered to objectify. The investigation established great differences in the number of significant mitoses in the study groups. A clear trend was found for differences in the number of acanthotic strands. A new differential diagnostic algorithm for CIN III and ISM, which included the number of significant mitoses and acanthotic strands and p16 and CK17 expression, was proposed.

  8. An Algorithmic Framework for Multiobjective Optimization

    PubMed Central

    Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

    2013-01-01

    Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

  9. Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

    PubMed Central

    Hernández-Ocaña, Betania; Pozos-Parra, Ma. Del Pilar; Mezura-Montes, Efrén; Portilla-Flores, Edgar Alfredo; Vega-Alvarado, Eduardo; Calva-Yáñez, Maria Bárbara

    2016-01-01

    This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem. PMID:27057156

  10. Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms.

    PubMed

    Hernández-Ocaña, Betania; Pozos-Parra, Ma Del Pilar; Mezura-Montes, Efrén; Portilla-Flores, Edgar Alfredo; Vega-Alvarado, Eduardo; Calva-Yáñez, Maria Bárbara

    2016-01-01

    This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

  11. From Determinism and Probability to Chaos: Chaotic Evolution towards Philosophy and Methodology of Chaotic Optimization

    PubMed Central

    2015-01-01

    We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed. PMID:25879067

  12. From determinism and probability to chaos: chaotic evolution towards philosophy and methodology of chaotic optimization.

    PubMed

    Pei, Yan

    2015-01-01

    We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed.

  13. A Comprehensive Review of Swarm Optimization Algorithms

    PubMed Central

    2015-01-01

    Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655

  14. Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

    NASA Astrophysics Data System (ADS)

    Baraldi, P.; Bonfanti, G.; Zio, E.

    2018-03-01

    The identification of the current degradation state of an industrial component and the prediction of its future evolution is a fundamental step for the development of condition-based and predictive maintenance approaches. The objective of the present work is to propose a general method for extracting a health indicator to measure the amount of component degradation from a set of signals measured during operation. The proposed method is based on the combined use of feature extraction techniques, such as Empirical Mode Decomposition and Auto-Associative Kernel Regression, and a multi-objective Binary Differential Evolution (BDE) algorithm for selecting the subset of features optimal for the definition of the health indicator. The objectives of the optimization are desired characteristics of the health indicator, such as monotonicity, trendability and prognosability. A case study is considered, concerning the prediction of the remaining useful life of turbofan engines. The obtained results confirm that the method is capable of extracting health indicators suitable for accurate prognostics.

  15. SGO: A fast engine for ab initio atomic structure global optimization by differential evolution

    NASA Astrophysics Data System (ADS)

    Chen, Zhanghui; Jia, Weile; Jiang, Xiangwei; Li, Shu-Shen; Wang, Lin-Wang

    2017-10-01

    As the high throughout calculations and material genome approaches become more and more popular in material science, the search for optimal ways to predict atomic global minimum structure is a high research priority. This paper presents a fast method for global search of atomic structures at ab initio level. The structures global optimization (SGO) engine consists of a high-efficiency differential evolution algorithm, accelerated local relaxation methods and a plane-wave density functional theory code running on GPU machines. The purpose is to show what can be achieved by combining the superior algorithms at the different levels of the searching scheme. SGO can search the global-minimum configurations of crystals, two-dimensional materials and quantum clusters without prior symmetry restriction in a relatively short time (half or several hours for systems with less than 25 atoms), thus making such a task a routine calculation. Comparisons with other existing methods such as minima hopping and genetic algorithm are provided. One motivation of our study is to investigate the properties of magnetic systems in different phases. The SGO engine is capable of surveying the local minima surrounding the global minimum, which provides the information for the overall energy landscape of a given system. Using this capability we have found several new configurations for testing systems, explored their energy landscape, and demonstrated that the magnetic moment of metal clusters fluctuates strongly in different local minima.

  16. On convergence of differential evolution over a class of continuous functions with unique global optimum.

    PubMed

    Ghosh, Sayan; Das, Swagatam; Vasilakos, Athanasios V; Suresh, Kaushik

    2012-02-01

    Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. Since its inception in the mid 1990s, DE has been finding many successful applications in real-world optimization problems from diverse domains of science and engineering. This paper takes a first significant step toward the convergence analysis of a canonical DE (DE/rand/1/bin) algorithm. It first deduces a time-recursive relationship for the probability density function (PDF) of the trial solutions, taking into consideration the DE-type mutation, crossover, and selection mechanisms. Then, by applying the concepts of Lyapunov stability theorems, it shows that as time approaches infinity, the PDF of the trial solutions concentrates narrowly around the global optimum of the objective function, assuming the shape of a Dirac delta distribution. Asymptotic convergence behavior of the population PDF is established by constructing a Lyapunov functional based on the PDF and showing that it monotonically decreases with time. The analysis is applicable to a class of continuous and real-valued objective functions that possesses a unique global optimum (but may have multiple local optima). Theoretical results have been substantiated with relevant computer simulations.

  17. A Multilevel Algorithm for the Solution of Second Order Elliptic Differential Equations on Sparse Grids

    NASA Technical Reports Server (NTRS)

    Pflaum, Christoph

    1996-01-01

    A multilevel algorithm is presented that solves general second order elliptic partial differential equations on adaptive sparse grids. The multilevel algorithm consists of several V-cycles. Suitable discretizations provide that the discrete equation system can be solved in an efficient way. Numerical experiments show a convergence rate of order Omicron(1) for the multilevel algorithm.

  18. Random Matrix Approach to Quantum Adiabatic Evolution Algorithms

    NASA Technical Reports Server (NTRS)

    Boulatov, Alexei; Smelyanskiy, Vadier N.

    2004-01-01

    We analyze the power of quantum adiabatic evolution algorithms (Q-QA) for solving random NP-hard optimization problems within a theoretical framework based on the random matrix theory (RMT). We present two types of the driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that the failure mechanism of the QAA is due to the interaction of the ground state with the "cloud" formed by all the excited states, confirming that in the driven RMT models. the Landau-Zener mechanism of dissipation is not important. We show that the QAEA has a finite probability of success in a certain range of parameters. implying the polynomial complexity of the algorithm. The second model corresponds to the standard QAEA with the problem Hamiltonian taken from the Gaussian Unitary RMT ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. However, the driven RMT model always leads to the exponential complexity of the algorithm due to the presence of the long-range intertemporal correlations of the eigenvalues. Our results indicate that the weakness of effective transitions is the leading effect that can make the Markovian type QAEA successful.

  19. Design Document for Differential GPS Ground Reference Station Pseudorange Correction Generation Algorithm

    DOT National Transportation Integrated Search

    1986-12-01

    The algorithms described in this report determine the differential corrections to be broadcast to users of the Global Positioning System (GPS) who require higher accuracy navigation or position information than the 30 to 100 meters that GPS normally ...

  20. A hybrid optimization algorithm to explore atomic configurations of TiO 2 nanoparticles

    DOE PAGES

    Inclan, Eric J.; Geohegan, David B.; Yoon, Mina

    2017-10-17

    Here in this paper we present a hybrid algorithm comprised of differential evolution, coupled with the Broyden–Fletcher–Goldfarb–Shanno quasi-Newton optimization algorithm, for the purpose of identifying a broad range of (meta)stable Ti nO 2n nanoparticles, as an example system, described by Buckingham interatomic potential. The potential and its gradient are modified to be piece-wise continuous to enable use of these continuous-domain, unconstrained algorithms, thereby improving compatibility. To measure computational effectiveness a regression on known structures is used. This approach defines effectiveness as the ability of an algorithm to produce a set of structures whose energy distribution follows the regression as themore » number of Ti nO 2n increases such that the shape of the distribution is consistent with the algorithm’s stated goals. Our calculation demonstrates that the hybrid algorithm finds global minimum configurations more effectively than the differential evolution algorithms, widely employed in the field of materials science. Specifically, the hybrid algorithm is shown to reproduce the global minimum energy structures reported in the literature up to n = 5, and retains good agreement with the regression up to n = 25. For 25 < n < 100, where literature structures are unavailable, the hybrid effectively obtains structures that are in lower energies per TiO 2 unit as the system size increases.« less

  1. A novel composite adaptive flap controller design by a high-efficient modified differential evolution identification approach.

    PubMed

    Li, Nailu; Mu, Anle; Yang, Xiyun; Magar, Kaman T; Liu, Chao

    2018-05-01

    The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Bouc-Wen hysteresis model identification using Modified Firefly Algorithm

    NASA Astrophysics Data System (ADS)

    Zaman, Mohammad Asif; Sikder, Urmita

    2015-12-01

    The parameters of Bouc-Wen hysteresis model are identified using a Modified Firefly Algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount of error between a set of given data points and points obtained from the Bouc-Wen model. The performance of the algorithm is compared with the performance of conventional Firefly Algorithm, Genetic Algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc-Wen model parameters. Finally, the proposed method is used to find the Bouc-Wen model parameters from experimental data. The obtained model is found to be in good agreement with measured data.

  3. Thermal evolution of a partially differentiated H chondrite parent body

    NASA Astrophysics Data System (ADS)

    Abrahams, J. N. H.; Bryson, J. F. J.; Weiss, B. P.; Nimmo, F.

    2016-12-01

    It has traditionally been assumed that planetesimals either melted entirely or remained completely undifferentiated as they accreted. The unmelted textures and cooling histories of chondrites have been used to argue that these meteorites originated from bodies that never differentiated. However, paleomagnetic measurements indicate that some chondrites (e.g., the H chondrite Portales Valley and several CV chondrites) were magnetized by a core dynamo magnetic field, implying that their parent bodies were partially differentiated. It has been unclear, however, whether planetesimal histories consistent with dynamo production can also be consistent with the diversity of chondrite cooling rates and ages. To address this, we modeled the thermal evolution of the H chondrite parent body, considering a variety of accretion histories and parent body radii. We considered partial differentiation using two-stage accretion involving the initial formation and differentiation of a small body, followed by the later addition of low thermal conductivity chondritic material that remains mostly unmelted. We were able to reproduce the measured thermal evolution of multiple H chondrites for a range of parent body parameters, including initial radii from 70-150 km, chondritic layer thicknesses from 50 km to over 100 km, and second stage accretion times of 2.5-3 Myr after solar system formation. Our predicted rates of core cooling and crystallization are consistent with dynamo generation by compositional convection beginning 60-200 Myr after solar system formation and lasting for at least tens of millions of years. This is consistent with magnetic studies of Portales Valley [Bryson et al., this meeting]. In summary, we find that thermal models of partial differentiation are consistent the radiometric ages, magnetization, and cooling rates of a diversity H chondrites.

  4. Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots.

    PubMed

    Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores

    2015-09-16

    One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot's pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area.

  5. Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots

    PubMed Central

    Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores

    2015-01-01

    One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot’s pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area. PMID:26389914

  6. Design of a genetic algorithm for the simulated evolution of a library of asymmetric transfer hydrogenation catalysts.

    PubMed

    Vriamont, Nicolas; Govaerts, Bernadette; Grenouillet, Pierre; de Bellefon, Claude; Riant, Olivier

    2009-06-15

    A library of catalysts was designed for asymmetric-hydrogen transfer to acetophenone. At first, the whole library was submitted to evaluation using high-throughput experiments (HTE). The catalysts were listed in ascending order, with respect to their performance, and best catalysts were identified. In the second step, various simulated evolution experiments, based on a genetic algorithm, were applied to this library. A small part of the library, called the mother generation (G0), thus evolved from generation to generation. The goal was to use our collection of HTE data to adjust the parameters of the genetic algorithm, in order to obtain a maximum of the best catalysts within a minimal number of generations. It was namely found that simulated evolution's results depended on the selection of G0 and that a random G0 should be preferred. We also demonstrated that it was possible to get 5 to 6 of the ten best catalysts while investigating only 10 % of the library. Moreover, we developed a double algorithm making this result still achievable if the evolution started with one of the worst G0.

  7. Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

    NASA Astrophysics Data System (ADS)

    Pholdee, Nantiwat; Park, Won-Woong; Kim, Dong-Kyu; Im, Yong-Taek; Bureerat, Sujin; Kwon, Hyuck-Cheol; Chun, Myung-Sik

    2015-04-01

    This article proposes an efficient metaheuristic based on hybridization of teaching-learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching-learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.

  8. A POPULATION MEMETICS APPROACH TO CULTURAL EVOLUTION IN CHAFFINCH SONG: DIFFERENTIATION AMONG POPULATIONS.

    PubMed

    Lynch, Alejandro; Baker, Allan J

    1994-04-01

    We investigated cultural evolution in populations of common chaffinches (Fringilla coelebs) in the Atlantic islands (Azores, Madeira, and Canaries) and neighboring continental regions (Morocco and Iberia) by employing a population-memetic approach. To quantify differentiation, we used the concept of a song meme, defined as a single syllable or a series of linked syllables capable of being transmitted. The levels of cultural differentiation are higher among the Canaries populations than among the Azorean ones, even though the islands are on average closer to each other geographically. This is likely the result of reduced levels of migration, lower population sizes, and bottlenecks (possibly during the colonization of these populations) in the Canaries; all these factors produce a smaller effective population size and therefore accentuate the effects of differentiation by random drift. Significant levels of among-population differentiation in the Azores, in spite of substantial levels of migration, attest to the differentiating effects of high mutation rates of memes, which allow the accumulation of new mutants in different populations before migration can disperse them throughout the entire region. © 1994 The Society for the Study of Evolution.

  9. The Stochastic Evolution of a Protocell: The Gillespie Algorithm in a Dynamically Varying Volume

    PubMed Central

    Carletti, T.; Filisetti, A.

    2012-01-01

    We propose an improvement of the Gillespie algorithm allowing us to study the time evolution of an ensemble of chemical reactions occurring in a varying volume, whose growth is directly related to the amount of some specific molecules, belonging to the reactions set. This allows us to study the stochastic evolution of a protocell, whose volume increases because of the production of container molecules. Several protocell models are considered and compared with the deterministic models. PMID:22536297

  10. Evidence of correlated evolution and adaptive differentiation of stem and leaf functional traits in the herbaceous genus, Helianthus.

    PubMed

    Pilote, Alex J; Donovan, Lisa A

    2016-12-01

    Patterns of plant stem traits are expected to align with a "fast-slow" plant economic spectrum across taxa. Although broad patterns support such tradeoffs in field studies, tests of hypothesized correlated trait evolution and adaptive differentiation are more robust when taxa relatedness and environment are taken into consideration. Here we test for correlated evolution of stem and leaf traits and their adaptive differentiation across environments in the herbaceous genus, Helianthus. Stem and leaf traits of 14 species of Helianthus (28 populations) were assessed in a common garden greenhouse study. Phylogenetically independent contrasts were used to test for evidence of correlated evolution of stem hydraulic and biomechanical properties, correlated evolution of stem and leaf traits, and adaptive differentiation associated with source habitat environments. Among stem traits, there was evidence for correlated evolution of some hydraulic and biomechanical properties, supporting an expected tradeoff between stem theoretical hydraulic efficiency and resistance to bending stress. Population differentiation for suites of stem and leaf traits was found to be consistent with a "fast-slow" resource-use axis for traits related to water transport and use. Associations of population traits with source habitat characteristics supported repeated evolution of a resource-acquisitive "drought-escape" strategy in arid environments. This study provides evidence of correlated evolution of stem and leaf traits consistent with the fast-slow spectrum of trait combinations related to water transport and use along the stem-to-leaf pathway. Correlations of traits with source habitat characteristics further indicate that the correlated evolution is associated, at least in part, with adaptive differentiation of Helianthus populations among native habitats differing in climate. © 2016 Botanical Society of America.

  11. Towards developing robust algorithms for solving partial differential equations on MIMD machines

    NASA Technical Reports Server (NTRS)

    Saltz, Joel H.; Naik, Vijay K.

    1988-01-01

    Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.

  12. Towards developing robust algorithms for solving partial differential equations on MIMD machines

    NASA Technical Reports Server (NTRS)

    Saltz, J. H.; Naik, V. K.

    1985-01-01

    Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.

  13. Evolution of tuf genes: ancient duplication, differential loss and gene conversion.

    PubMed

    Lathe, W C; Bork, P

    2001-08-03

    The tuf gene of eubacteria, encoding the EF-tu elongation factor, was duplicated early in the evolution of the taxon. Phylogenetic and genomic location analysis of 20 complete eubacterial genomes suggests that this ancient duplication has been differentially lost and maintained in eubacteria.

  14. Improved Bat Algorithm Applied to Multilevel Image Thresholding

    PubMed Central

    2014-01-01

    Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed. PMID:25165733

  15. GPU-accelerated adjoint algorithmic differentiation

    NASA Astrophysics Data System (ADS)

    Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe

    2016-03-01

    Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the ;tape;. Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.

  16. GPU-Accelerated Adjoint Algorithmic Differentiation

    PubMed Central

    Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe

    2015-01-01

    Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the “tape”. Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography

  17. GPU-Accelerated Adjoint Algorithmic Differentiation.

    PubMed

    Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe

    2016-03-01

    Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.

  18. An evolution based biosensor receptor DNA sequence generation algorithm.

    PubMed

    Kim, Eungyeong; Lee, Malrey; Gatton, Thomas M; Lee, Jaewan; Zang, Yupeng

    2010-01-01

    A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements.

  19. Structures vibration control via Tuned Mass Dampers using a co-evolution Coral Reefs Optimization algorithm

    NASA Astrophysics Data System (ADS)

    Salcedo-Sanz, S.; Camacho-Gómez, C.; Magdaleno, A.; Pereira, E.; Lorenzana, A.

    2017-04-01

    In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.

  20. Evolution of plant conducting cells: perspectives from key regulators of vascular cell differentiation.

    PubMed

    Ohtani, Misato; Akiyoshi, Nobuhiro; Takenaka, Yuto; Sano, Ryosuke; Demura, Taku

    2017-01-01

    One crucial problem that plants faced during their evolution, particularly during the transition to growth on land, was how to transport water, nutrients, metabolites, and small signaling molecules within a large, multicellular body. As a solution to this problem, land plants developed specific tissues for conducting molecules, called water-conducting cells (WCCs) and food-conducting cells (FCCs). The well-developed WCCs and FCCs in extant plants are the tracheary elements and sieve elements, respectively, which are found in vascular plants. Recent molecular genetic studies revealed that transcriptional networks regulate the differentiation of tracheary and sieve elements, and that the networks governing WCC differentiation are largely conserved among land plant species. In this review, we discuss the molecular evolution of plant conducting cells. By focusing on the evolution of the key transcription factors that regulate vascular cell differentiation, the NAC transcription factor VASCULAR-RELATED NAC-DOMAIN for WCCs and the MYB-coiled-coil (CC)-type transcription factor ALTERED PHLOEM DEVELOPMENT for sieve elements, we describe how land plants evolved molecular systems to produce the specialized cells that function as WCCs and FCCs. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  1. An improved harmony search algorithm for emergency inspection scheduling

    NASA Astrophysics Data System (ADS)

    Kallioras, Nikos A.; Lagaros, Nikos D.; Karlaftis, Matthew G.

    2014-11-01

    The ability of nature-inspired search algorithms to efficiently handle combinatorial problems, and their successful implementation in many fields of engineering and applied sciences, have led to the development of new, improved algorithms. In this work, an improved harmony search (IHS) algorithm is presented, while a holistic approach for solving the problem of post-disaster infrastructure management is also proposed. The efficiency of IHS is compared with that of the algorithms of particle swarm optimization, differential evolution, basic harmony search and the pure random search procedure, when solving the districting problem that is the first part of post-disaster infrastructure management. The ant colony optimization algorithm is employed for solving the associated routing problem that constitutes the second part. The comparison is based on the quality of the results obtained, the computational demands and the sensitivity on the algorithmic parameters.

  2. Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

    PubMed Central

    Cruz-Aceves, I.; Avina-Cervantes, J. G.; Lopez-Hernandez, J. M.; Rostro-Gonzalez, H.; Garcia-Capulin, C. H.; Torres-Cisneros, M.; Guzman-Cabrera, R.

    2013-01-01

    This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation. PMID:23983809

  3. Cognitive algorithms: dynamic logic, working of the mind, evolution of consciousness and cultures

    NASA Astrophysics Data System (ADS)

    Perlovsky, Leonid I.

    2007-04-01

    The paper discusses evolution of consciousness driven by the knowledge instinct, a fundamental mechanism of the mind which determines its higher cognitive functions. Dynamic logic mathematically describes the knowledge instinct. It overcomes past mathematical difficulties encountered in modeling intelligence and relates it to mechanisms of concepts, emotions, instincts, consciousness and unconscious. The two main aspects of the knowledge instinct are differentiation and synthesis. Differentiation is driven by dynamic logic and proceeds from vague and unconscious states to more crisp and conscious states, from less knowledge to more knowledge at each hierarchical level of the mind. Synthesis is driven by dynamic logic operating in a hierarchical organization of the mind; it strives to achieve unity and meaning of knowledge: every concept finds its deeper and more general meaning at a higher level. These mechanisms are in complex relationship of symbiosis and opposition, which leads to complex dynamics of evolution of consciousness and cultures. Modeling this dynamics in a population leads to predictions for the evolution of consciousness, and cultures. Cultural predictive models can be compared to experimental data and used for improvement of human conditions. We discuss existing evidence and future research directions.

  4. Differential paralog divergence modulates genome evolution across yeast species

    PubMed Central

    Lynch, Bryony; Huang, Mei; Alcantara, Erica; DeSevo, Christopher G.; Pai, Dave A.; Hoang, Margaret L.

    2017-01-01

    Evolutionary outcomes depend not only on the selective forces acting upon a species, but also on the genetic background. However, large timescales and uncertain historical selection pressures can make it difficult to discern such important background differences between species. Experimental evolution is one tool to compare evolutionary potential of known genotypes in a controlled environment. Here we utilized a highly reproducible evolutionary adaptation in Saccharomyces cerevisiae to investigate whether experimental evolution of other yeast species would select for similar adaptive mutations. We evolved populations of S. cerevisiae, S. paradoxus, S. mikatae, S. uvarum, and interspecific hybrids between S. uvarum and S. cerevisiae for ~200–500 generations in sulfate-limited continuous culture. Wild-type S. cerevisiae cultures invariably amplify the high affinity sulfate transporter gene, SUL1. However, while amplification of the SUL1 locus was detected in S. paradoxus and S. mikatae populations, S. uvarum cultures instead selected for amplification of the paralog, SUL2. We measured the relative fitness of strains bearing deletions and amplifications of both SUL genes from different species, confirming that, converse to S. cerevisiae, S. uvarum SUL2 contributes more to fitness in sulfate limitation than S. uvarum SUL1. By measuring the fitness and gene expression of chimeric promoter-ORF constructs, we were able to delineate the cause of this differential fitness effect primarily to the promoter of S. uvarum SUL1. Our data show evidence of differential sub-functionalization among the sulfate transporters across Saccharomyces species through recent changes in noncoding sequence. Furthermore, these results show a clear example of how such background differences due to paralog divergence can drive changes in genome evolution. PMID:28196070

  5. An investigation of generalized differential evolution metaheuristic for multiobjective optimal crop-mix planning decision.

    PubMed

    Adekanmbi, Oluwole; Olugbara, Oludayo; Adeyemo, Josiah

    2014-01-01

    This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms-being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem.

  6. DE and NLP Based QPLS Algorithm

    NASA Astrophysics Data System (ADS)

    Yu, Xiaodong; Huang, Dexian; Wang, Xiong; Liu, Bo

    As a novel evolutionary computing technique, Differential Evolution (DE) has been considered to be an effective optimization method for complex optimization problems, and achieved many successful applications in engineering. In this paper, a new algorithm of Quadratic Partial Least Squares (QPLS) based on Nonlinear Programming (NLP) is presented. And DE is used to solve the NLP so as to calculate the optimal input weights and the parameters of inner relationship. The simulation results based on the soft measurement of diesel oil solidifying point on a real crude distillation unit demonstrate that the superiority of the proposed algorithm to linear PLS and QPLS which is based on Sequential Quadratic Programming (SQP) in terms of fitting accuracy and computational costs.

  7. Differential sampling for fast frequency acquisition via adaptive extended least squares algorithm

    NASA Technical Reports Server (NTRS)

    Kumar, Rajendra

    1987-01-01

    This paper presents a differential signal model along with appropriate sampling techinques for least squares estimation of the frequency and frequency derivatives and possibly the phase and amplitude of a sinusoid received in the presence of noise. The proposed algorithm is recursive in mesurements and thus the computational requirement increases only linearly with the number of measurements. The dimension of the state vector in the proposed algorithm does not depend upon the number of measurements and is quite small, typically around four. This is an advantage when compared to previous algorithms wherein the dimension of the state vector increases monotonically with the product of the frequency uncertainty and the observation period. Such a computational simplification may possibly result in some loss of optimality. However, by applying the sampling techniques of the paper such a possible loss in optimality can made small.

  8. New algorithms for solving high even-order differential equations using third and fourth Chebyshev-Galerkin methods

    NASA Astrophysics Data System (ADS)

    Doha, E. H.; Abd-Elhameed, W. M.; Bassuony, M. A.

    2013-03-01

    This paper is concerned with spectral Galerkin algorithms for solving high even-order two point boundary value problems in one dimension subject to homogeneous and nonhomogeneous boundary conditions. The proposed algorithms are extended to solve two-dimensional high even-order differential equations. The key to the efficiency of these algorithms is to construct compact combinations of Chebyshev polynomials of the third and fourth kinds as basis functions. The algorithms lead to linear systems with specially structured matrices that can be efficiently inverted. Numerical examples are included to demonstrate the validity and applicability of the proposed algorithms, and some comparisons with some other methods are made.

  9. AI-BL1.0: a program for automatic on-line beamline optimization using the evolutionary algorithm.

    PubMed

    Xi, Shibo; Borgna, Lucas Santiago; Zheng, Lirong; Du, Yonghua; Hu, Tiandou

    2017-01-01

    In this report, AI-BL1.0, an open-source Labview-based program for automatic on-line beamline optimization, is presented. The optimization algorithms used in the program are Genetic Algorithm and Differential Evolution. Efficiency was improved by use of a strategy known as Observer Mode for Evolutionary Algorithm. The program was constructed and validated at the XAFCA beamline of the Singapore Synchrotron Light Source and 1W1B beamline of the Beijing Synchrotron Radiation Facility.

  10. An Effective Hybrid Cuckoo Search Algorithm with Improved Shuffled Frog Leaping Algorithm for 0-1 Knapsack Problems

    PubMed Central

    Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun

    2014-01-01

    An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. PMID:25404940

  11. 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.

  12. Implications of monotreme and marsupial chromosome evolution on sex determination and differentiation.

    PubMed

    Deakin, Janine E

    2017-04-01

    Studies of chromosomes from monotremes and marsupials endemic to Australasia have provided important insight into the evolution of their genomes as well as uncovering fundamental differences in their sex determination/differentiation pathways. Great advances have been made this century into solving the mystery of the complicated sex chromosome system in monotremes. Monotremes possess multiple different X and Y chromosomes and a candidate sex determining gene has been identified. Even greater advancements have been made for marsupials, with reconstruction of the ancestral karyotype enabling the evolutionary history of marsupial chromosomes to be determined. Furthermore, the study of sex chromosomes in intersex marsupials has afforded insight into differences in the sexual differentiation pathway between marsupials and eutherians, together with experiments showing the insensitivity of the mammary glands, pouch and scrotum to exogenous hormones, led to the hypothesis that there is a gene (or genes) on the X chromosome responsible for the development of either pouch or scrotum. This review highlights the major advancements made towards understanding chromosome evolution and how this has impacted on our understanding of sex determination and differentiation in these interesting mammals. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. The origin and evolution of a differentiated Mimas

    NASA Astrophysics Data System (ADS)

    Neveu, M.; Rhoden, A. R.

    2017-11-01

    In stark contrast with its neighbor moon Enceladus, Mimas is surprisingly geologically quiet, despite an eccentric orbit and distance to Saturn prone to levels of tidal dissipation 30 times higher. While Mimas' lack of geological activity could be due to a stiff, frigid interior, libration data acquired using the Cassini spacecraft suggest that its interior is not homogeneous. Here, we present one-dimensional models of the thermal, structural, and orbital evolution of Mimas under two accretion scenarios: primordial, undifferentiated formation in the Saturnian sub-nebula, and late, layered formation from a debris ring created by the disruption of one or more previous moons. We find it difficult to reproduce a differentiated, eccentric Mimas under a primordial accretion scenario: either Mimas never differentiates, or the internal warming that leads to differentiation increases tidal dissipation, yielding runaway heating that produces a persistent ocean, thereby circularizing Mimas' orbit. Only if Mimas accretes very early (so that the decay of short-lived radionuclides initiates differentiation) but its rheology is not highly dissipative (in order to stop runaway tidal heating even if the eccentricity is not negligible) can the simulations match the observational constraints. Alternatively, a late, layered accretion scenario yields a present-day Mimas that matches observational constraints, independently of the magnitude of tidal dissipation. Consistent with previous findings, these models do not produce an ocean on Enceladus unless its orbital eccentricity is higher than today's value.

  14. Real coded genetic algorithm for fuzzy time series prediction

    NASA Astrophysics Data System (ADS)

    Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

    2017-10-01

    Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

  15. Presupernova Evolution of Differentially Rotating Massive Stars Including Magnetic Fields

    NASA Astrophysics Data System (ADS)

    Heger, A.; Woosley, S. E.; Spruit, H. C.

    2005-06-01

    As a massive star evolves through multiple stages of nuclear burning on its way to becoming a supernova, a complex, differentially rotating structure is set up. Angular momentum is transported by a variety of classic instabilities and also by magnetic torques from fields generated by the differential rotation. We present the first stellar evolution calculations to follow the evolution of rotating massive stars including, at least approximately, all these effects, magnetic and nonmagnetic, from the zero-age main sequence until the onset of iron-core collapse. The evolution and action of the magnetic fields is as described by Spruit in 2002, and a range of uncertain parameters is explored. In general, we find that magnetic torques decrease the final rotation rate of the collapsing iron core by about a factor of 30-50 when compared with the nonmagnetic counterparts. Angular momentum in that part of the presupernova star destined to become a neutron star is an increasing function of main-sequence mass. That is, pulsars derived from more massive stars rotate faster and rotation plays a more important role in the star's explosion. The final angular momentum of the core has been determined-to within a factor of 2-by the time the star ignites carbon burning. For the lighter stars studied, around 15 Msolar, we predict pulsar periods at birth near 15 ms, though a factor of 2 range is easily tolerated by the uncertainties. Several mechanisms for additional braking in a young neutron star, especially by fallback, are explored.

  16. Parareal algorithms with local time-integrators for time fractional differential equations

    NASA Astrophysics Data System (ADS)

    Wu, Shu-Lin; Zhou, Tao

    2018-04-01

    It is challenge work to design parareal algorithms for time-fractional differential equations due to the historical effect of the fractional operator. A direct extension of the classical parareal method to such equations will lead to unbalance computational time in each process. In this work, we present an efficient parareal iteration scheme to overcome this issue, by adopting two recently developed local time-integrators for time fractional operators. In both approaches, one introduces auxiliary variables to localized the fractional operator. To this end, we propose a new strategy to perform the coarse grid correction so that the auxiliary variables and the solution variable are corrected separately in a mixed pattern. It is shown that the proposed parareal algorithm admits robust rate of convergence. Numerical examples are presented to support our conclusions.

  17. An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

    NASA Astrophysics Data System (ADS)

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

    2018-03-01

    This paper draws on the "human reliability" concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of deviation to encompass three elegant adjustments to literature model in terms of maintenance time, workforce performance and return-on-workforce investments. These fully explain the results of our influence. The presented structure breaks new grounds in maintenance workforce theory and practice from a number of perspectives. First, we have successfully implemented fuzzy goal programming (FGP) and differential evolution (DE) techniques for the solution of optimisation problem in maintenance of a process plant for the first time. The results obtained in this work showed better quality of solution from the DE algorithm compared with those of genetic algorithm and particle swarm optimisation algorithm, thus expressing superiority of the proposed procedure over them. Second, the analytical discourse, which was framed on stochastic theory, focusing on specific application to a process plant in Nigeria is a novelty. The work provides more insights into maintenance workforce planning during overhaul rework and overtime maintenance activities in manufacturing systems and demonstrated capacity in generating substantially helpful information for practice.

  18. Algorithm for solving the linear Cauchy problem for large systems of ordinary differential equations with the use of parallel computations

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

    Moryakov, A. V., E-mail: sailor@orc.ru

    2016-12-15

    An algorithm for solving the linear Cauchy problem for large systems of ordinary differential equations is presented. The algorithm for systems of first-order differential equations is implemented in the EDELWEISS code with the possibility of parallel computations on supercomputers employing the MPI (Message Passing Interface) standard for the data exchange between parallel processes. The solution is represented by a series of orthogonal polynomials on the interval [0, 1]. The algorithm is characterized by simplicity and the possibility to solve nonlinear problems with a correction of the operator in accordance with the solution obtained in the previous iterative process.

  19. Co-evolution for Problem Simplification

    NASA Technical Reports Server (NTRS)

    Haith, Gary L.; Lohn, Jason D.; Cplombano, Silvano P.; Stassinopoulos, Dimitris

    1999-01-01

    This paper explores a co-evolutionary approach applicable to difficult problems with limited failure/success performance feedback. Like familiar "predator-prey" frameworks this algorithm evolves two populations of individuals - the solutions (predators) and the problems (prey). The approach extends previous work by rewarding only the problems that match their difficulty to the level of solut,ion competence. In complex problem domains with limited feedback, this "tractability constraint" helps provide an adaptive fitness gradient that, effectively differentiates the candidate solutions. The algorithm generates selective pressure toward the evolution of increasingly competent solutions by rewarding solution generality and uniqueness and problem tractability and difficulty. Relative (inverse-fitness) and absolute (static objective function) approaches to evaluating problem difficulty are explored and discussed. On a simple control task, this co-evolutionary algorithm was found to have significant advantages over a genetic algorithm with either a static fitness function or a fitness function that changes on a hand-tuned schedule.

  20. Duality quantum algorithm efficiently simulates open quantum systems

    PubMed Central

    Wei, Shi-Jie; Ruan, Dong; Long, Gui-Lu

    2016-01-01

    Because of inevitable coupling with the environment, nearly all practical quantum systems are open system, where the evolution is not necessarily unitary. In this paper, we propose a duality quantum algorithm for simulating Hamiltonian evolution of an open quantum system. In contrast to unitary evolution in a usual quantum computer, the evolution operator in a duality quantum computer is a linear combination of unitary operators. In this duality quantum algorithm, the time evolution of the open quantum system is realized by using Kraus operators which is naturally implemented in duality quantum computer. This duality quantum algorithm has two distinct advantages compared to existing quantum simulation algorithms with unitary evolution operations. Firstly, the query complexity of the algorithm is O(d3) in contrast to O(d4) in existing unitary simulation algorithm, where d is the dimension of the open quantum system. Secondly, By using a truncated Taylor series of the evolution operators, this duality quantum algorithm provides an exponential improvement in precision compared with previous unitary simulation algorithm. PMID:27464855

  1. Pigment cell interactions and differential xanthophore recruitment underlying zebrafish stripe reiteration and Danio pattern evolution

    PubMed Central

    Patterson, Larissa B.; Bain, Emily J.; Parichy, David M.

    2014-01-01

    Fishes have diverse pigment patterns, yet mechanisms of pattern evolution remain poorly understood. In zebrafish, Danio rerio, pigment-cell autonomous interactions generate dark stripes of melanophores that alternate with light interstripes of xanthophores and iridophores. Here, we identify mechanisms underlying the evolution of a uniform pattern in D. albolineatus in which all three pigment cell classes are intermingled. We show that in this species xanthophores differentiate precociously over a wider area, and that cis regulatory evolution has increased expression of xanthogenic Colony Stimulating Factor-1 (Csf1). Expressing Csf1 similarly in D. rerio has cascading effects, driving the intermingling of all three pigment cell classes and resulting in the loss of stripes, as in D. albolineatus. Our results identify novel mechanisms of pattern development and illustrate how pattern diversity can be generated when a core network of pigment-cell autonomous interactions is coupled with changes in pigment cell differentiation. PMID:25374113

  2. Advancing X-ray scattering metrology using inverse genetic algorithms.

    PubMed

    Hannon, Adam F; Sunday, Daniel F; Windover, Donald; Kline, R Joseph

    2016-01-01

    We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.

  3. Evaluation of an Agricultural Meteorological Disaster Based on Multiple Criterion Decision Making and Evolutionary Algorithm

    PubMed Central

    Yu, Xiaobing; Yu, Xianrui; Lu, Yiqun

    2018-01-01

    The evaluation of a meteorological disaster can be regarded as a multiple-criteria decision making problem because it involves many indexes. Firstly, a comprehensive indexing system for an agricultural meteorological disaster is proposed, which includes the disaster rate, the inundated rate, and the complete loss rate. Following this, the relative weights of the three criteria are acquired using a novel proposed evolutionary algorithm. The proposed algorithm consists of a differential evolution algorithm and an evolution strategy. Finally, a novel evaluation model, based on the proposed algorithm and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), is presented to estimate the agricultural meteorological disaster of 2008 in China. The geographic information system (GIS) technique is employed to depict the disaster. The experimental results demonstrated that the agricultural meteorological disaster of 2008 was very serious, especially in Hunan and Hubei provinces. Some useful suggestions are provided to relieve agriculture meteorological disasters. PMID:29597243

  4. Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

    NASA Astrophysics Data System (ADS)

    Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

    2017-04-01

    Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

  5. Lunar initial Nd-143/Nd-144 - Differential evolution of the lunar crust and mantle

    NASA Technical Reports Server (NTRS)

    Lugmair, G. W.; Marti, K.

    1978-01-01

    The Sm-Nd evolution of Apollo 15 green glass is discussed. The ICE age (intercept with chondritic evolution) of 3.8 + or - 0.4 eons overlaps the range of reported (Ar-39)-(Ar-40) ages and implies a distinct source region for green glass, characterized by very low and unfractionated REE abundances. Evidence is presented that LINd (lunar initial Nd) is compatible with a 'chondritic'-type Nd isotopic evolution as observed in the Juvinas meteorite. This normalization is used to study the Sm-Nd system of various lunar rock types. The results obtained from a limited number of rocks clearly indicate differential Sm-Nd evolution for the lunar crust and mantle. High-Ti basalts returned by the Apollo 11 and 17 missions were derived from distinct source regions. The Nd-143 evolution in KREEP requires a source region which is clearly distinct from any mantle reservoir.

  6. An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.

    PubMed

    Li, Bai; Gong, Li-gang; Yang, Wen-lun

    2014-01-01

    Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

  7. Algorithms and Results of Eye Tissues Differentiation Based on RF Ultrasound

    PubMed Central

    Jurkonis, R.; Janušauskas, A.; Marozas, V.; Jegelevičius, D.; Daukantas, S.; Patašius, M.; Paunksnis, A.; Lukoševičius, A.

    2012-01-01

    Algorithms and software were developed for analysis of B-scan ultrasonic signals acquired from commercial diagnostic ultrasound system. The algorithms process raw ultrasonic signals in backscattered spectrum domain, which is obtained using two time-frequency methods: short-time Fourier and Hilbert-Huang transformations. The signals from selected regions of eye tissues are characterized by parameters: B-scan envelope amplitude, approximated spectral slope, approximated spectral intercept, mean instantaneous frequency, mean instantaneous bandwidth, and parameters of Nakagami distribution characterizing Hilbert-Huang transformation output. The backscattered ultrasound signal parameters characterizing intraocular and orbit tissues were processed by decision tree data mining algorithm. The pilot trial proved that applied methods are able to correctly classify signals from corpus vitreum blood, extraocular muscle, and orbit tissues. In 26 cases of ocular tissues classification, one error occurred, when tissues were classified into classes of corpus vitreum blood, extraocular muscle, and orbit tissue. In this pilot classification parameters of spectral intercept and Nakagami parameter for instantaneous frequencies distribution of the 1st intrinsic mode function were found specific for corpus vitreum blood, orbit and extraocular muscle tissues. We conclude that ultrasound data should be further collected in clinical database to establish background for decision support system for ocular tissue noninvasive differentiation. PMID:22654643

  8. Case study: Optimizing fault model input parameters using bio-inspired algorithms

    NASA Astrophysics Data System (ADS)

    Plucar, Jan; Grunt, Onřej; Zelinka, Ivan

    2017-07-01

    We present a case study that demonstrates a bio-inspired approach in the process of finding optimal parameters for GSM fault model. This model is constructed using Petri Nets approach it represents dynamic model of GSM network environment in the suburban areas of Ostrava city (Czech Republic). We have been faced with a task of finding optimal parameters for an application that requires high amount of data transfers between the application itself and secure servers located in datacenter. In order to find the optimal set of parameters we employ bio-inspired algorithms such as Differential Evolution (DE) or Self Organizing Migrating Algorithm (SOMA). In this paper we present use of these algorithms, compare results and judge their performance in fault probability mitigation.

  9. Second kind Chebyshev operational matrix algorithm for solving differential equations of Lane-Emden type

    NASA Astrophysics Data System (ADS)

    Doha, E. H.; Abd-Elhameed, W. M.; Youssri, Y. H.

    2013-10-01

    In this paper, we present a new second kind Chebyshev (S2KC) operational matrix of derivatives. With the aid of S2KC, an algorithm is described to obtain numerical solutions of a class of linear and nonlinear Lane-Emden type singular initial value problems (IVPs). The idea of obtaining such solutions is essentially based on reducing the differential equation with its initial conditions to a system of algebraic equations. Two illustrative examples concern relevant physical problems (the Lane-Emden equations of the first and second kind) are discussed to demonstrate the validity and applicability of the suggested algorithm. Numerical results obtained are comparing favorably with the analytical known solutions.

  10. A comparison of various algorithms to extract Magic Formula tyre model coefficients for vehicle dynamics simulations

    NASA Astrophysics Data System (ADS)

    Vijay Alagappan, A.; Narasimha Rao, K. V.; Krishna Kumar, R.

    2015-02-01

    Tyre models are a prerequisite for any vehicle dynamics simulation. Tyre models range from the simplest mathematical models that consider only the cornering stiffness to a complex set of formulae. Among all the steady-state tyre models that are in use today, the Magic Formula tyre model is unique and most popular. Though the Magic Formula tyre model is widely used, obtaining the model coefficients from either the experimental or the simulation data is not straightforward due to its nonlinear nature and the presence of a large number of coefficients. A common procedure used for this extraction is the least-squares minimisation that requires considerable experience for initial guesses. Various researchers have tried different algorithms, namely, gradient and Newton-based methods, differential evolution, artificial neural networks, etc. The issues involved in all these algorithms are setting bounds or constraints, sensitivity of the parameters, the features of the input data such as the number of points, noisy data, experimental procedure used such as slip angle sweep or tyre measurement (TIME) procedure, etc. The extracted Magic Formula coefficients are affected by these variants. This paper highlights the issues that are commonly encountered in obtaining these coefficients with different algorithms, namely, least-squares minimisation using trust region algorithms, Nelder-Mead simplex, pattern search, differential evolution, particle swarm optimisation, cuckoo search, etc. A key observation is that not all the algorithms give the same Magic Formula coefficients for a given data. The nature of the input data and the type of the algorithm decide the set of the Magic Formula tyre model coefficients.

  11. The explicit computation of integration algorithms and first integrals for ordinary differential equations with polynomials coefficients using trees

    NASA Technical Reports Server (NTRS)

    Crouch, P. E.; Grossman, Robert

    1992-01-01

    This note is concerned with the explicit symbolic computation of expressions involving differential operators and their actions on functions. The derivation of specialized numerical algorithms, the explicit symbolic computation of integrals of motion, and the explicit computation of normal forms for nonlinear systems all require such computations. More precisely, if R = k(x(sub 1),...,x(sub N)), where k = R or C, F denotes a differential operator with coefficients from R, and g member of R, we describe data structures and algorithms for efficiently computing g. The basic idea is to impose a multiplicative structure on the vector space with basis the set of finite rooted trees and whose nodes are labeled with the coefficients of the differential operators. Cancellations of two trees with r + 1 nodes translates into cancellation of O(N(exp r)) expressions involving the coefficient functions and their derivatives.

  12. Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

    NASA Astrophysics Data System (ADS)

    Wang, Z.

    2015-12-01

    For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

  13. Parameter estimation by Differential Search Algorithm from horizontal loop electromagnetic (HLEM) data

    NASA Astrophysics Data System (ADS)

    Alkan, Hilal; Balkaya, Çağlayan

    2018-02-01

    We present an efficient inversion tool for parameter estimation from horizontal loop electromagnetic (HLEM) data using Differential Search Algorithm (DSA) which is a swarm-intelligence-based metaheuristic proposed recently. The depth, dip, and origin of a thin subsurface conductor causing the anomaly are the parameters estimated by the HLEM method commonly known as Slingram. The applicability of the developed scheme was firstly tested on two synthetically generated anomalies with and without noise content. Two control parameters affecting the convergence characteristic to the solution of the algorithm were tuned for the so-called anomalies including one and two conductive bodies, respectively. Tuned control parameters yielded more successful statistical results compared to widely used parameter couples in DSA applications. Two field anomalies measured over a dipping graphitic shale from Northern Australia were then considered, and the algorithm provided the depth estimations being in good agreement with those of previous studies and drilling information. Furthermore, the efficiency and reliability of the results obtained were investigated via probability density function. Considering the results obtained, we can conclude that DSA characterized by the simple algorithmic structure is an efficient and promising metaheuristic for the other relatively low-dimensional geophysical inverse problems. Finally, the researchers after being familiar with the content of developed scheme displaying an easy to use and flexible characteristic can easily modify and expand it for their scientific optimization problems.

  14. Effect of differential speed rolling on the texture evolution of Mg-4Zn-1Gd alloy

    NASA Astrophysics Data System (ADS)

    Shim, Myeong-Shik; Suh, Byeong-Chan; Kim, Jae H.; Kim, Nack J.

    2015-05-01

    The microstructural and texture evolution during differential speed rolling process of Mg 4Zn-1Gd (wt%) alloy have been investigated by means of electron backscatter diffraction observation and texture analysis. The angular distribution of basal poles are inclined about 10° from the normal direction towards the rolling direction and the maximum intensities of basal poles are decreased, compared to the conventional rolling process. Such an inclination of angular distribution of basal poles can be induced by the operation of shear stress along the rolling direction, as much as one quarter of tensile stress along the RD and one quarter of compressive stress along the ND. When the reduction ratios in differential speed rolling increase, there is no difference in texture evolution although there is a significant change in activated twinning systems. In addition, the engineering stresses after differential speed rolling are also similar to that after conventional rolling process, while ductility and stretch formability in the former are worse than those in the latter.

  15. Directed evolution of bacteriorhodopsin for applications in bioelectronics

    PubMed Central

    Wagner, Nicole L.; Greco, Jordan A.; Ranaghan, Matthew J.; Birge, Robert R.

    2013-01-01

    In nature, biological systems gradually evolve through complex, algorithmic processes involving mutation and differential selection. Evolution has optimized biological macromolecules for a variety of functions to provide a comparative advantage. However, nature does not optimize molecules for use in human-made devices, as it would gain no survival advantage in such cooperation. Recent advancements in genetic engineering, most notably directed evolution, have allowed for the stepwise manipulation of the properties of living organisms, promoting the expansion of protein-based devices in nanotechnology. In this review, we highlight the use of directed evolution to optimize photoactive proteins, with an emphasis on bacteriorhodopsin (BR), for device applications. BR, a highly stable light-activated proton pump, has shown great promise in three-dimensional optical memories, real-time holographic processors and artificial retinas. PMID:23676894

  16. Algorithm for Overcoming the Curse of Dimensionality for Certain Non-convex Hamilton-Jacobi Equations, Projections and Differential Games

    DTIC Science & Technology

    2016-05-01

    Algorithm for Overcoming the Curse of Dimensionality for Certain Non-convex Hamilton-Jacobi Equations, Projections and Differential Games Yat Tin...subproblems. Our approach is expected to have wide applications in continuous dynamic games , control theory problems, and elsewhere. Mathematics...differential dynamic games , control theory problems, and dynamical systems coming from the physical world, e.g. [11]. An important application is to

  17. Dynamical evolution of differentiated asteroid families

    NASA Astrophysics Data System (ADS)

    Martins-Filho, W. S.; Carvano, J.; Mothe-Diniz, T.; Roig, F.

    2014-10-01

    The project aims to study the dynamical evolution of a family of asteroids formed from a fully differentiated parent body, considering family members with different physical properties consistent with what is expected from the break up of a body formed by a metallic nucleus surrounded by a rocky mantle. Initially, we study the effects of variations in density, bond albedo, and thermal inertia in the semi-major axis drift caused by the Yarkovsky effect. The Yarkovsky effect is a non-conservative force caused by the thermal re-radiation of the solar radiation by an irregular body. In Solar System bodies, it is known to cause changes in the orbital motions (Peterson, 1976), eventually bringing asteroids into transport routes to near-Earth space, such as some mean motion resonances. We expressed the equations of variation of the semi-major axis directly in terms of physical properties (such as the mean motion, frequency of rotation, conductivity, thermal parameter, specific heat, obliquity and bond albedo). This development was based on the original formalism for the Yarkovsky effect (i.e., Bottke et al., 2006 and references therein). The derivation of above equations allowed us to closely study the variation of the semi-major axis individually for each physical parameter, clearly showing that the changes in semi-major axis for silicate bodies is twice or three times greater than for metal bodies. The next step was to calculate the orbital elements of a synthetic family after the break-up. That was accomplished assuming that the catastrophic disruption energy is given by the formalism described by Stewart and Leinhardt (2009) and assuming an isotropic distribution of velocities for the fragments of the nucleus and the mantle. Finally, the orbital evolution of the fragments is implemented using a simpletic integrator, and the result compared with the distribution of real asteroid families.

  18. On the Improvement of Convergence Performance for Integrated Design of Wind Turbine Blade Using a Vector Dominating Multi-objective Evolution Algorithm

    NASA Astrophysics Data System (ADS)

    Wang, L.; Wang, T. G.; Wu, J. H.; Cheng, G. P.

    2016-09-01

    A novel multi-objective optimization algorithm incorporating evolution strategies and vector mechanisms, referred as VD-MOEA, is proposed and applied in aerodynamic- structural integrated design of wind turbine blade. In the algorithm, a set of uniformly distributed vectors is constructed to guide population in moving forward to the Pareto front rapidly and maintain population diversity with high efficiency. For example, two- and three- objective designs of 1.5MW wind turbine blade are subsequently carried out for the optimization objectives of maximum annual energy production, minimum blade mass, and minimum extreme root thrust. The results show that the Pareto optimal solutions can be obtained in one single simulation run and uniformly distributed in the objective space, maximally maintaining the population diversity. In comparison to conventional evolution algorithms, VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation for handling complex problems of multi-variables, multi-objectives and multi-constraints. This provides a reliable high-performance optimization approach for the aerodynamic-structural integrated design of wind turbine blade.

  19. Multi-Objective Differential Evolution for Voltage Security Constrained Optimal Power Flow in Deregulated Power Systems

    NASA Astrophysics Data System (ADS)

    Roselyn, J. Preetha; Devaraj, D.; Dash, Subhransu Sekhar

    2013-11-01

    Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal

  20. A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

    NASA Astrophysics Data System (ADS)

    Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue

    2016-01-01

    A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

  1. Experiments with a Parallel Multi-Objective Evolutionary Algorithm for Scheduling

    NASA Technical Reports Server (NTRS)

    Brown, Matthew; Johnston, Mark D.

    2013-01-01

    Evolutionary multi-objective algorithms have great potential for scheduling in those situations where tradeoffs among competing objectives represent a key requirement. One challenge, however, is runtime performance, as a consequence of evolving not just a single schedule, but an entire population, while attempting to sample the Pareto frontier as accurately and uniformly as possible. The growing availability of multi-core processors in end user workstations, and even laptops, has raised the question of the extent to which such hardware can be used to speed up evolutionary algorithms. In this paper we report on early experiments in parallelizing a Generalized Differential Evolution (GDE) algorithm for scheduling long-range activities on NASA's Deep Space Network. Initial results show that significant speedups can be achieved, but that performance does not necessarily improve as more cores are utilized. We describe our preliminary results and some initial suggestions from parallelizing the GDE algorithm. Directions for future work are outlined.

  2. SMACK: A New Algorithm for Modeling Collisions and Dynamics of Planetesimals in Debris Disks

    NASA Technical Reports Server (NTRS)

    Nesvold, Erika Rose; Kuchner, Marc J.; Rein, Hanno; Pan, Margaret

    2013-01-01

    We present the Superparticle Model/Algorithm for Collisions in Kuiper belts and debris disks (SMACK), a new method for simultaneously modeling, in 3-D, the collisional and dynamical evolution of planetesimals in a debris disk with planets. SMACK can simulate azimuthal asymmetries and how these asymmetries evolve over time. We show that SMACK is stable to numerical viscosity and numerical heating over 10(exp 7) yr, and that it can reproduce analytic models of disk evolution. We use SMACK to model the evolution of a debris ring containing a planet on an eccentric orbit. Differential precession creates a spiral structure as the ring evolves, but collisions subsequently break up the spiral, leaving a narrower eccentric ring.

  3. A dynamical regularization algorithm for solving inverse source problems of elliptic partial differential equations

    NASA Astrophysics Data System (ADS)

    Zhang, Ye; Gong, Rongfang; Cheng, Xiaoliang; Gulliksson, Mårten

    2018-06-01

    This study considers the inverse source problem for elliptic partial differential equations with both Dirichlet and Neumann boundary data. The unknown source term is to be determined by additional boundary conditions. Unlike the existing methods found in the literature, which usually employ the first-order in time gradient-like system (such as the steepest descent methods) for numerically solving the regularized optimization problem with a fixed regularization parameter, we propose a novel method with a second-order in time dissipative gradient-like system and a dynamical selected regularization parameter. A damped symplectic scheme is proposed for the numerical solution. Theoretical analysis is given for both the continuous model and the numerical algorithm. Several numerical examples are provided to show the robustness of the proposed algorithm.

  4. THE DIFFERENTIAL ALGORITHM BETWEEN RHEUMATOLOGIC AND MALIGN DISEASES

    PubMed Central

    Këpuska, Arbnore Batalli; Spahiju, Lidvana; Bejiq, Ramush; Manqestena, Rufadije; Stavileci, Valbona; Ibraimi, Zana

    2016-01-01

    Objective: The aim of this study is to determine the differential algorithm between rheumatism and malignant diseases. For every pediatrician, to be warned when attending joint pain and child arthralgia and prevent and treat within time malignant diseases. Methods: Our case presented in Pediatric Clinic, was referred by Regional Hospital of Ferizaj with suspected diagnose of Febris Rheumatica and Arthralgia. The main complaint was joint pain. Initially the patient was admitted at Cardiology and Rheumatology department. Then after examinations was referred to Hemato-Oncology department. Hospitalized during the period from 12.12.2014 to 18.01.2015. Results: Bone marrow biopsy as terminal diagnostic tool reviled severe malignant hematologic disease, which was masked by clinical and lab findings as Febris Rheumatica. Conclusion: Arthralgia as one of child’s often complain, should have a special attention paid to, as it might be a warning sign for a lot of diseases. Steroid treatment should not be used before final diagnose of the disease and before rolling out hematologic etiology with peripheral blood smear. PMID:27147926

  5. An optimized treatment for algorithmic differentiation of an important glaciological fixed-point problem

    DOE PAGES

    Goldberg, Daniel N.; Narayanan, Sri Hari Krishna; Hascoet, Laurent; ...

    2016-05-20

    We apply an optimized method to the adjoint generation of a time-evolving land ice model through algorithmic differentiation (AD). The optimization involves a special treatment of the fixed-point iteration required to solve the nonlinear stress balance, which differs from a straightforward application of AD software, and leads to smaller memory requirements and in some cases shorter computation times of the adjoint. The optimization is done via implementation of the algorithm of Christianson (1994) for reverse accumulation of fixed-point problems, with the AD tool OpenAD. For test problems, the optimized adjoint is shown to have far lower memory requirements, potentially enablingmore » larger problem sizes on memory-limited machines. In the case of the land ice model, implementation of the algorithm allows further optimization by having the adjoint model solve a sequence of linear systems with identical (as opposed to varying) matrices, greatly improving performance. Finally, the methods introduced here will be of value to other efforts applying AD tools to ice models, particularly ones which solve a hybrid shallow ice/shallow shelf approximation to the Stokes equations.« less

  6. Irrigation water allocation optimization using multi-objective evolutionary algorithm (MOEA) - a review

    NASA Astrophysics Data System (ADS)

    Fanuel, Ibrahim Mwita; Mushi, Allen; Kajunguri, Damian

    2018-03-01

    This paper analyzes more than 40 papers with a restricted area of application of Multi-Objective Genetic Algorithm, Non-Dominated Sorting Genetic Algorithm-II and Multi-Objective Differential Evolution (MODE) to solve the multi-objective problem in agricultural water management. The paper focused on different application aspects which include water allocation, irrigation planning, crop pattern and allocation of available land. The performance and results of these techniques are discussed. The review finds that there is a potential to use MODE to analyzed the multi-objective problem, the application is more significance due to its advantage of being simple and powerful technique than any Evolutionary Algorithm. The paper concludes with the hopeful new trend of research that demand effective use of MODE; inclusion of benefits derived from farm byproducts and production costs into the model.

  7. Accurate Singular Values and Differential QD Algorithms

    DTIC Science & Technology

    1992-07-01

    of the Cholesky Algorithm 5 4 The Quotient Difference Algorithm 8 5 Incorporation of Shifts 11 5.1 Shifted qd Algorithms...Effects of Finite Precision 18 7.1 Error Analysis - Overview ........ ........................... 18 7.2 High Relative Accuracy in the Presence of...showing that it was preferable to replace the DK zero-shift QR transform by two steps of zero-shift LR implemented in a qd (quotient- difference ) format

  8. Speedup for quantum optimal control from automatic differentiation based on graphics processing units

    NASA Astrophysics Data System (ADS)

    Leung, Nelson; Abdelhafez, Mohamed; Koch, Jens; Schuster, David

    2017-04-01

    We implement a quantum optimal control algorithm based on automatic differentiation and harness the acceleration afforded by graphics processing units (GPUs). Automatic differentiation allows us to specify advanced optimization criteria and incorporate them in the optimization process with ease. We show that the use of GPUs can speedup calculations by more than an order of magnitude. Our strategy facilitates efficient numerical simulations on affordable desktop computers and exploration of a host of optimization constraints and system parameters relevant to real-life experiments. We demonstrate optimization of quantum evolution based on fine-grained evaluation of performance at each intermediate time step, thus enabling more intricate control on the evolution path, suppression of departures from the truncated model subspace, as well as minimization of the physical time needed to perform high-fidelity state preparation and unitary gates.

  9. Multiple Convective Cell Identification and Tracking Algorithm for documenting time-height evolution of measured polarimetric radar and lightning properties

    NASA Astrophysics Data System (ADS)

    Rosenfeld, D.; Hu, J.; Zhang, P.; Snyder, J.; Orville, R. E.; Ryzhkov, A.; Zrnic, D.; Williams, E.; Zhang, R.

    2017-12-01

    A methodology to track the evolution of the hydrometeors and electrification of convective cells is presented and applied to various convective clouds from warm showers to super-cells. The input radar data are obtained from the polarimetric NEXRAD weather radars, The information on cloud electrification is obtained from Lightning Mapping Arrays (LMA). The development time and height of the hydrometeors and electrification requires tracking the evolution and lifecycle of convective cells. A new methodology for Multi-Cell Identification and Tracking (MCIT) is presented in this study. This new algorithm is applied to time series of radar volume scans. A cell is defined as a local maximum in the Vertical Integrated Liquid (VIL), and the echo area is divided between cells using a watershed algorithm. The tracking of the cells between radar volume scans is done by identifying the two cells in consecutive radar scans that have maximum common VIL. The vertical profile of the polarimetric radar properties are used for constructing the time-height cross section of the cell properties around the peak reflectivity as a function of height. The LMA sources that occur within the cell area are integrated as a function of height as well for each time step, as determined by the radar volume scans. The result of the tracking can provide insights to the evolution of storms, hydrometer types, precipitation initiation and cloud electrification under different thermodynamic, aerosol and geographic conditions. The details of the MCIT algorithm, its products and their performance for different types of storm are described in this poster.

  10. Algorithm for Stabilizing a POD-Based Dynamical System

    NASA Technical Reports Server (NTRS)

    Kalb, Virginia L.

    2010-01-01

    This algorithm provides a new way to improve the accuracy and asymptotic behavior of a low-dimensional system based on the proper orthogonal decomposition (POD). Given a data set representing the evolution of a system of partial differential equations (PDEs), such as the Navier-Stokes equations for incompressible flow, one may obtain a low-dimensional model in the form of ordinary differential equations (ODEs) that should model the dynamics of the flow. Temporal sampling of the direct numerical simulation of the PDEs produces a spatial time series. The POD extracts the temporal and spatial eigenfunctions of this data set. Truncated to retain only the most energetic modes followed by Galerkin projection of these modes onto the PDEs obtains a dynamical system of ordinary differential equations for the time-dependent behavior of the flow. In practice, the steps leading to this system of ODEs entail numerically computing first-order derivatives of the mean data field and the eigenfunctions, and the computation of many inner products. This is far from a perfect process, and often results in the lack of long-term stability of the system and incorrect asymptotic behavior of the model. This algorithm describes a new stabilization method that utilizes the temporal eigenfunctions to derive correction terms for the coefficients of the dynamical system to significantly reduce these errors.

  11. Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan

    2006-01-01

    Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more

  12. Selfish Gene Algorithm Vs Genetic Algorithm: A Review

    NASA Astrophysics Data System (ADS)

    Ariff, Norharyati Md; Khalid, Noor Elaiza Abdul; Hashim, Rathiah; Noor, Noorhayati Mohamed

    2016-11-01

    Evolutionary algorithm is one of the algorithms inspired by the nature. Within little more than a decade hundreds of papers have reported successful applications of EAs. In this paper, the Selfish Gene Algorithms (SFGA), as one of the latest evolutionary algorithms (EAs) inspired from the Selfish Gene Theory which is an interpretation of Darwinian Theory ideas from the biologist Richards Dawkins on 1989. In this paper, following a brief introduction to the Selfish Gene Algorithm (SFGA), the chronology of its evolution is presented. It is the purpose of this paper is to present an overview of the concepts of Selfish Gene Algorithm (SFGA) as well as its opportunities and challenges. Accordingly, the history, step involves in the algorithm are discussed and its different applications together with an analysis of these applications are evaluated.

  13. Confident difference criterion: a new Bayesian differentially expressed gene selection algorithm with applications.

    PubMed

    Yu, Fang; Chen, Ming-Hui; Kuo, Lynn; Talbott, Heather; Davis, John S

    2015-08-07

    Recently, the Bayesian method becomes more popular for analyzing high dimensional gene expression data as it allows us to borrow information across different genes and provides powerful estimators for evaluating gene expression levels. It is crucial to develop a simple but efficient gene selection algorithm for detecting differentially expressed (DE) genes based on the Bayesian estimators. In this paper, by extending the two-criterion idea of Chen et al. (Chen M-H, Ibrahim JG, Chi Y-Y. A new class of mixture models for differential gene expression in DNA microarray data. J Stat Plan Inference. 2008;138:387-404), we propose two new gene selection algorithms for general Bayesian models and name these new methods as the confident difference criterion methods. One is based on the standardized differences between two mean expression values among genes; the other adds the differences between two variances to it. The proposed confident difference criterion methods first evaluate the posterior probability of a gene having different gene expressions between competitive samples and then declare a gene to be DE if the posterior probability is large. The theoretical connection between the proposed first method based on the means and the Bayes factor approach proposed by Yu et al. (Yu F, Chen M-H, Kuo L. Detecting differentially expressed genes using alibrated Bayes factors. Statistica Sinica. 2008;18:783-802) is established under the normal-normal-model with equal variances between two samples. The empirical performance of the proposed methods is examined and compared to those of several existing methods via several simulations. The results from these simulation studies show that the proposed confident difference criterion methods outperform the existing methods when comparing gene expressions across different conditions for both microarray studies and sequence-based high-throughput studies. A real dataset is used to further demonstrate the proposed methodology. In the real

  14. Leading-Color Fully Differential Two-Loop Soft Corrections to QCD Dipole Showers

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

    Dulat, Falko; Höche, Stefan; Prestel, Stefan

    We compute the next-to-leading order corrections to soft-gluon radiation differentially in the one-emission phase space. We show that their contribution to the evolution of color dipoles can be obtained in a modified subtraction scheme, such that both one- and two-emission terms are amenable to Monte-Carlo integration. The two-loop cusp anomalous dimension is recovered naturally upon integration over the full phase space. We present two independent implementations of the new algorithm in the two event generators Pythia and Sherpa, and we compare the resulting fully differential simulation to the CMW scheme.

  15. Parsing parallel evolution: ecological divergence and differential gene expression in the adaptive radiations of thick-lipped Midas cichlid fishes from Nicaragua.

    PubMed

    Manousaki, Tereza; Hull, Pincelli M; Kusche, Henrik; Machado-Schiaffino, Gonzalo; Franchini, Paolo; Harrod, Chris; Elmer, Kathryn R; Meyer, Axel

    2013-02-01

    The study of parallel evolution facilitates the discovery of common rules of diversification. Here, we examine the repeated evolution of thick lips in Midas cichlid fishes (the Amphilophus citrinellus species complex)-from two Great Lakes and two crater lakes in Nicaragua-to assess whether similar changes in ecology, phenotypic trophic traits and gene expression accompany parallel trait evolution. Using next-generation sequencing technology, we characterize transcriptome-wide differential gene expression in the lips of wild-caught sympatric thick- and thin-lipped cichlids from all four instances of repeated thick-lip evolution. Six genes (apolipoprotein D, myelin-associated glycoprotein precursor, four-and-a-half LIM domain protein 2, calpain-9, GTPase IMAP family member 8-like and one hypothetical protein) are significantly underexpressed in the thick-lipped morph across all four lakes. However, other aspects of lips' gene expression in sympatric morphs differ in a lake-specific pattern, including the magnitude of differentially expressed genes (97-510). Generally, fewer genes are differentially expressed among morphs in the younger crater lakes than in those from the older Great Lakes. Body shape, lower pharyngeal jaw size and shape, and stable isotopes (δ(13)C and δ(15)N) differ between all sympatric morphs, with the greatest differentiation in the Great Lake Nicaragua. Some ecological traits evolve in parallel (those related to foraging ecology; e.g. lip size, body and head shape) but others, somewhat surprisingly, do not (those related to diet and food processing; e.g. jaw size and shape, stable isotopes). Taken together, this case of parallelism among thick- and thin-lipped cichlids shows a mosaic pattern of parallel and nonparallel evolution. © 2012 Blackwell Publishing Ltd.

  16. Efficient Parallel Algorithms for Landscape Evolution Modelling

    NASA Astrophysics Data System (ADS)

    Moresi, L. N.; Mather, B.; Beucher, R.

    2017-12-01

    Landscape erosion and the deposition of sediments by river systems are strongly controlled bytopography, rainfall patterns, and the susceptibility of the basement to the action ofrunning water. It is well understood that each of these processes depends on the other, for example:topography results from active tectonic processes; deformation, metamorphosis andexhumation alter the competence of the basement; rainfall patterns depend on topography;uplift and subsidence in response to tectonic stress can be amplified by erosionand sediment deposition. We typically gain understanding of such coupled systems through forward models which capture theessential interactions of the various components and attempt parameterise those parts of the individual systemthat are unresolvable at the scale of the interaction. Here we address the problem of predicting erosion and deposition rates at a continental scalewith a resolution of tens to hundreds of metres in a dynamic, Lagrangian framework. This isa typical requirement for a code to interface with a mantle / lithosphere dynamics model anddemands an efficient, unstructured, parallel implementation. We address this through a very general algorithm that treats all parts of the landscape evolution equationsin sparse-matrix form including those for stream-flow accumulation, dam-filling and catchment determination. This givesus considerable flexibility in developing unstructured, parallel code, and in creating a modular packagethat can be configured by users to work at different temporal and spatial scales, but is also has potential advantagesin treating the non-linear parts of the problem in a general manner.

  17. Symbolic Solution of Linear Differential Equations

    NASA Technical Reports Server (NTRS)

    Feinberg, R. B.; Grooms, R. G.

    1981-01-01

    An algorithm for solving linear constant-coefficient ordinary differential equations is presented. The computational complexity of the algorithm is discussed and its implementation in the FORMAC system is described. A comparison is made between the algorithm and some classical algorithms for solving differential equations.

  18. New knowledge-based genetic algorithm for excavator boom structural optimization

    NASA Astrophysics Data System (ADS)

    Hua, Haiyan; Lin, Shuwen

    2014-03-01

    Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

  19. Improved artificial bee colony algorithm for wavefront sensor-less system in free space optical communication

    NASA Astrophysics Data System (ADS)

    Niu, Chaojun; Han, Xiang'e.

    2015-10-01

    Adaptive optics (AO) technology is an effective way to alleviate the effect of turbulence on free space optical communication (FSO). A new adaptive compensation method can be used without a wave-front sensor. Artificial bee colony algorithm (ABC) is a population-based heuristic evolutionary algorithm inspired by the intelligent foraging behaviour of the honeybee swarm with the advantage of simple, good convergence rate, robust and less parameter setting. In this paper, we simulate the application of the improved ABC to correct the distorted wavefront and proved its effectiveness. Then we simulate the application of ABC algorithm, differential evolution (DE) algorithm and stochastic parallel gradient descent (SPGD) algorithm to the FSO system and analyze the wavefront correction capabilities by comparison of the coupling efficiency, the error rate and the intensity fluctuation in different turbulence before and after the correction. The results show that the ABC algorithm has much faster correction speed than DE algorithm and better correct ability for strong turbulence than SPGD algorithm. Intensity fluctuation can be effectively reduced in strong turbulence, but not so effective in week turbulence.

  20. An efficient algorithm for global periodic orbits generation near irregular-shaped asteroids

    NASA Astrophysics Data System (ADS)

    Shang, Haibin; Wu, Xiaoyu; Ren, Yuan; Shan, Jinjun

    2017-07-01

    Periodic orbits (POs) play an important role in understanding dynamical behaviors around natural celestial bodies. In this study, an efficient algorithm was presented to generate the global POs around irregular-shaped uniformly rotating asteroids. The algorithm was performed in three steps, namely global search, local refinement, and model continuation. First, a mascon model with a low number of particles and optimized mass distribution was constructed to remodel the exterior gravitational potential of the asteroid. Using this model, a multi-start differential evolution enhanced with a deflection strategy with strong global exploration and bypassing abilities was adopted. This algorithm can be regarded as a search engine to find multiple globally optimal regions in which potential POs were located. This was followed by applying a differential correction to locally refine global search solutions and generate the accurate POs in the mascon model in which an analytical Jacobian matrix was derived to improve convergence. Finally, the concept of numerical model continuation was introduced and used to convert the POs from the mascon model into a high-fidelity polyhedron model by sequentially correcting the initial states. The efficiency of the proposed algorithm was substantiated by computing the global POs around an elongated shoe-shaped asteroid 433 Eros. Various global POs with different topological structures in the configuration space were successfully located. Specifically, the proposed algorithm was generic and could be conveniently extended to explore periodic motions in other gravitational systems.

  1. Sensitivity derivatives for advanced CFD algorithm and viscous modelling parameters via automatic differentiation

    NASA Technical Reports Server (NTRS)

    Green, Lawrence L.; Newman, Perry A.; Haigler, Kara J.

    1993-01-01

    The computational technique of automatic differentiation (AD) is applied to a three-dimensional thin-layer Navier-Stokes multigrid flow solver to assess the feasibility and computational impact of obtaining exact sensitivity derivatives typical of those needed for sensitivity analyses. Calculations are performed for an ONERA M6 wing in transonic flow with both the Baldwin-Lomax and Johnson-King turbulence models. The wing lift, drag, and pitching moment coefficients are differentiated with respect to two different groups of input parameters. The first group consists of the second- and fourth-order damping coefficients of the computational algorithm, whereas the second group consists of two parameters in the viscous turbulent flow physics modelling. Results obtained via AD are compared, for both accuracy and computational efficiency with the results obtained with divided differences (DD). The AD results are accurate, extremely simple to obtain, and show significant computational advantage over those obtained by DD for some cases.

  2. Three-phase Interstellar Medium in Galaxies Resolving Evolution with Star Formation and Supernova Feedback (TIGRESS): Algorithms, Fiducial Model, and Convergence

    NASA Astrophysics Data System (ADS)

    Kim, Chang-Goo; Ostriker, Eve C.

    2017-09-01

    We introduce TIGRESS, a novel framework for multi-physics numerical simulations of the star-forming interstellar medium (ISM) implemented in the Athena MHD code. The algorithms of TIGRESS are designed to spatially and temporally resolve key physical features, including: (1) the gravitational collapse and ongoing accretion of gas that leads to star formation in clusters; (2) the explosions of supernovae (SNe), both near their progenitor birth sites and from runaway OB stars, with time delays relative to star formation determined by population synthesis; (3) explicit evolution of SN remnants prior to the onset of cooling, which leads to the creation of the hot ISM; (4) photoelectric heating of the warm and cold phases of the ISM that tracks the time-dependent ambient FUV field from the young cluster population; (5) large-scale galactic differential rotation, which leads to epicyclic motion and shears out overdense structures, limiting large-scale gravitational collapse; (6) accurate evolution of magnetic fields, which can be important for vertical support of the ISM disk as well as angular momentum transport. We present tests of the newly implemented physics modules, and demonstrate application of TIGRESS in a fiducial model representing the solar neighborhood environment. We use a resolution study to demonstrate convergence and evaluate the minimum resolution {{Δ }}x required to correctly recover several ISM properties, including the star formation rate, wind mass-loss rate, disk scale height, turbulent and Alfvénic velocity dispersions, and volume fractions of warm and hot phases. For the solar neighborhood model, all these ISM properties are converged at {{Δ }}x≤slant 8 {pc}.

  3. Learning partial differential equations via data discovery and sparse optimization

    NASA Astrophysics Data System (ADS)

    Schaeffer, Hayden

    2017-01-01

    We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

  4. Learning partial differential equations via data discovery and sparse optimization.

    PubMed

    Schaeffer, Hayden

    2017-01-01

    We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

  5. Learning partial differential equations via data discovery and sparse optimization

    PubMed Central

    2017-01-01

    We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection. PMID:28265183

  6. Quantum Adiabatic Algorithms and Large Spin Tunnelling

    NASA Technical Reports Server (NTRS)

    Boulatov, A.; Smelyanskiy, V. N.

    2003-01-01

    We provide a theoretical study of the quantum adiabatic evolution algorithm with different evolution paths proposed in this paper. The algorithm is applied to a random binary optimization problem (a version of the 3-Satisfiability problem) where the n-bit cost function is symmetric with respect to the permutation of individual bits. The evolution paths are produced, using the generic control Hamiltonians H (r) that preserve the bit symmetry of the underlying optimization problem. In the case where the ground state of H(0) coincides with the totally-symmetric state of an n-qubit system the algorithm dynamics is completely described in terms of the motion of a spin-n/2. We show that different control Hamiltonians can be parameterized by a set of independent parameters that are expansion coefficients of H (r) in a certain universal set of operators. Only one of these operators can be responsible for avoiding the tunnelling in the spin-n/2 system during the quantum adiabatic algorithm. We show that it is possible to select a coefficient for this operator that guarantees a polynomial complexity of the algorithm for all problem instances. We show that a successful evolution path of the algorithm always corresponds to the trajectory of a classical spin-n/2 and provide a complete characterization of such paths.

  7. Muscular MRI-based algorithm to differentiate inherited myopathies presenting with spinal rigidity.

    PubMed

    Tordjman, Mickael; Dabaj, Ivana; Laforet, Pascal; Felter, Adrien; Ferreiro, Ana; Biyoukar, Moustafa; Law-Ye, Bruno; Zanoteli, Edmar; Castiglioni, Claudia; Rendu, John; Beroud, Christophe; Chamouni, Alexandre; Richard, Pascale; Mompoint, Dominique; Quijano-Roy, Susana; Carlier, Robert-Yves

    2018-05-25

    Inherited myopathies are major causes of muscle atrophy and are often characterized by rigid spine syndrome, a clinical feature designating patients with early spinal contractures. We aim to present a decision algorithm based on muscular whole body magnetic resonance imaging (mWB-MRI) as a unique tool to orientate the diagnosis of each inherited myopathy long before the genetically confirmed diagnosis. This multicentre retrospective study enrolled 79 patients from referral centres in France, Brazil and Chile. The patients underwent 1.5-T or 3-T mWB-MRI. The protocol comprised STIR and T1 sequences in axial and coronal planes, from head to toe. All images were analyzed manually by multiple raters. Fatty muscle replacement was evaluated on mWB-MRI using both the Mercuri scale and statistical comparison based on the percentage of affected muscle. Between February 2005 and December 2015, 76 patients with genetically confirmed inherited myopathy were included. They were affected by Pompe disease or harbored mutations in RYR1, Collagen VI, LMNA, SEPN1, LAMA2 and MYH7 genes. Each myopathy had a specific pattern of affected muscles recognizable on mWB-MRI. This allowed us to create a novel decision algorithm for patients with rigid spine syndrome by segregating these signs. This algorithm was validated by five external evaluators on a cohort of seven patients with a diagnostic accuracy of 94.3% compared with the genetic diagnosis. We provide a novel decision algorithm based on muscle fat replacement graded on mWB-MRI that allows diagnosis and differentiation of inherited myopathies presenting with spinal rigidity. • Inherited myopathies are rare, diagnosis is challenging and genetic tests require specialized centres and often take years. • Inherited myopathies are often characterized by spinal rigidity. • Whole body magnetic resonance imaging is a unique tool to orientate the diagnosis of each inherited myopathy presenting with spinal rigidity. • Each inherited

  8. A multi-group firefly algorithm for numerical optimization

    NASA Astrophysics Data System (ADS)

    Tong, Nan; Fu, Qiang; Zhong, Caiming; Wang, Pengjun

    2017-08-01

    To solve the problem of premature convergence of firefly algorithm (FA), this paper analyzes the evolution mechanism of the algorithm, and proposes an improved Firefly algorithm based on modified evolution model and multi-group learning mechanism (IMGFA). A Firefly colony is divided into several subgroups with different model parameters. Within each subgroup, the optimal firefly is responsible for leading the others fireflies to implement the early global evolution, and establish the information mutual system among the fireflies. And then, each firefly achieves local search by following the brighter firefly in its neighbors. At the same time, learning mechanism among the best fireflies in various subgroups to exchange information can help the population to obtain global optimization goals more effectively. Experimental results verify the effectiveness of the proposed algorithm.

  9. 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.

  10. Sexual and reproductive behaviour of Drosophila melanogaster from a microclimatically interslope differentiated population of "Evolution Canyon" (Mount Carmel, Israel).

    PubMed

    Iliadi, K; Iliadi, N; Rashkovetsky, E; Minkov, I; Nevo, E; Korol, A

    2001-11-22

    The strong microscale interslope environmental differences in "Evolution Canyon" provide an excellent natural model for sympatric speciation. Our previous studies revealed significant slope-specific differences for a fitness complex of Drosophila. This complex involved either adaptation traits (tolerance to high temperature, different viability and longevity pattern) or behavioural differentiation, manifested in habitat choice and non-random mating. This remarkable differentiation has evolved despite a very small interslope distance (a few hundred metres only). Our hypothesis is that strong interslope microclimatic contrast caused differential selection for fitness-related traits accompanied by behavioural differentiation and reinforced by some sexual isolation, which started incipient speciation. Here we describe the results of a systematic analysis of sexual behaviour in a non-choice situation and several reproductive parameters of D. melanogaster populations from the opposite slopes of "Evolution Canyon". The evidence indicates that: (i) mate choice derives from differences in mating propensity and discrimination; (ii) females from the milder north-facing slope discriminate strongly against males of the opposite slope; (iii) both sexes of the south-facing slope display distinct reproductive and behavioural patterns with females showing increased fecundity, shorter time before remating and relatively higher receptivity, and males showing higher mating propensity. These patterns represent adaptive life strategies contributing to higher fitness.

  11. Differential Adhesion between Moving Particles as a Mechanism for the Evolution of Social Groups

    PubMed Central

    Garcia, Thomas; Brunnet, Leonardo Gregory; De Monte, Silvia

    2014-01-01

    The evolutionary stability of cooperative traits, that are beneficial to other individuals but costly to their carrier, is considered possible only through the establishment of a sufficient degree of assortment between cooperators. Chimeric microbial populations, characterized by simple interactions between unrelated individuals, restrain the applicability of standard mechanisms generating such assortment, in particular when cells disperse between successive reproductive events such as happens in Dicyostelids and Myxobacteria. In this paper, we address the evolutionary dynamics of a costly trait that enhances attachment to others as well as group cohesion. By modeling cells as self-propelled particles moving on a plane according to local interaction forces and undergoing cycles of aggregation, reproduction and dispersal, we show that blind differential adhesion provides a basis for assortment in the process of group formation. When reproductive performance depends on the social context of players, evolution by natural selection can lead to the success of the social trait, and to the concomitant emergence of sizeable groups. We point out the conditions on the microscopic properties of motion and interaction that make such evolutionary outcome possible, stressing that the advent of sociality by differential adhesion is restricted to specific ecological contexts. Moreover, we show that the aggregation process naturally implies the existence of non-aggregated particles, and highlight their crucial evolutionary role despite being largely neglected in theoretical models for the evolution of sociality. PMID:24586133

  12. Computations involving differential operators and their actions on functions

    NASA Technical Reports Server (NTRS)

    Crouch, Peter E.; Grossman, Robert; Larson, Richard

    1991-01-01

    The algorithms derived by Grossmann and Larson (1989) are further developed for rewriting expressions involving differential operators. The differential operators involved arise in the local analysis of nonlinear dynamical systems. These algorithms are extended in two different directions: the algorithms are generalized so that they apply to differential operators on groups and the data structures and algorithms are developed to compute symbolically the action of differential operators on functions. Both of these generalizations are needed for applications.

  13. A global optimization algorithm inspired in the behavior of selfish herds.

    PubMed

    Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

    2017-10-01

    In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. A retrieval algorithm of hydrometer profile for submillimeter-wave radiometer

    NASA Astrophysics Data System (ADS)

    Liu, Yuli; Buehler, Stefan; Liu, Heguang

    2017-04-01

    Vertical profiles of particle microphysics perform vital functions for the estimation of climatic feedback. This paper proposes a new algorithm to retrieve the profile of the parameters of the hydrometeor(i.e., ice, snow, rain, liquid cloud, graupel) based on passive submillimeter-wave measurements. These parameters include water content and particle size. The first part of the algorithm builds the database and retrieves the integrated quantities. Database is built up by Atmospheric Radiative Transfer Simulator(ARTS), which uses atmosphere data to simulate the corresponding brightness temperature. Neural network, trained by the precalculated database, is developed to retrieve the water path for each type of particles. The second part of the algorithm analyses the statistical relationship between water path and vertical parameters profiles. Based on the strong dependence existing between vertical layers in the profiles, Principal Component Analysis(PCA) technique is applied. The third part of the algorithm uses the forward model explicitly to retrieve the hydrometeor profiles. Cost function is calculated in each iteration, and Differential Evolution(DE) algorithm is used to adjust the parameter values during the evolutionary process. The performance of this algorithm is planning to be verified for both simulation database and measurement data, by retrieving profiles in comparison with the initial one. Results show that this algorithm has the ability to retrieve the hydrometeor profiles efficiently. The combination of ARTS and optimization algorithm can get much better results than the commonly used database approach. Meanwhile, the concept that ARTS can be used explicitly in the retrieval process shows great potential in providing solution to other retrieval problems.

  15. An efficient algorithm for sorting by block-interchanges and its application to the evolution of vibrio species.

    PubMed

    Lin, Ying Chih; Lu, Chin Lung; Chang, Hwan-You; Tang, Chuan Yi

    2005-01-01

    In the study of genome rearrangement, the block-interchanges have been proposed recently as a new kind of global rearrangement events affecting a genome by swapping two nonintersecting segments of any length. The so-called block-interchange distance problem, which is equivalent to the sorting-by-block-interchange problem, is to find a minimum series of block-interchanges for transforming one chromosome into another. In this paper, we study this problem by considering the circular chromosomes and propose a Omicron(deltan) time algorithm for solving it by making use of permutation groups in algebra, where n is the length of the circular chromosome and delta is the minimum number of block-interchanges required for the transformation, which can be calculated in Omicron(n) time in advance. Moreover, we obtain analogous results by extending our algorithm to linear chromosomes. Finally, we have implemented our algorithm and applied it to the circular genomic sequences of three human vibrio pathogens for predicting their evolutionary relationships. Consequently, our experimental results coincide with the previous ones obtained by others using a different comparative genomics approach, which implies that the block-interchange events seem to play a significant role in the evolution of vibrio species.

  16. Improved Differentiation of Streptococcus pneumoniae and Other S. mitis Group Streptococci by MALDI Biotyper Using an Improved MALDI Biotyper Database Content and a Novel Result Interpretation Algorithm.

    PubMed

    Harju, Inka; Lange, Christoph; Kostrzewa, Markus; Maier, Thomas; Rantakokko-Jalava, Kaisu; Haanperä, Marjo

    2017-03-01

    Reliable distinction of Streptococcus pneumoniae and viridans group streptococci is important because of the different pathogenic properties of these organisms. Differentiation between S. pneumoniae and closely related Sreptococcus mitis species group streptococci has always been challenging, even when using such modern methods as 16S rRNA gene sequencing or matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry. In this study, a novel algorithm combined with an enhanced database was evaluated for differentiation between S. pneumoniae and S. mitis species group streptococci. One hundred one clinical S. mitis species group streptococcal strains and 188 clinical S. pneumoniae strains were identified by both the standard MALDI Biotyper database alone and that combined with a novel algorithm. The database update from 4,613 strains to 5,627 strains drastically improved the differentiation of S. pneumoniae and S. mitis species group streptococci: when the new database version containing 5,627 strains was used, only one of the 101 S. mitis species group isolates was misidentified as S. pneumoniae , whereas 66 of them were misidentified as S. pneumoniae when the earlier 4,613-strain MALDI Biotyper database version was used. The updated MALDI Biotyper database combined with the novel algorithm showed even better performance, producing no misidentifications of the S. mitis species group strains as S. pneumoniae All S. pneumoniae strains were correctly identified as S. pneumoniae with both the standard MALDI Biotyper database and the standard MALDI Biotyper database combined with the novel algorithm. This new algorithm thus enables reliable differentiation between pneumococci and other S. mitis species group streptococci with the MALDI Biotyper. Copyright © 2017 American Society for Microbiology.

  17. Fast stochastic algorithm for simulating evolutionary population dynamics

    NASA Astrophysics Data System (ADS)

    Tsimring, Lev; Hasty, Jeff; Mather, William

    2012-02-01

    Evolution and co-evolution of ecological communities are stochastic processes often characterized by vastly different rates of reproduction and mutation and a coexistence of very large and very small sub-populations of co-evolving species. This creates serious difficulties for accurate statistical modeling of evolutionary dynamics. In this talk, we introduce a new exact algorithm for fast fully stochastic simulations of birth/death/mutation processes. It produces a significant speedup compared to the direct stochastic simulation algorithm in a typical case when the total population size is large and the mutation rates are much smaller than birth/death rates. We illustrate the performance of the algorithm on several representative examples: evolution on a smooth fitness landscape, NK model, and stochastic predator-prey system.

  18. Risk management algorithm for rear-side collision avoidance using a combined steering torque overlay and differential braking

    NASA Astrophysics Data System (ADS)

    Lee, Junyung; Yi, Kyongsu; Yoo, Hyunjae; Chong, Hyokjin; Ko, Bongchul

    2015-06-01

    This paper describes a risk management algorithm for rear-side collision avoidance. The proposed risk management algorithm consists of a supervisor and a coordinator. The supervisor is designed to monitor collision risks between the subject vehicle and approaching vehicle in the adjacent lane. An appropriate criterion of intervention, which satisfies high acceptance to drivers through the consideration of a realistic traffic, has been determined based on the analysis of the kinematics of the vehicles in longitudinal and lateral directions. In order to assist the driver actively and increase driver's safety, a coordinator is designed to combine lateral control using a steering torque overlay by motor-driven power steering and differential braking by vehicle stability control. In order to prevent the collision while limiting actuator's control inputs and vehicle dynamics to safe values for the assurance of the driver's comfort, the Lyapunov theory and linear matrix inequalities based optimisation methods have been used. The proposed risk management algorithm has been evaluated via simulation using CarSim and MATLAB/Simulink.

  19. On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.

    2004-01-01

    Differential Evolution (DE) is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. These approaches are implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

  20. A new three-dimensional manufacturing service composition method under various structures using improved Flower Pollination Algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Wenyu; Yang, Yushu; Zhang, Shuai; Yu, Dejian; Chen, Yong

    2018-05-01

    With the growing complexity of customer requirements and the increasing scale of manufacturing services, how to select and combine the single services to meet the complex demand of the customer has become a growing concern. This paper presents a new manufacturing service composition method to solve the multi-objective optimization problem based on quality of service (QoS). The proposed model not only presents different methods for calculating the transportation time and transportation cost under various structures but also solves the three-dimensional composition optimization problem, including service aggregation, service selection, and service scheduling simultaneously. Further, an improved Flower Pollination Algorithm (IFPA) is proposed to solve the three-dimensional composition optimization problem using a matrix-based representation scheme. The mutation operator and crossover operator of the Differential Evolution (DE) algorithm are also used to extend the basic Flower Pollination Algorithm (FPA) to improve its performance. Compared to Genetic Algorithm, DE, and basic FPA, the experimental results confirm that the proposed method demonstrates superior performance than other meta heuristic algorithms and can obtain better manufacturing service composition solutions.

  1. Algorithms, complexity, and the sciences

    PubMed Central

    Papadimitriou, Christos

    2014-01-01

    Algorithms, perhaps together with Moore’s law, compose the engine of the information technology revolution, whereas complexity—the antithesis of algorithms—is one of the deepest realms of mathematical investigation. After introducing the basic concepts of algorithms and complexity, and the fundamental complexity classes P (polynomial time) and NP (nondeterministic polynomial time, or search problems), we discuss briefly the P vs. NP problem. We then focus on certain classes between P and NP which capture important phenomena in the social and life sciences, namely the Nash equlibrium and other equilibria in economics and game theory, and certain processes in population genetics and evolution. Finally, an algorithm known as multiplicative weights update (MWU) provides an algorithmic interpretation of the evolution of allele frequencies in a population under sex and weak selection. All three of these equivalences are rife with domain-specific implications: The concept of Nash equilibrium may be less universal—and therefore less compelling—than has been presumed; selection on gene interactions may entail the maintenance of genetic variation for longer periods than selection on single alleles predicts; whereas MWU can be shown to maximize, for each gene, a convex combination of the gene’s cumulative fitness in the population and the entropy of the allele distribution, an insight that may be pertinent to the maintenance of variation in evolution. PMID:25349382

  2. Algorithm for predicting the evolution of series of dynamics of complex systems in solving information problems

    NASA Astrophysics Data System (ADS)

    Kasatkina, T. I.; Dushkin, A. V.; Pavlov, V. A.; Shatovkin, R. R.

    2018-03-01

    In the development of information, systems and programming to predict the series of dynamics, neural network methods have recently been applied. They are more flexible, in comparison with existing analogues and are capable of taking into account the nonlinearities of the series. In this paper, we propose a modified algorithm for predicting the series of dynamics, which includes a method for training neural networks, an approach to describing and presenting input data, based on the prediction by the multilayer perceptron method. To construct a neural network, the values of a series of dynamics at the extremum points and time values corresponding to them, formed based on the sliding window method, are used as input data. The proposed algorithm can act as an independent approach to predicting the series of dynamics, and be one of the parts of the forecasting system. The efficiency of predicting the evolution of the dynamics series for a short-term one-step and long-term multi-step forecast by the classical multilayer perceptron method and a modified algorithm using synthetic and real data is compared. The result of this modification was the minimization of the magnitude of the iterative error that arises from the previously predicted inputs to the inputs to the neural network, as well as the increase in the accuracy of the iterative prediction of the neural network.

  3. Embracing equifinality with efficiency: Limits of Acceptability sampling using the DREAM(LOA) algorithm

    NASA Astrophysics Data System (ADS)

    Vrugt, Jasper A.; Beven, Keith J.

    2018-04-01

    This essay illustrates some recent developments to the DiffeRential Evolution Adaptive Metropolis (DREAM) MATLAB toolbox of Vrugt (2016) to delineate and sample the behavioural solution space of set-theoretic likelihood functions used within the GLUE (Limits of Acceptability) framework (Beven and Binley, 1992, 2014; Beven and Freer, 2001; Beven, 2006). This work builds on the DREAM(ABC) algorithm of Sadegh and Vrugt (2014) and enhances significantly the accuracy and CPU-efficiency of Bayesian inference with GLUE. In particular it is shown how lack of adequate sampling in the model space might lead to unjustified model rejection.

  4. Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

    PubMed

    Stoll, Gautier; Viara, Eric; Barillot, Emmanuel; Calzone, Laurence

    2012-08-29

    Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time. There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature. Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential

  5. Thermal evolution and differentiation of planetesimals and planetary embryos

    NASA Astrophysics Data System (ADS)

    Šrámek, Ondřej; Milelli, Laura; Ricard, Yanick; Labrosse, Stéphane

    2012-01-01

    In early Solar System during the runaway growth stage of planetary formation, the distribution of planetary bodies progressively evolved from a large number of planetesimals to a smaller number of objects with a few dominant embryos. Here, we study the possible thermal and compositional evolution of these planetesimals and planetary embryos in a series of models with increasing complexities. We show that the heating stages of planetesimals by the radioactive decay of now extinct isotopes (in particular 26Al) and by impact heating can occur in two stages or simultaneously. Depending on the accretion rate, melting occurs from the center outward, in a shallow outer shell progressing inward, or in the two locations. We discuss the regime domains of these situations and show that the exponent β that controls the planetary growth rate R˙∝Rβ of planetesimals plays a crucial role. For a given terminal radius and accretion duration, the increase of β maintains the planetesimals very small until the end of accretion, and therefore allows radioactive heating to be radiated away before a large mass can be accreted. To melt the center of ˜500 km planetesimal during its runaway growth stage, with the value β = 2 predicted by astrophysicists, it needs to be formed within a couple of million years after condensation of the first solids. We then develop a multiphase model where the phase changes and phase separations by compaction are taken into account in 1-D spherical geometry. Our model handles simultaneously metal and silicates in both solid and liquid states. The segregation of the protocore decreases the efficiency of radiogenic heating by confining the 26Al in the outer silicate shell. Various types of planetesimals partly differentiated and sometimes differentiated in multiple metal-silicate layers can be obtained.

  6. A real-time algorithm for integrating differential satellite and inertial navigation information during helicopter approach. M.S. Thesis

    NASA Technical Reports Server (NTRS)

    Hoang, TY

    1994-01-01

    A real-time, high-rate precision navigation Kalman filter algorithm is developed and analyzed. This Navigation algorithm blends various navigation data collected during terminal area approach of an instrumented helicopter. Navigation data collected include helicopter position and velocity from a global position system in differential mode (DGPS) as well as helicopter velocity and attitude from an inertial navigation system (INS). The goal of the Navigation algorithm is to increase the DGPS accuracy while producing navigational data at the 64 Hertz INS update rate. It is important to note that while the data was post flight processed, the Navigation algorithm was designed for real-time analysis. The design of the Navigation algorithm resulted in a nine-state Kalman filter. The Kalman filter's state matrix contains position, velocity, and velocity bias components. The filter updates positional readings with DGPS position, INS velocity, and velocity bias information. In addition, the filter incorporates a sporadic data rejection scheme. This relatively simple model met and exceeded the ten meter absolute positional requirement. The Navigation algorithm results were compared with truth data derived from a laser tracker. The helicopter flight profile included terminal glideslope angles of 3, 6, and 9 degrees. Two flight segments extracted during each terminal approach were used to evaluate the Navigation algorithm. The first segment recorded small dynamic maneuver in the lateral plane while motion in the vertical plane was recorded by the second segment. The longitudinal, lateral, and vertical averaged positional accuracies for all three glideslope approaches are as follows (mean plus or minus two standard deviations in meters): longitudinal (-0.03 plus or minus 1.41), lateral (-1.29 plus or minus 2.36), and vertical (-0.76 plus or minus 2.05).

  7. [Asynchronous asymmetry (sexual and lateral differentiation--a consequence of asynchronous evolution)].

    PubMed

    Geodakian, V A

    1993-01-01

    In the paper is presented a unified interpretation of sex differentiation and lateral asymmetry as asynchronous evolution. The operative subsystems, i.e. the male and the left hemisphere of the brain evolutionize earlier than the conservative ones, i.e. the female and the right hemisphere. New functions appear at first in males and after many generations they are transferred to females. The leading centers of their control are at first originated in the left hemisphere, then they are translocated to the right one. The criterion for functions localization in the hemisphere is their evolutionary age: new functions are controlled by the left hemisphere, old functions by the right one. Therefore the left hemisphere is socio-cultural, ethnic, the right one is biological, special. The theory explains from a single standpoint the phenomena of sex, handedness, nervous crossover, as well as many know facts, and predicts the new ones.

  8. A hybrid algorithm for coupling partial differential equation and compartment-based dynamics.

    PubMed

    Harrison, Jonathan U; Yates, Christian A

    2016-09-01

    Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction-diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations (PDEs). Although the numerical solution of such PDEs is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not valid when copy numbers are low and stochastic effects dominate. Therefore, to take advantage of the benefits of both of these types of models, each of which may be appropriate in different parts of a spatial domain, we have developed an algorithm that can be used to couple these two types of model together. This hybrid coupling algorithm uses an overlap region between the two modelling regimes. By coupling fluxes at one end of the interface and using a concentration-matching condition at the other end, we ensure that mass is appropriately transferred between PDE- and compartment-based regimes. Our methodology gives notable reductions in simulation time in comparison with using a fully stochastic model, while maintaining the important stochastic features of the system and providing detail in appropriate areas of the domain. We test our hybrid methodology robustly by applying it to several biologically motivated problems including diffusion and morphogen gradient formation. Our analysis shows that the resulting error is small, unbiased and does not grow over time. © 2016 The Authors.

  9. A hybrid algorithm for coupling partial differential equation and compartment-based dynamics

    PubMed Central

    Yates, Christian A.

    2016-01-01

    Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction–diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations (PDEs). Although the numerical solution of such PDEs is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not valid when copy numbers are low and stochastic effects dominate. Therefore, to take advantage of the benefits of both of these types of models, each of which may be appropriate in different parts of a spatial domain, we have developed an algorithm that can be used to couple these two types of model together. This hybrid coupling algorithm uses an overlap region between the two modelling regimes. By coupling fluxes at one end of the interface and using a concentration-matching condition at the other end, we ensure that mass is appropriately transferred between PDE- and compartment-based regimes. Our methodology gives notable reductions in simulation time in comparison with using a fully stochastic model, while maintaining the important stochastic features of the system and providing detail in appropriate areas of the domain. We test our hybrid methodology robustly by applying it to several biologically motivated problems including diffusion and morphogen gradient formation. Our analysis shows that the resulting error is small, unbiased and does not grow over time. PMID:27628171

  10. 3D magnetization vector inversion based on fuzzy clustering: inversion algorithm, uncertainty analysis, and application to geology differentiation

    NASA Astrophysics Data System (ADS)

    Sun, J.; Li, Y.

    2017-12-01

    Magnetic data contain important information about the subsurface rocks that were magnetized in the geological history, which provides an important avenue to the study of the crustal heterogeneities associated with magmatic and hydrothermal activities. Interpretation of magnetic data has been widely used in mineral exploration, basement characterization and large scale crustal studies for several decades. However, interpreting magnetic data has been often complicated by the presence of remanent magnetizations with unknown magnetization directions. Researchers have developed different methods to deal with the challenges posed by remanence. We have developed a new and effective approach to inverting magnetic data for magnetization vector distributions characterized by region-wise consistency in the magnetization directions. This approach combines the classical Tikhonov inversion scheme with fuzzy C-means clustering algorithm, and constrains the estimated magnetization vectors to a specified small number of possible directions while fitting the observed magnetic data to within noise level. Our magnetization vector inversion recovers both the magnitudes and the directions of the magnetizations in the subsurface. Magnetization directions reflect the unique geological or hydrothermal processes applied to each geological unit, and therefore, can potentially be used for the purpose of differentiating various geological units. We have developed a practically convenient and effective way of assessing the uncertainty associated with the inverted magnetization directions (Figure 1), and investigated how geological differentiation results might be affected (Figure 2). The algorithm and procedures we have developed for magnetization vector inversion and uncertainty analysis open up new possibilities of extracting useful information from magnetic data affected by remanence. We will use a field data example from exploration of an iron-oxide-copper-gold (IOCG) deposit in Brazil to

  11. Adaptive grid based multi-objective Cauchy differential evolution for stochastic dynamic economic emission dispatch with wind power uncertainty

    PubMed Central

    Lei, Xiaohui; Wang, Chao; Yue, Dong; Xie, Xiangpeng

    2017-01-01

    Since wind power is integrated into the thermal power operation system, dynamic economic emission dispatch (DEED) has become a new challenge due to its uncertain characteristics. This paper proposes an adaptive grid based multi-objective Cauchy differential evolution (AGB-MOCDE) for solving stochastic DEED with wind power uncertainty. To properly deal with wind power uncertainty, some scenarios are generated to simulate those possible situations by dividing the uncertainty domain into different intervals, the probability of each interval can be calculated using the cumulative distribution function, and a stochastic DEED model can be formulated under different scenarios. For enhancing the optimization efficiency, Cauchy mutation operation is utilized to improve differential evolution by adjusting the population diversity during the population evolution process, and an adaptive grid is constructed for retaining diversity distribution of Pareto front. With consideration of large number of generated scenarios, the reduction mechanism is carried out to decrease the scenarios number with covariance relationships, which can greatly decrease the computational complexity. Moreover, the constraint-handling technique is also utilized to deal with the system load balance while considering transmission loss among thermal units and wind farms, all the constraint limits can be satisfied under the permitted accuracy. After the proposed method is simulated on three test systems, the obtained results reveal that in comparison with other alternatives, the proposed AGB-MOCDE can optimize the DEED problem while handling all constraint limits, and the optimal scheme of stochastic DEED can decrease the conservation of interval optimization, which can provide a more valuable optimal scheme for real-world applications. PMID:28961262

  12. High-order hydrodynamic algorithms for exascale computing

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

    Morgan, Nathaniel Ray

    Hydrodynamic algorithms are at the core of many laboratory missions ranging from simulating ICF implosions to climate modeling. The hydrodynamic algorithms commonly employed at the laboratory and in industry (1) typically lack requisite accuracy for complex multi- material vortical flows and (2) are not well suited for exascale computing due to poor data locality and poor FLOP/memory ratios. Exascale computing requires advances in both computer science and numerical algorithms. We propose to research the second requirement and create a new high-order hydrodynamic algorithm that has superior accuracy, excellent data locality, and excellent FLOP/memory ratios. This proposal will impact a broadmore » range of research areas including numerical theory, discrete mathematics, vorticity evolution, gas dynamics, interface instability evolution, turbulent flows, fluid dynamics and shock driven flows. If successful, the proposed research has the potential to radically transform simulation capabilities and help position the laboratory for computing at the exascale.« less

  13. Testing algorithms for critical slowing down

    NASA Astrophysics Data System (ADS)

    Cossu, Guido; Boyle, Peter; Christ, Norman; Jung, Chulwoo; Jüttner, Andreas; Sanfilippo, Francesco

    2018-03-01

    We present the preliminary tests on two modifications of the Hybrid Monte Carlo (HMC) algorithm. Both algorithms are designed to travel much farther in the Hamiltonian phase space for each trajectory and reduce the autocorrelations among physical observables thus tackling the critical slowing down towards the continuum limit. We present a comparison of costs of the new algorithms with the standard HMC evolution for pure gauge fields, studying the autocorrelation times for various quantities including the topological charge.

  14. Inverse problem of HIV cell dynamics using Genetic Algorithms

    NASA Astrophysics Data System (ADS)

    González, J. A.; Guzmán, F. S.

    2017-01-01

    In order to describe the cell dynamics of T-cells in a patient infected with HIV, we use a flavour of Perelson's model. This is a non-linear system of Ordinary Differential Equations that describes the evolution of healthy, latently infected, infected T-cell concentrations and the free viral cells. Different parameters in the equations give different dynamics. Considering the concentration of these types of cells is known for a particular patient, the inverse problem consists in estimating the parameters in the model. We solve this inverse problem using a Genetic Algorithm (GA) that minimizes the error between the solutions of the model and the data from the patient. These errors depend on the parameters of the GA, like mutation rate and population, although a detailed analysis of this dependence will be described elsewhere.

  15. Noise-enhanced clustering and competitive learning algorithms.

    PubMed

    Osoba, Osonde; Kosko, Bart

    2013-01-01

    Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. An Algorithm of Image Heterogeneity with Contrast-Enhanced Ultrasound in Differential Diagnosis of Solid Thyroid Nodules.

    PubMed

    Jin, Lifang; Xu, Changsong; Xie, Xueqian; Li, Fan; Lv, Xiuhong; Du, Lianfang

    2017-01-01

    Enhancement heterogeneity on contrast-enhanced ultrasonography (CEUS) is used to differentiate between benign and malignant thyroid nodules. In this study, we used an algorithm to quantify enhancement heterogeneity of solid thyroid nodules on CEUS. The heterogeneity value (HV) is calculated as standard deviation/mean intensity × 100 (using Adobe Photoshop). The heterogeneity ratio (HR) is calculated as the ratio of the HV of the nodule to that of the surrounding parenchyma. Three phases-ascending, peak and descending phases-were studied. HV values at ascending (HV a ) and peak (HV p ) phases were significantly higher in malignant nodules than in benign nodules (95.57 ± 43.87 vs. 73.06 ± 44.04, p = 0.009, and 32.53 ± 10.73 vs. 26.44 ± 8.25, p = 0.002, respectively). HR a , HR p and HR d were significantly higher in malignant nodules than in benign nodules (1.93 ± 1.03 vs. 1.00 ± 0.47, p = 0.000, 1.43 ± 0.51 vs. 1.09 ± 0.28, p = 0.000, and 1.33 ± 0.40 vs. 1.08 ± 0.33, p = 0.001, respectively). HR a achieved optimal diagnostic performance on receiver operating characteristic curve analysis. The algorithm used for assessment of image heterogeneity on CEUS examination may be a useful adjunct to conventional ultrasound for differential diagnosis of solid thyroid nodules. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

  17. Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.

    PubMed

    Chen, Chi-Kan

    2017-07-26

    -step algorithms can potentially incorporate with different nonlinear differential equation models to reconstruct the GRN.

  18. High-order Newton-penalty algorithms

    NASA Astrophysics Data System (ADS)

    Dussault, Jean-Pierre

    2005-10-01

    Recent efforts in differentiable non-linear programming have been focused on interior point methods, akin to penalty and barrier algorithms. In this paper, we address the classical equality constrained program solved using the simple quadratic loss penalty function/algorithm. The suggestion to use extrapolations to track the differentiable trajectory associated with penalized subproblems goes back to the classic monograph of Fiacco & McCormick. This idea was further developed by Gould who obtained a two-steps quadratically convergent algorithm using prediction steps and Newton correction. Dussault interpreted the prediction step as a combined extrapolation with respect to the penalty parameter and the residual of the first order optimality conditions. Extrapolation with respect to the residual coincides with a Newton step.We explore here higher-order extrapolations, thus higher-order Newton-like methods. We first consider high-order variants of the Newton-Raphson method applied to non-linear systems of equations. Next, we obtain improved asymptotic convergence results for the quadratic loss penalty algorithm by using high-order extrapolation steps.

  19. Thermal Evolution of Charon and the Major Satellites of Uranus: Constraints on Early Differentiation

    NASA Astrophysics Data System (ADS)

    Spohn, T.; Multhaup, K.

    2007-12-01

    A thermal history model developed for medium-sized icy satellites containing silicate rock at low volume fractions is applied to Charon and the satellites of Uranus Ariel, Umbriel, Titania, Oberon and Miranda. The model assumes homogeneously accreted satellites. To calculate the initial temperature profile we assume that infalling planetesimals deposit a fraction h of their kinetic energy as heat at the instantaneous surface of the growing satellites. The parameter h is varied between models. The model continuously checks for convectively unstable shells in the interior by updating the temperature profile and calculating the Rayleigh number and the temperature-dependent viscosity. The viscosity parameter values are taken as those of ice I although the satellites under consideration likely contain admixtures of lighter constituents. Their effects and those of rock on the viscosity are discussed. Convective heat transport is calculated assuming the stagnant lid model for strongly temperature dependent viscosity. In convectively stable regions heat transfer is by conduction with a temperature dependent thermal conductivity. Thermal evolution calculations considering radiogenic heating by the long-lived radiogenic isotopes of U, Th, and K suggest that Ariel, Umbriel, Titania, Oberon and Charon may have started to differentiate after a few hundred million years of evolution. With short-lived isotopes -- if present in sizeable concentrations -- this time will move earlier. Results for Miranda -- the smallest satellite of Uranus -- indicate that it never convected or differentiated if heated by the said long-lived isotopes only. Miranda's interior temperature was found to be not even close to the melting temperatures of reasonable mixtures of water and ammonia. This finding is in contrast to its heavily modified surface and supports theories that propose alternative heating mechanisms such as the decay of short-lived isotopes or early tidal heating.

  20. Day-Ahead PM2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution

    PubMed Central

    Wang, Deyun; Liu, Yanling; Luo, Hongyuan; Yue, Chenqiang; Cheng, Sheng

    2017-01-01

    Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by differential evolution (DE) algorithm. Firstly, WT is employed to disassemble the PM2.5 concentration series into a number of subsets with different frequencies. Secondly, VMD is applied to decompose each subset into a set of variational modes (VMs). Thirdly, DE-BP model is utilized to forecast all the VMs. Fourthly, the forecast value of each subset is obtained through aggregating the forecast results of all the VMs obtained from VMD decomposition of this subset. Finally, the final forecast series of PM2.5 concentration is obtained by adding up the forecast values of all subsets. Two PM2.5 concentration series collected from Wuhan and Tianjin, respectively, located in China are used to test the effectiveness of the proposed model. The results demonstrate that the proposed model outperforms all the other considered models in this paper. PMID:28704955

  1. Shift-connected SIMD array architectures for digital optical computing systems, with algorithms for numerical transforms and partial differential equations

    NASA Astrophysics Data System (ADS)

    Drabik, Timothy J.; Lee, Sing H.

    1986-11-01

    The intrinsic parallelism characteristics of easily realizable optical SIMD arrays prompt their present consideration in the implementation of highly structured algorithms for the numerical solution of multidimensional partial differential equations and the computation of fast numerical transforms. Attention is given to a system, comprising several spatial light modulators (SLMs), an optical read/write memory, and a functional block, which performs simple, space-invariant shifts on images with sufficient flexibility to implement the fastest known methods for partial differential equations as well as a wide variety of numerical transforms in two or more dimensions. Either fixed or floating-point arithmetic may be used. A performance projection of more than 1 billion floating point operations/sec using SLMs with 1000 x 1000-resolution and operating at 1-MHz frame rates is made.

  2. A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks.

    PubMed

    Zhang, Qingguo; Fok, Mable P

    2017-01-09

    Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate's target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate's target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage-distance rate and the number of moved mobile sensors, when compare with other approaches.

  3. From evolutionary computation to the evolution of things.

    PubMed

    Eiben, Agoston E; Smith, Jim

    2015-05-28

    Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

  4. Differentially Private Frequent Subgraph Mining

    PubMed Central

    Xu, Shengzhi; Xiong, Li; Cheng, Xiang; Xiao, Ke

    2016-01-01

    Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individual's privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies ε-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility. PMID:27616876

  5. Improving CMD Areal Density Analysis: Algorithms and Strategies

    NASA Astrophysics Data System (ADS)

    Wilson, R. E.

    2014-06-01

    Essential ideas, successes, and difficulties of Areal Density Analysis (ADA) for color-magnitude diagrams (CMD¡¯s) of resolved stellar populations are examined, with explanation of various algorithms and strategies for optimal performance. A CMDgeneration program computes theoretical datasets with simulated observational error and a solution program inverts the problem by the method of Differential Corrections (DC) so as to compute parameter values from observed magnitudes and colors, with standard error estimates and correlation coefficients. ADA promises not only impersonal results, but also significant saving of labor, especially where a given dataset is analyzed with several evolution models. Observational errors and multiple star systems, along with various single star characteristics and phenomena, are modeled directly via the Functional Statistics Algorithm (FSA). Unlike Monte Carlo, FSA is not dependent on a random number generator. Discussions include difficulties and overall requirements, such as need for fast evolutionary computation and realization of goals within machine memory limits. Degradation of results due to influence of pixelization on derivatives, Initial Mass Function (IMF) quantization, IMF steepness, low Areal Densities (A ), and large variation in A are reduced or eliminated through a variety of schemes that are explained sufficiently for general application. The Levenberg-Marquardt and MMS algorithms for improvement of solution convergence are contained within the DC program. An example of convergence, which typically is very good, is shown in tabular form. A number of theoretical and practical solution issues are discussed, as are prospects for further development.

  6. A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm

    NASA Astrophysics Data System (ADS)

    Thirer, Nonel

    2013-05-01

    With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.

  7. Bayesian phylogeny of sucrose transporters: ancient origins, differential expansion and convergent evolution in monocots and dicots

    PubMed Central

    Peng, Duo; Gu, Xi; Xue, Liang-Jiao; Leebens-Mack, James H.; Tsai, Chung-Jui

    2014-01-01

    Sucrose transporters (SUTs) are essential for the export and efficient movement of sucrose from source leaves to sink organs in plants. The angiosperm SUT family was previously classified into three or four distinct groups, Types I, II (subgroup IIB), and III, with dicot-specific Type I and monocot-specific Type IIB functioning in phloem loading. To shed light on the underlying drivers of SUT evolution, Bayesian phylogenetic inference was undertaken using 41 sequenced plant genomes, including seven basal lineages at key evolutionary junctures. Our analysis supports four phylogenetically and structurally distinct SUT subfamilies, originating from two ancient groups (AG1 and AG2) that diverged early during terrestrial colonization. In both AG1 and AG2, multiple intron acquisition events in the progenitor vascular plant established the gene structures of modern SUTs. Tonoplastic Type III and plasmalemmal Type II represent evolutionarily conserved descendants of AG1 and AG2, respectively. Type I and Type IIB were previously thought to evolve after the dicot-monocot split. We show, however, that divergence of Type I from Type III SUT predated basal angiosperms, likely associated with evolution of vascular cambium and phloem transport. Type I SUT was subsequently lost in monocots along with vascular cambium, and independent evolution of Type IIB coincided with modified monocot vasculature. Both Type I and Type IIB underwent lineage-specific expansion. In multiple unrelated taxa, the newly-derived SUTs exhibit biased expression in reproductive tissues, suggesting a functional link between phloem loading and reproductive fitness. Convergent evolution of Type I and Type IIB for SUT function in phloem loading and reproductive organs supports the idea that differential vascular development in dicots and monocots is a strong driver for SUT family evolution in angiosperms. PMID:25429293

  8. Effect of deformation path on microstructure, microhardness and texture evolution of interstitial free steel fabricated by differential speed rolling

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

    Hamad, Kotiba; Chung, Bong Kwon; Ko, Young Gun, E-mail: younggun@ynu.ac.kr

    2014-08-15

    This paper reports the effect of the deformation path on the microstructure, microhardness, and texture evolution of interstitial free (IF) steel processed by differential speed rolling (DSR) method. For this purpose, total height reductions of 50% and 75% were imposed on the samples by a series of differential speed rolling operations with various height reductions per pass (deformation levels) ranging from 10 to 50% under a fixed roll speed ratio of 1:4 for the upper and lower rolls, respectively. Microstructural observations using transmission electron microscopy and electron backscattered diffraction measurements showed that the samples rolled at deformation level of 50%more » had the finest mean grain size (∼ 0.5 μm) compared to the other counterparts; also the samples rolled at deformation level of 50% showed a more uniform microstructure. Based on the microhardness measurements along the thickness direction of the deformed samples, gradual evolution of the microhardness value and its homogeneity was observed with the increase of the deformation level per pass. Texture analysis showed that, as the deformation level per pass increased, the fraction of alpha fiber and gamma fiber in the deformed samples increased. The textures obtained by the differential speed rolling process under the lubricated condition would be equivalent to those obtained by the conventional rolling. - Highlights: • Effect of DSR deformation path on microstructure of IF steel is significant. • IF steel rolled at deformation level of 50% has the ultrafine grains of ∼ 0.5 μm. • Rolling texture components are pronounced with increasing deformation level.« less

  9. Myeloma Cell Dynamics in Response to Treatment Supports a Model of Hierarchical Differentiation and Clonal Evolution.

    PubMed

    Tang, Min; Zhao, Rui; van de Velde, Helgi; Tross, Jennifer G; Mitsiades, Constantine; Viselli, Suzanne; Neuwirth, Rachel; Esseltine, Dixie-Lee; Anderson, Kenneth; Ghobrial, Irene M; San Miguel, Jesús F; Richardson, Paul G; Tomasson, Michael H; Michor, Franziska

    2016-08-15

    Since the pioneering work of Salmon and Durie, quantitative measures of tumor burden in multiple myeloma have been used to make clinical predictions and model tumor growth. However, such quantitative analyses have not yet been performed on large datasets from trials using modern chemotherapy regimens. We analyzed a large set of tumor response data from three randomized controlled trials of bortezomib-based chemotherapy regimens (total sample size n = 1,469 patients) to establish and validate a novel mathematical model of multiple myeloma cell dynamics. Treatment dynamics in newly diagnosed patients were most consistent with a model postulating two tumor cell subpopulations, "progenitor cells" and "differentiated cells." Differential treatment responses were observed with significant tumoricidal effects on differentiated cells and less clear effects on progenitor cells. We validated this model using a second trial of newly diagnosed patients and a third trial of refractory patients. When applying our model to data of relapsed patients, we found that a hybrid model incorporating both a differentiation hierarchy and clonal evolution best explains the response patterns. The clinical data, together with mathematical modeling, suggest that bortezomib-based therapy exerts a selection pressure on myeloma cells that can shape the disease phenotype, thereby generating further inter-patient variability. This model may be a useful tool for improving our understanding of disease biology and the response to chemotherapy regimens. Clin Cancer Res; 22(16); 4206-14. ©2016 AACR. ©2016 American Association for Cancer Research.

  10. Size evolution of ultrafine particles: Differential signatures of normal and episodic events.

    PubMed

    Joshi, Manish; Khan, Arshad; Anand, S; Sapra, B K

    2016-01-01

    The effect of fireworks on the aerosol number characteristics of atmosphere was studied for an urban mega city. Measurements were made at 50 m height to assess the local changes around the festival days. Apart from the increase in total number concentration and characteristic accumulation mode, short-term increase of ultrafine particle concentration was noted. Total number concentration varies an order of magnitude during the measurement period in which peak occurs at a frequency of approximately one per day. On integral scale, it seems not possible to distinguish an episodic (e.g. firework bursting induced aerosol emission) and a normal (ambient atmospheric changes) event. However these events could be differentiated on the basis of size evolution analysis around number concentration peaks. The results are discussed relative to past studies and inferences are drawn towards aerosol signatures of firework bursting. The short-term burst in ultrafine particle concentration can pose an inhalation hazard. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Gene Duplication and the Evolution of Hemoglobin Isoform Differentiation in Birds*

    PubMed Central

    Grispo, Michael T.; Natarajan, Chandrasekhar; Projecto-Garcia, Joana; Moriyama, Hideaki; Weber, Roy E.; Storz, Jay F.

    2012-01-01

    The majority of bird species co-express two functionally distinct hemoglobin (Hb) isoforms in definitive erythrocytes as follows: HbA (the major adult Hb isoform, with α-chain subunits encoded by the αA-globin gene) and HbD (the minor adult Hb isoform, with α-chain subunits encoded by the αD-globin gene). The αD-globin gene originated via tandem duplication of an embryonic α-like globin gene in the stem lineage of tetrapod vertebrates, which suggests the possibility that functional differentiation between the HbA and HbD isoforms may be attributable to a retained ancestral character state in HbD that harkens back to a primordial, embryonic function. To investigate this possibility, we conducted a combined analysis of protein biochemistry and sequence evolution to characterize the structural and functional basis of Hb isoform differentiation in birds. Functional experiments involving purified HbA and HbD isoforms from 11 different bird species revealed that HbD is characterized by a consistently higher O2 affinity in the presence of allosteric effectors such as organic phosphates and Cl− ions. In the case of both HbA and HbD, analyses of oxygenation properties under the two-state Monod-Wyman-Changeux allosteric model revealed that the pH dependence of Hb-O2 affinity stems primarily from changes in the O2 association constant of deoxy (T-state)-Hb. Ancestral sequence reconstructions revealed that the amino acid substitutions that distinguish the adult-expressed Hb isoforms are not attributable to the retention of an ancestral (pre-duplication) character state in the αD-globin gene that is shared with the embryonic α-like globin gene. PMID:22962007

  12. Gene duplication and the evolution of hemoglobin isoform differentiation in birds.

    PubMed

    Grispo, Michael T; Natarajan, Chandrasekhar; Projecto-Garcia, Joana; Moriyama, Hideaki; Weber, Roy E; Storz, Jay F

    2012-11-02

    The majority of bird species co-express two functionally distinct hemoglobin (Hb) isoforms in definitive erythrocytes as follows: HbA (the major adult Hb isoform, with α-chain subunits encoded by the α(A)-globin gene) and HbD (the minor adult Hb isoform, with α-chain subunits encoded by the α(D)-globin gene). The α(D)-globin gene originated via tandem duplication of an embryonic α-like globin gene in the stem lineage of tetrapod vertebrates, which suggests the possibility that functional differentiation between the HbA and HbD isoforms may be attributable to a retained ancestral character state in HbD that harkens back to a primordial, embryonic function. To investigate this possibility, we conducted a combined analysis of protein biochemistry and sequence evolution to characterize the structural and functional basis of Hb isoform differentiation in birds. Functional experiments involving purified HbA and HbD isoforms from 11 different bird species revealed that HbD is characterized by a consistently higher O(2) affinity in the presence of allosteric effectors such as organic phosphates and Cl(-) ions. In the case of both HbA and HbD, analyses of oxygenation properties under the two-state Monod-Wyman-Changeux allosteric model revealed that the pH dependence of Hb-O(2) affinity stems primarily from changes in the O(2) association constant of deoxy (T-state)-Hb. Ancestral sequence reconstructions revealed that the amino acid substitutions that distinguish the adult-expressed Hb isoforms are not attributable to the retention of an ancestral (pre-duplication) character state in the α(D)-globin gene that is shared with the embryonic α-like globin gene.

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

    PubMed

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

    2017-12-02

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

  14. Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

    PubMed Central

    Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

    2012-01-01

    Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms. PMID:23226565

  15. Constrained minimization of smooth functions using a genetic algorithm

    NASA Technical Reports Server (NTRS)

    Moerder, Daniel D.; Pamadi, Bandu N.

    1994-01-01

    The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.

  16. An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

    PubMed

    Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

    2013-01-01

    The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

  17. An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

    PubMed Central

    Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

    2013-01-01

    The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

  18. Fast computation of derivative based sensitivities of PSHA models via algorithmic differentiation

    NASA Astrophysics Data System (ADS)

    Leövey, Hernan; Molkenthin, Christian; Scherbaum, Frank; Griewank, Andreas; Kuehn, Nicolas; Stafford, Peter

    2015-04-01

    Probabilistic seismic hazard analysis (PSHA) is the preferred tool for estimation of potential ground-shaking hazard due to future earthquakes at a site of interest. A modern PSHA represents a complex framework which combines different models with possible many inputs. Sensitivity analysis is a valuable tool for quantifying changes of a model output as inputs are perturbed, identifying critical input parameters and obtaining insight in the model behavior. Differential sensitivity analysis relies on calculating first-order partial derivatives of the model output with respect to its inputs. Moreover, derivative based global sensitivity measures (Sobol' & Kucherenko '09) can be practically used to detect non-essential inputs of the models, thus restricting the focus of attention to a possible much smaller set of inputs. Nevertheless, obtaining first-order partial derivatives of complex models with traditional approaches can be very challenging, and usually increases the computation complexity linearly with the number of inputs appearing in the models. In this study we show how Algorithmic Differentiation (AD) tools can be used in a complex framework such as PSHA to successfully estimate derivative based sensitivities, as is the case in various other domains such as meteorology or aerodynamics, without no significant increase in the computation complexity required for the original computations. First we demonstrate the feasibility of the AD methodology by comparing AD derived sensitivities to analytically derived sensitivities for a basic case of PSHA using a simple ground-motion prediction equation. In a second step, we derive sensitivities via AD for a more complex PSHA study using a ground motion attenuation relation based on a stochastic method to simulate strong motion. The presented approach is general enough to accommodate more advanced PSHA studies of higher complexity.

  19. Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

    PubMed Central

    Sun, Lijuan; Guo, Jian; Xu, Bin; Li, Shujing

    2017-01-01

    The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability. PMID:28127305

  20. Numerical Differentiation of Noisy, Nonsmooth Data

    DOE PAGES

    Chartrand, Rick

    2011-01-01

    We consider the problem of differentiating a function specified by noisy data. Regularizing the differentiation process avoids the noise amplification of finite-difference methods. We use total-variation regularization, which allows for discontinuous solutions. The resulting simple algorithm accurately differentiates noisy functions, including those which have a discontinuous derivative.

  1. Computation and visualization of geometric partial differential equations

    NASA Astrophysics Data System (ADS)

    Tiee, Christopher L.

    The chief goal of this work is to explore a modern framework for the study and approximation of partial differential equations, recast common partial differential equations into this framework, and prove theorems about such equations and their approximations. A central motivation is to recognize and respect the essential geometric nature of such problems, and take it into consideration when approximating. The hope is that this process will lead to the discovery of more refined algorithms and processes and apply them to new problems. In the first part, we introduce our quantities of interest and reformulate traditional boundary value problems in the modern framework. We see how Hilbert complexes capture and abstract the most important properties of such boundary value problems, leading to generalizations of important classical results such as the Hodge decomposition theorem. They also provide the proper setting for numerical approximations. We also provide an abstract framework for evolution problems in these spaces: Bochner spaces. We next turn to approximation. We build layers of abstraction, progressing from functions, to differential forms, and finally, to Hilbert complexes. We explore finite element exterior calculus (FEEC), which allows us to approximate solutions involving differential forms, and analyze the approximation error. In the second part, we prove our central results. We first prove an extension of current error estimates for the elliptic problem in Hilbert complexes. This extension handles solutions with nonzero harmonic part. Next, we consider evolution problems in Hilbert complexes and prove abstract error estimates. We apply these estimates to the problem for Riemannian hypersurfaces in R. {n+1},generalizing current results for open subsets of R. {n}. Finally, we applysome of the concepts to a nonlinear problem, the Ricci flow on surfaces, and use tools from nonlinear analysis to help develop and analyze the equations. In the appendices, we

  2. Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography

    PubMed Central

    Li, Yanqiu; Liu, Shi; Inaki, Schlaberg H.

    2017-01-01

    Accuracy and speed of algorithms play an important role in the reconstruction of temperature field measurements by acoustic tomography. Existing algorithms are based on static models which only consider the measurement information. A dynamic model of three-dimensional temperature reconstruction by acoustic tomography is established in this paper. A dynamic algorithm is proposed considering both acoustic measurement information and the dynamic evolution information of the temperature field. An objective function is built which fuses measurement information and the space constraint of the temperature field with its dynamic evolution information. Robust estimation is used to extend the objective function. The method combines a tunneling algorithm and a local minimization technique to solve the objective function. Numerical simulations show that the image quality and noise immunity of the dynamic reconstruction algorithm are better when compared with static algorithms such as least square method, algebraic reconstruction technique and standard Tikhonov regularization algorithms. An effective method is provided for temperature field reconstruction by acoustic tomography. PMID:28895930

  3. Newton Algorithms for Analytic Rotation: An Implicit Function Approach

    ERIC Educational Resources Information Center

    Boik, Robert J.

    2008-01-01

    In this paper implicit function-based parameterizations for orthogonal and oblique rotation matrices are proposed. The parameterizations are used to construct Newton algorithms for minimizing differentiable rotation criteria applied to "m" factors and "p" variables. The speed of the new algorithms is compared to that of existing algorithms and to…

  4. A Model Parameter Extraction Method for Dielectric Barrier Discharge Ozone Chamber using Differential Evolution

    NASA Astrophysics Data System (ADS)

    Amjad, M.; Salam, Z.; Ishaque, K.

    2014-04-01

    In order to design an efficient resonant power supply for ozone gas generator, it is necessary to accurately determine the parameters of the ozone chamber. In the conventional method, the information from Lissajous plot is used to estimate the values of these parameters. However, the experimental setup for this purpose can only predict the parameters at one operating frequency and there is no guarantee that it results in the highest ozone gas yield. This paper proposes a new approach to determine the parameters using a search and optimization technique known as Differential Evolution (DE). The desired objective function of DE is set at the resonance condition and the chamber parameter values can be searched regardless of experimental constraints. The chamber parameters obtained from the DE technique are validated by experiment.

  5. Controlling Tensegrity Robots Through Evolution

    NASA Technical Reports Server (NTRS)

    Iscen, Atil; Agogino, Adrian; SunSpiral, Vytas; Tumer, Kagan

    2013-01-01

    Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball-shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400 percent better than a hand-coded solution, while the multi-agent evolution performs 800 percent better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.

  6. Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

    PubMed

    Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva

    2017-03-01

    In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Genetic evidence for differential selection of grain and embryo weight during wheat evolution under domestication

    PubMed Central

    Golan, Guy; Oksenberg, Adi; Peleg, Zvi

    2015-01-01

    Wheat is one of the Neolithic founder crops domesticated ~10 500 years ago. Following the domestication episode, its evolution under domestication has resulted in various genetic modifications. Grain weight, embryo weight, and the interaction between those factors were examined among domesticated durum wheat and its direct progenitor, wild emmer wheat. Experimental data show that grain weight has increased over the course of wheat evolution without any parallel change in embryo weight, resulting in a significantly reduced (30%) embryo weight/grain weight ratio in domesticated wheat. The genetic factors associated with these modifications were further investigated using a population of recombinant inbred substitution lines that segregated for chromosome 2A. A cluster of loci affecting grain weight and shape was identified on the long arm of chromosome 2AL. Interestingly, a novel locus controlling embryo weight was mapped on chromosome 2AS, on which the wild emmer allele promotes heavier embryos and greater seedling vigour. To the best of our knowledge, this is the first report of a QTL for embryo weight in wheat. The results suggest a differential selection of grain and embryo weight during the evolution of domesticated wheat. It is argued that conscious selection by early farmers favouring larger grains and smaller embryos appears to have resulted in a significant change in endosperm weight/embryo weight ratio in the domesticated wheat. Exposing the genetic factors associated with endosperm and embryo size improves our understanding of the evolutionary dynamics of wheat under domestication and is likely to be useful for future wheat-breeding efforts. PMID:26019253

  8. A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks

    PubMed Central

    Zhang, Qingguo; Fok, Mable P.

    2017-01-01

    Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate’s target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate’s target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage–distance rate and the number of moved mobile sensors, when compare with other approaches. PMID:28075365

  9. Poorly Differentiated Thyroid Carcinoma.

    PubMed

    Setia, Namrata; Barletta, Justine A

    2014-12-01

    Poorly differentiated thyroid carcinoma (PDTC) has been recognized for the past 30 years as an entity showing intermediate differentiation and clinical behavior between well-differentiated thyroid carcinomas (ie, papillary thyroid carcinoma and follicular thyroid carcinoma) and anaplastic thyroid carcinoma; however, there has been considerable controversy around the definition of PDTC. In this review, the evolution in the definition of PDTC, current diagnostic criteria, differential diagnoses, potentially helpful immunohistochemical studies, and molecular alterations are discussed with the aim of highlighting where the diagnosis of PDTC currently stands. Published by Elsevier Inc.

  10. Solving TSP problem with improved genetic algorithm

    NASA Astrophysics Data System (ADS)

    Fu, Chunhua; Zhang, Lijun; Wang, Xiaojing; Qiao, Liying

    2018-05-01

    The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

  11. Algorithme intelligent d'optimisation d'un design structurel de grande envergure

    NASA Astrophysics Data System (ADS)

    Dominique, Stephane

    genetic algorithm that prevents new individuals to be born too close to previously evaluated solutions. The restricted area becomes smaller or larger during the optimisation to allow global or local search when necessary. Also, a new search operator named Substitution Operator is incorporated in GATE. This operator allows an ANN surrogate model to guide the algorithm toward the most promising areas of the design space. The suggested CBR approach and GATE were tested on several simple test problems, as well as on the industrial problem of designing a gas turbine engine rotor's disc. These results are compared to other results obtained for the same problems by many other popular optimisation algorithms, such as (depending of the problem) gradient algorithms, binary genetic algorithm, real number genetic algorithm, genetic algorithm using multiple parents crossovers, differential evolution genetic algorithm, Hookes & Jeeves generalized pattern search method and POINTER from the software I-SIGHT 3.5. Results show that GATE is quite competitive, giving the best results for 5 of the 6 constrained optimisation problem. GATE also provided the best results of all on problem produced by a Maximum Set Gaussian landscape generator. Finally, GATE provided a disc 4.3% lighter than the best other tested algorithm (POINTER) for the gas turbine engine rotor's disc problem. One drawback of GATE is a lesser efficiency for highly multimodal unconstrained problems, for which he gave quite poor results with respect to its implementation cost. To conclude, according to the preliminary results obtained during this thesis, the suggested CBR process, combined with GATE, seems to be a very good candidate to automate and accelerate the structural design of mechanical devices, potentially reducing significantly the cost of industrial preliminary design processes.

  12. [Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

    PubMed

    Li, Jing; Hong, Wenxue

    2014-12-01

    The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

  13. Fixation times in differentiation and evolution in the presence of bottlenecks, deserts, and oases.

    PubMed

    Chou, Tom; Wang, Yu

    2015-05-07

    Cellular differentiation and evolution are stochastic processes that can involve multiple types (or states) of particles moving on a complex, high-dimensional state-space or "fitness" landscape. Cells of each specific type can thus be quantified by their population at a corresponding node within a network of states. Their dynamics across the state-space network involve genotypic or phenotypic transitions that can occur upon cell division, such as during symmetric or asymmetric cell differentiation, or upon spontaneous mutation. Here, we use a general multi-type branching processes to study first passage time statistics for a single cell to appear in a specific state. Our approach readily allows for nonexponentially distributed waiting times between transitions, reflecting, e.g., the cell cycle. For simplicity, we restrict most of our detailed analysis to exponentially distributed waiting times (Poisson processes). We present results for a sequential evolutionary process in which L successive transitions propel a population from a "wild-type" state to a given "terminally differentiated," "resistant," or "cancerous" state. Analytic and numeric results are also found for first passage times across an evolutionary chain containing a node with increased death or proliferation rate, representing a desert/bottleneck or an oasis. Processes involving cell proliferation are shown to be "nonlinear" (even though mean-field equations for the expected particle numbers are linear) resulting in first passage time statistics that depend on the position of the bottleneck or oasis. Our results highlight the sensitivity of stochastic measures to cell division fate and quantify the limitations of using certain approximations (such as the fixed-population and mean-field assumptions) in evaluating fixation times. Published by Elsevier Ltd.

  14. Detecting microsatellites within genomes: significant variation among algorithms.

    PubMed

    Leclercq, Sébastien; Rivals, Eric; Jarne, Philippe

    2007-04-18

    Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algorithms have been developed for this purpose. Here, we compared the detection efficiency of five of them (TRF, Mreps, Sputnik, STAR, and RepeatMasker). Our analysis was first conducted on the human X chromosome, and microsatellite distributions were characterized by microsatellite number, length, and divergence from a pure motif. The algorithms work with user-defined parameters, and we demonstrate that the parameter values chosen can strongly influence microsatellite distributions. The five algorithms were then compared by fixing parameters settings, and the analysis was extended to three other genomes (Saccharomyces cerevisiae, Neurospora crassa and Drosophila melanogaster) spanning a wide range of size and structure. Significant differences for all characteristics of microsatellites were observed among algorithms, but not among genomes, for both perfect and imperfect microsatellites. Striking differences were detected for short microsatellites (below 20 bp), regardless of motif. Since the algorithm used strongly influences empirical distributions, studies analyzing microsatellite evolution based on a comparison between empirical and theoretical size distributions should therefore be considered with caution. We also discuss why a typological definition of microsatellites limits our capacity to capture their genomic distributions.

  15. Thermal evolution of magma reservoirs in the shallow crust and incidence on magma differentiation: the St-Jean-du-Doigt layered intrusion (Brittany, France)

    NASA Astrophysics Data System (ADS)

    Barboni, M.; Bussy, F.; Ovtcharova, M.; Schoene, B.

    2009-12-01

    Understanding the emplacement and growth of intrusive bodies in terms of mechanism, duration, thermal evolution and rates are fundamental aspects of crustal evolution. Recent studies show that many plutons grow in several Ma by in situ accretion of discrete magma pulses, which constitute small-scale magmatic reservoirs. The residence time of magmas, and hence their capacities to interact and differentiate, are controlled by the local thermal environment. The latter is highly dependant on 1) the emplacement depth, 2) the magmas and country rock composition, 3) the country rock thermal conductivity, 4) the rate of magma injection and 5) the geometry of the intrusion. In shallow level plutons, where magmas solidify quickly, evidence for magma mixing and/or differentiation processes is considered by many authors to be inherited from deeper levels. We show however that in-situ differentiation and magma interactions occurred within basaltic and felsic sills at shallow depth (0.3 GPa) in the St-Jean-du-Doigt bimodal intrusion, France. Field evidence coupled to high precision zircon U-Pb dating document progressive thermal maturation within the incrementally built laccolith. Early m-thick mafic sills are homogeneous and fine-grained with planar contacts with neighbouring felsic sills; within a minimal 0.5 Ma time span, the system gets warmer, adjacent sills interact and mingle, and mafic sills are differentiating in the top 40 cm of the layer. Rheological and thermal modelling show that observed in-situ differentiation-accumulation processes may be achieved in less than 10 years at shallow depth, provided that (1) the differentiating sills are injected beneath consolidated, yet still warm basalt sills, which act as low conductive insulating screens, (2) the early mafic sills accreted under the roof of the laccolith as a 100m thick top layer within 0.5 My, and (3) subsequent and sustained magmatic activity occurred on a short time scale (years) at an injection rate of ca. 0

  16. Algebraic aspects of evolution partial differential equation arising in the study of constant elasticity of variance model from financial mathematics

    NASA Astrophysics Data System (ADS)

    Motsepa, Tanki; Aziz, Taha; Fatima, Aeeman; Khalique, Chaudry Masood

    2018-03-01

    The optimal investment-consumption problem under the constant elasticity of variance (CEV) model is investigated from the perspective of Lie group analysis. The Lie symmetry group of the evolution partial differential equation describing the CEV model is derived. The Lie point symmetries are then used to obtain an exact solution of the governing model satisfying a standard terminal condition. Finally, we construct conservation laws of the underlying equation using the general theorem on conservation laws.

  17. Exponential integration algorithms applied to viscoplasticity

    NASA Technical Reports Server (NTRS)

    Freed, Alan D.; Walker, Kevin P.

    1991-01-01

    Four, linear, exponential, integration algorithms (two implicit, one explicit, and one predictor/corrector) are applied to a viscoplastic model to assess their capabilities. Viscoplasticity comprises a system of coupled, nonlinear, stiff, first order, ordinary differential equations which are a challenge to integrate by any means. Two of the algorithms (the predictor/corrector and one of the implicits) give outstanding results, even for very large time steps.

  18. Using some results about the Lie evolution of differential operators to obtain the Fokker-Planck equation for non-Hamiltonian dynamical systems of interest

    NASA Astrophysics Data System (ADS)

    Bianucci, Marco

    2018-05-01

    Finding the generalized Fokker-Planck Equation (FPE) for the reduced probability density function of a subpart of a given complex system is a classical issue of statistical mechanics. Zwanzig projection perturbation approach to this issue leads to the trouble of resumming a series of commutators of differential operators that we show to correspond to solving the Lie evolution of first order differential operators along the unperturbed Liouvillian of the dynamical system of interest. In this paper, we develop in a systematic way the procedure to formally solve this problem. In particular, here we show which the basic assumptions are, concerning the dynamical system of interest, necessary for the Lie evolution to be a group on the space of first order differential operators, and we obtain the coefficients of the so-evolved operators. It is thus demonstrated that if the Liouvillian of the system of interest is not a first order differential operator, in general, the FPE structure breaks down and the master equation contains all the power of the partial derivatives, up to infinity. Therefore, this work shed some light on the trouble of the ubiquitous emergence of both thermodynamics from microscopic systems and regular regression laws at macroscopic scales. However these results are very general and can be applied also in other contexts that are non-Hamiltonian as, for example, geophysical fluid dynamics, where important events, like El Niño, can be considered as large time scale phenomena emerging from the observation of few ocean degrees of freedom of a more complex system, including the interaction with the atmosphere.

  19. Thermal evolution of the earth

    NASA Technical Reports Server (NTRS)

    Spohn, T.

    1984-01-01

    The earth's heat budget and models of the earth's thermal evolution are discussed. Sources of the planetary heat are considered and modes of heat transport are addressed, including conduction, convection, and chemical convection. Thermal and convectional models of the earth are covered, and models of thermal evolution are discussed in detail, including changes in the core, the influence of layered mantle convection on the thermal evolution, and the effect of chemical differentiation on the continents.

  20. Sex Pheromone Evolution Is Associated with Differential Regulation of the Same Desaturase Gene in Two Genera of Leafroller Moths

    PubMed Central

    Albre, Jérôme; Liénard, Marjorie A.; Sirey, Tamara M.; Schmidt, Silvia; Tooman, Leah K.; Carraher, Colm; Greenwood, David R.; Löfstedt, Christer; Newcomb, Richard D.

    2012-01-01

    Chemical signals are prevalent in sexual communication systems. Mate recognition has been extensively studied within the Lepidoptera, where the production and recognition of species-specific sex pheromone signals are typically the defining character. While the specific blend of compounds that makes up the sex pheromones of many species has been characterized, the molecular mechanisms underpinning the evolution of pheromone-based mate recognition systems remain largely unknown. We have focused on two sets of sibling species within the leafroller moth genera Ctenopseustis and Planotortrix that have rapidly evolved the use of distinct sex pheromone blends. The compounds within these blends differ almost exclusively in the relative position of double bonds that are introduced by desaturase enzymes. Of the six desaturase orthologs isolated from all four species, functional analyses in yeast and gene expression in pheromone glands implicate three in pheromone biosynthesis, two Δ9-desaturases, and a Δ10-desaturase, while the remaining three desaturases include a Δ6-desaturase, a terminal desaturase, and a non-functional desaturase. Comparative quantitative real-time PCR reveals that the Δ10-desaturase is differentially expressed in the pheromone glands of the two sets of sibling species, consistent with differences in the pheromone blend in both species pairs. In the pheromone glands of species that utilize (Z)-8-tetradecenyl acetate as sex pheromone component (Ctenopseustis obliquana and Planotortrix octo), the expression levels of the Δ10-desaturase are significantly higher than in the pheromone glands of their respective sibling species (C. herana and P. excessana). Our results demonstrate that interspecific sex pheromone differences are associated with differential regulation of the same desaturase gene in two genera of moths. We suggest that differential gene regulation among members of a multigene family may be an important mechanism of molecular innovation in

  1. Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

    NASA Astrophysics Data System (ADS)

    Asaithambi, Sasikumar; Rajappa, Muthaiah

    2018-05-01

    In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

  2. Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm.

    PubMed

    Asaithambi, Sasikumar; Rajappa, Muthaiah

    2018-05-01

    In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

  3. Genetic evidence for differential selection of grain and embryo weight during wheat evolution under domestication.

    PubMed

    Golan, Guy; Oksenberg, Adi; Peleg, Zvi

    2015-09-01

    Wheat is one of the Neolithic founder crops domesticated ~10 500 years ago. Following the domestication episode, its evolution under domestication has resulted in various genetic modifications. Grain weight, embryo weight, and the interaction between those factors were examined among domesticated durum wheat and its direct progenitor, wild emmer wheat. Experimental data show that grain weight has increased over the course of wheat evolution without any parallel change in embryo weight, resulting in a significantly reduced (30%) embryo weight/grain weight ratio in domesticated wheat. The genetic factors associated with these modifications were further investigated using a population of recombinant inbred substitution lines that segregated for chromosome 2A. A cluster of loci affecting grain weight and shape was identified on the long arm of chromosome 2AL. Interestingly, a novel locus controlling embryo weight was mapped on chromosome 2AS, on which the wild emmer allele promotes heavier embryos and greater seedling vigour. To the best of our knowledge, this is the first report of a QTL for embryo weight in wheat. The results suggest a differential selection of grain and embryo weight during the evolution of domesticated wheat. It is argued that conscious selection by early farmers favouring larger grains and smaller embryos appears to have resulted in a significant change in endosperm weight/embryo weight ratio in the domesticated wheat. Exposing the genetic factors associated with endosperm and embryo size improves our understanding of the evolutionary dynamics of wheat under domestication and is likely to be useful for future wheat-breeding efforts. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.

  4. Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes.

    PubMed

    Chitalia, Rhea; Mueller, Jenna; Fu, Henry L; Whitley, Melodi Javid; Kirsch, David G; Brown, J Quincy; Willett, Rebecca; Ramanujam, Nimmi

    2016-09-01

    Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems.

  5. Iterative algorithms for large sparse linear systems on parallel computers

    NASA Technical Reports Server (NTRS)

    Adams, L. M.

    1982-01-01

    Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.

  6. Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation.

    PubMed

    Liu, Xiaofeng; Bai, Fang; Ouyang, Sisheng; Wang, Xicheng; Li, Honglin; Jiang, Hualiang

    2009-03-31

    Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105-112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 A to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 +/- 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of

  7. Differential Evolution Based IDWNN Controller for Fault Ride-Through of Grid-Connected Doubly Fed Induction Wind Generators

    PubMed Central

    Manonmani, N.; Subbiah, V.; Sivakumar, L.

    2015-01-01

    The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation. PMID:26516636

  8. Differential Evolution Based IDWNN Controller for Fault Ride-Through of Grid-Connected Doubly Fed Induction Wind Generators.

    PubMed

    Manonmani, N; Subbiah, V; Sivakumar, L

    2015-01-01

    The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation.

  9. Optimal Control for Stochastic Delay Evolution Equations

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

    Meng, Qingxin, E-mail: mqx@hutc.zj.cn; Shen, Yang, E-mail: skyshen87@gmail.com

    2016-08-15

    In this paper, we investigate a class of infinite-dimensional optimal control problems, where the state equation is given by a stochastic delay evolution equation with random coefficients, and the corresponding adjoint equation is given by an anticipated backward stochastic evolution equation. We first prove the continuous dependence theorems for stochastic delay evolution equations and anticipated backward stochastic evolution equations, and show the existence and uniqueness of solutions to anticipated backward stochastic evolution equations. Then we establish necessary and sufficient conditions for optimality of the control problem in the form of Pontryagin’s maximum principles. To illustrate the theoretical results, we applymore » stochastic maximum principles to study two examples, an infinite-dimensional linear-quadratic control problem with delay and an optimal control of a Dirichlet problem for a stochastic partial differential equation with delay. Further applications of the two examples to a Cauchy problem for a controlled linear stochastic partial differential equation and an optimal harvesting problem are also considered.« less

  10. Trajectory data privacy protection based on differential privacy mechanism

    NASA Astrophysics Data System (ADS)

    Gu, Ke; Yang, Lihao; Liu, Yongzhi; Liao, Niandong

    2018-05-01

    In this paper, we propose a trajectory data privacy protection scheme based on differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user’s trajectory data; secondly, the algorithm forms the polygon according to the protected points and the adjacent and high frequent accessed points that are selected from the accessing point database, then the algorithm calculates the polygon centroids; finally, the noises are added to the polygon centroids by the differential privacy method, and the polygon centroids replace the protected points, and then the algorithm constructs and issues the new trajectory data. The experiments show that the running time of the proposed algorithms is fast, the privacy protection of the scheme is effective and the data usability of the scheme is higher.

  11. The early thermal evolution of Mars

    NASA Astrophysics Data System (ADS)

    Bhatia, G. K.; Sahijpal, S.

    2016-01-01

    Hf-W isotopic systematics of Martian meteorites have provided evidence for the early accretion and rapid core formation of Mars. We present the results of numerical simulations performed to study the early thermal evolution and planetary scale differentiation of Mars. The simulations are confined to the initial 50 Myr (Ma) of the formation of solar system. The accretion energy produced during the growth of Mars and the decay energy due to the short-lived radio-nuclides 26Al, 60Fe, and the long-lived nuclides, 40K, 235U, 238U, and 232Th are incorporated as the heat sources for the thermal evolution of Mars. During the core-mantle differentiation of Mars, the molten metallic blobs were numerically moved using Stoke's law toward the center with descent velocity that depends on the local acceleration due to gravity. Apart from the accretion and the radioactive heat energies, the gravitational energy produced during the differentiation of Mars and the associated heat transfer is also parametrically incorporated in the present work to make an assessment of its contribution to the early thermal evolution of Mars. We conclude that the accretion energy alone cannot produce widespread melting and differentiation of Mars even with an efficient consumption of the accretion energy. This makes 26Al the prime source for the heating and planetary scale differentiation of Mars. We demonstrate a rapid accretion and core-mantle differentiation of Mars within the initial ~1.5 Myr. This is consistent with the chronological records of Martian meteorites.

  12. Differentially Private Frequent Sequence Mining via Sampling-based Candidate Pruning

    PubMed Central

    Xu, Shengzhi; Cheng, Xiang; Li, Zhengyi; Xiong, Li

    2016-01-01

    In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively reduce the number of unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose a novel differentially private FSM algorithm, which is referred to as PFS2. The core of our algorithm is to utilize sample databases to further prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which sequences are potentially frequent. To improve the accuracy of such private estimations, a sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS2 algorithm is ε-differentially private. Extensive experiments on real datasets illustrate that our PFS2 algorithm can privately find frequent sequences with high accuracy. PMID:26973430

  13. Computational evolution: taking liberties.

    PubMed

    Correia, Luís

    2010-09-01

    Evolution has, for a long time, inspired computer scientists to produce computer models mimicking its behavior. Evolutionary algorithm (EA) is one of the areas where this approach has flourished. EAs have been used to model and study evolution, but they have been especially developed for their aptitude as optimization tools for engineering. Developed models are quite simple in comparison with their natural sources of inspiration. However, since EAs run on computers, we have the freedom, especially in optimization models, to test approaches both realistic and outright speculative, from the biological point of view. In this article, we discuss different common evolutionary algorithm models, and then present some alternatives of interest. These include biologically inspired models, such as co-evolution and, in particular, symbiogenetics and outright artificial operators and representations. In each case, the advantages of the modifications to the standard model are identified. The other area of computational evolution, which has allowed us to study basic principles of evolution and ecology dynamics, is the development of artificial life platforms for open-ended evolution of artificial organisms. With these platforms, biologists can test theories by directly manipulating individuals and operators, observing the resulting effects in a realistic way. An overview of the most prominent of such environments is also presented. If instead of artificial platforms we use the real world for evolving artificial life, then we are dealing with evolutionary robotics (ERs). A brief description of this area is presented, analyzing its relations to biology. Finally, we present the conclusions and identify future research avenues in the frontier of computation and biology. Hopefully, this will help to draw the attention of more biologists and computer scientists to the benefits of such interdisciplinary research.

  14. A street rubbish detection algorithm based on Sift and RCNN

    NASA Astrophysics Data System (ADS)

    Yu, XiPeng; Chen, Zhong; Zhang, Shuo; Zhang, Ting

    2018-02-01

    This paper presents a street rubbish detection algorithm based on image registration with Sift feature and RCNN. Firstly, obtain the rubbish region proposal on the real-time street image and set up the CNN convolution neural network trained by the rubbish samples set consists of rubbish and non-rubbish images; Secondly, for every clean street image, obtain the Sift feature and do image registration with the real-time street image to obtain the differential image, the differential image filters a lot of background information, obtain the rubbish region proposal rect where the rubbish may appear on the differential image by the selective search algorithm. Then, the CNN model is used to detect the image pixel data in each of the region proposal on the real-time street image. According to the output vector of the CNN, it is judged whether the rubbish is in the region proposal or not. If it is rubbish, the region proposal on the real-time street image is marked. This algorithm avoids the large number of false detection caused by the detection on the whole image because the CNN is used to identify the image only in the region proposal on the real-time street image that may appear rubbish. Different from the traditional object detection algorithm based on the region proposal, the region proposal is obtained on the differential image not whole real-time street image, and the number of the invalid region proposal is greatly reduced. The algorithm has the high mean average precision (mAP).

  15. An algorithmic approach to the brain biopsy--part II.

    PubMed

    Prayson, Richard A; Kleinschmidt-DeMasters, B K

    2006-11-01

    The formulation of appropriate differential diagnoses for a slide is essential to the practice of surgical pathology but can be particularly challenging for residents and fellows. Algorithmic flow charts can help the less experienced pathologist to systematically consider all possible choices and eliminate incorrect diagnoses. They can assist pathologists-in-training in developing orderly, sequential, and logical thinking skills when confronting difficult cases. To present an algorithmic flow chart as an approach to formulating differential diagnoses for lesions seen in surgical neuropathology. An algorithmic flow chart to be used in teaching residents. Algorithms are not intended to be final diagnostic answers on any given case. Algorithms do not substitute for training received from experienced mentors nor do they substitute for comprehensive reading by trainees of reference textbooks. Algorithmic flow diagrams can, however, direct the viewer to the correct spot in reference texts for further in-depth reading once they hone down their diagnostic choices to a smaller number of entities. The best feature of algorithms is that they remind the user to consider all possibilities on each case, even if they can be quickly eliminated from further consideration. In Part II, we assist the resident in arriving at the correct diagnosis for neuropathologic lesions containing granulomatous inflammation, macrophages, or abnormal blood vessels.

  16. Detecting microsatellites within genomes: significant variation among algorithms

    PubMed Central

    Leclercq, Sébastien; Rivals, Eric; Jarne, Philippe

    2007-01-01

    Background Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algorithms have been developed for this purpose. Here, we compared the detection efficiency of five of them (TRF, Mreps, Sputnik, STAR, and RepeatMasker). Results Our analysis was first conducted on the human X chromosome, and microsatellite distributions were characterized by microsatellite number, length, and divergence from a pure motif. The algorithms work with user-defined parameters, and we demonstrate that the parameter values chosen can strongly influence microsatellite distributions. The five algorithms were then compared by fixing parameters settings, and the analysis was extended to three other genomes (Saccharomyces cerevisiae, Neurospora crassa and Drosophila melanogaster) spanning a wide range of size and structure. Significant differences for all characteristics of microsatellites were observed among algorithms, but not among genomes, for both perfect and imperfect microsatellites. Striking differences were detected for short microsatellites (below 20 bp), regardless of motif. Conclusion Since the algorithm used strongly influences empirical distributions, studies analyzing microsatellite evolution based on a comparison between empirical and theoretical size distributions should therefore be considered with caution. We also discuss why a typological definition of microsatellites limits our capacity to capture their genomic distributions. PMID:17442102

  17. Differential evolution of the UV luminosity function of Lyman break galaxies from z ~ 5 to 3

    NASA Astrophysics Data System (ADS)

    Iwata, I.; Ohta, K.; Tamura, N.; Akiyama, M.; Aoki, K.; Ando, M.; Kiuchi, G.; Sawicki, M.

    2007-04-01

    We report the ultraviolet luminosity function (UVLF) of Lyman break galaxies at z ~ 5 derived from a deep and wide survey using the prime focus camera of the 8.2 m Subaru telescope (Suprime-Cam). Target fields consist of two blank regions of the sky, namely, the region including the Hubble Deep Field-North and the J0053+1234 region, and the total effective surveyed area is 1290 arcmin2. Applications of carefully determined colour selection criteria in V - Ic and Ic - z' yield a detection of 853 z ~ 5 candidates with z'AB < 26.5 mag. The UVLF at z ~ 5 based on this sample shows no significant change in the number density of bright (L >~ L*z=3) LBGs from that at z ~ 3, while there is a significant decline in the LF's faint end with increasing look-back time. This result means that the evolution of the number densities is differential with UV luminosity: the number density of UV luminous objects remains almost constant from z ~ 5 to 3 (the cosmic age is about 1.2 to 2.1 Gyr) while the number density of fainter objects gradually increases with cosmic time. This trend becomes apparent thanks to the small uncertainties in number densities both in the bright and faint parts of LFs at different epochs that are made possible by the deep and wide surveys we use. We discuss the origins of this differential evolution of the UVLF along the cosmic time and suggest that our observational findings are consistent with the biased galaxy evolution scenario: a galaxy population hosted by massive dark haloes starts active star formation preferentially at early cosmic time, while less massive galaxies increase their number density later. We also calculated the UV luminosity density by integrating the UVLF and at z ~ 5 found it to be 38.8+6.7-4.1 per cent of that at z ~ 3 for the luminosity range L > 0.1L*z=3. By combining our results with those from the literature, we find that the cosmic UV luminosity density marks its peak at and then slowly declines towards higher redshift. Based on

  18. An Image Encryption Algorithm Based on Information Hiding

    NASA Astrophysics Data System (ADS)

    Ge, Xin; Lu, Bin; Liu, Fenlin; Gong, Daofu

    Aiming at resolving the conflict between security and efficiency in the design of chaotic image encryption algorithms, an image encryption algorithm based on information hiding is proposed based on the “one-time pad” idea. A random parameter is introduced to ensure a different keystream for each encryption, which has the characteristics of “one-time pad”, improving the security of the algorithm rapidly without significant increase in algorithm complexity. The random parameter is embedded into the ciphered image with information hiding technology, which avoids negotiation for its transport and makes the application of the algorithm easier. Algorithm analysis and experiments show that the algorithm is secure against chosen plaintext attack, differential attack and divide-and-conquer attack, and has good statistical properties in ciphered images.

  19. Genetic algorithms as global random search methods

    NASA Technical Reports Server (NTRS)

    Peck, Charles C.; Dhawan, Atam P.

    1995-01-01

    Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

  20. Genetic algorithms as global random search methods

    NASA Technical Reports Server (NTRS)

    Peck, Charles C.; Dhawan, Atam P.

    1995-01-01

    Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

  1. 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.

  2. Trees, bialgebras and intrinsic numerical algorithms

    NASA Technical Reports Server (NTRS)

    Crouch, Peter; Grossman, Robert; Larson, Richard

    1990-01-01

    Preliminary work about intrinsic numerical integrators evolving on groups is described. Fix a finite dimensional Lie group G; let g denote its Lie algebra, and let Y(sub 1),...,Y(sub N) denote a basis of g. A class of numerical algorithms is presented that approximate solutions to differential equations evolving on G of the form: dot-x(t) = F(x(t)), x(0) = p is an element of G. The algorithms depend upon constants c(sub i) and c(sub ij), for i = 1,...,k and j is less than i. The algorithms have the property that if the algorithm starts on the group, then it remains on the group. In addition, they also have the property that if G is the abelian group R(N), then the algorithm becomes the classical Runge-Kutta algorithm. The Cayley algebra generated by labeled, ordered trees is used to generate the equations that the coefficients c(sub i) and c(sub ij) must satisfy in order for the algorithm to yield an rth order numerical integrator and to analyze the resulting algorithms.

  3. Grand Views of Evolution.

    PubMed

    de Vladar, Harold P; Santos, Mauro; Szathmáry, Eörs

    2017-05-01

    Despite major advances in evolutionary theories, some aspects of evolution remain neglected: whether evolution: would come to a halt without abiotic change; is unbounded and open-ended; or is progressive and something beyond fitness is maximized. Here, we discuss some models of ecology and evolution and argue that ecological change, resulting in Red Queen dynamics, facilitates (but does not ensure) innovation. We distinguish three forms of open-endedness. In weak open-endedness, novel phenotypes can occur indefinitely. Strong open-endedness requires the continual appearance of evolutionary novelties and/or innovations. Ultimate open-endedness entails an indefinite increase in complexity, which requires unlimited heredity. Open-ended innovation needs exaptations that generate novel niches. This can result in new traits and new rules as the dynamics unfolds, suggesting that evolution is not fully algorithmic. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. The Fast Debris Evolution Model

    NASA Astrophysics Data System (ADS)

    Lewis, Hugh G.; Swinerd, Graham; Newland, Rebecca; Saunders, Arrun

    The ‘Particles-in-a-box' (PIB) model introduced by Talent (1992) removed the need for computerintensive Monte Carlo simulation to predict the gross characteristics of an evolving debris environment. The PIB model was described using a differential equation that allows the stability of the low Earth orbit (LEO) environment to be tested by a straightforward analysis of the equation's coefficients. As part of an ongoing research effort to investigate more efficient approaches to evolutionary modelling and to develop a suite of educational tools, a new PIB model has been developed. The model, entitled Fast Debris Evolution (FaDE), employs a first-order differential equation to describe the rate at which new objects (˜ 10 cm) are added and removed from the environment. Whilst Talent (1992) based the collision theory for the PIB approach on collisions between gas particles and adopted specific values for the parameters of the model from a number of references, the form and coefficients of the FaDE model equations can be inferred from the outputs of future projections produced by high-fidelity models, such as the DAMAGE model. The FaDE model has been implemented as a client-side, web-based service using Javascript embedded within a HTML document. Due to the simple nature of the algorithm, FaDE can deliver the results of future projections immediately in a graphical format, with complete user-control over key simulation parameters. Historical and future projections for the ˜ 10 cm low Earth orbit (LEO) debris environment under a variety of different scenarios are possible, including business as usual, no future launches, post-mission disposal and remediation. A selection of results is presented with comparisons with predictions made using the DAMAGE environment model. The results demonstrate that the FaDE model is able to capture comparable time-series of collisions and number of objects as predicted by DAMAGE in several scenarios. Further, and perhaps more importantly

  5. Evolution method and ``differential hierarchy'' of colored knot polynomials

    NASA Astrophysics Data System (ADS)

    Mironov, A.; Morozov, A.; Morozov, And.

    2013-10-01

    We consider braids with repeating patterns inside arbitrary knots which provides a multi-parametric family of knots, depending on the "evolution" parameter, which controls the number of repetitions. The dependence of knot (super)polynomials on such evolution parameters is very easy to find. We apply this evolution method to study of the families of knots and links which include the cases with just two parallel and anti-parallel strands in the braid, like the ordinary twist and 2-strand torus knots/links and counter-oriented 2-strand links. When the answers were available before, they are immediately reproduced, and an essentially new example is added of the "double braid", which is a combination of parallel and anti-parallel 2-strand braids. This study helps us to reveal with the full clarity and partly investigate a mysterious hierarchical structure of the colored HOMFLY polynomials, at least, in (anti)symmetric representations, which extends the original observation for the figure-eight knot to many (presumably all) knots. We demonstrate that this structure is typically respected by the t-deformation to the superpolynomials.

  6. NMR implementation of adiabatic SAT algorithm using strongly modulated pulses.

    PubMed

    Mitra, Avik; Mahesh, T S; Kumar, Anil

    2008-03-28

    NMR implementation of adiabatic algorithms face severe problems in homonuclear spin systems since the qubit selective pulses are long and during this period, evolution under the Hamiltonian and decoherence cause errors. The decoherence destroys the answer as it causes the final state to evolve to mixed state and in homonuclear systems, evolution under the internal Hamiltonian causes phase errors preventing the initial state to converge to the solution state. The resolution of these issues is necessary before one can proceed to implement an adiabatic algorithm in a large system where homonuclear coupled spins will become a necessity. In the present work, we demonstrate that by using "strongly modulated pulses" (SMPs) for the creation of interpolating Hamiltonian, one can circumvent both the problems and successfully implement the adiabatic SAT algorithm in a homonuclear three qubit system. This work also demonstrates that the SMPs tremendously reduce the time taken for the implementation of the algorithm, can overcome problems associated with decoherence, and will be the modality in future implementation of quantum information processing by NMR.

  7. Geologic Evolution of North America: Geologic features suggest that the continent has grown and differentiated through geologic time.

    PubMed

    Engel, A E

    1963-04-12

    The oldest decipherable rock complexes within continents (more than 2.5 billion years old) are largely basaltic volcanics and graywacke. Recent and modern analogs are the island arcs formed along and adjacent to the unstable interface of continental and oceanic crusts. The major interfacial reactions (orogenies) incorporate pre-existing sial, oceanic crust, and mantle into crust of a more continental type. Incipient stages of continental evolution, more than 3 billion years ago, remain obscure. They may involve either a cataclysmic granite-forming event or a succession of volcanic-sedimentary and granite-forming cycles. Intermediate and recent stages of continental evolution, as indicated by data for North America, involve accretion of numerous crustal interfaces with fragments of adjacent continental crust and their partial melting, reinjection, elevation, unroofing, and stabilization. Areas of relict provinces defined by ages of granites suggest that continental growth is approximately linear. But the advanced differentiation found in many provinces and the known overlaps permit wide deviation from linearity in the direction of a more explosive early or intermediate growth.

  8. Back to basics--how the evolution of the extracellular matrix underpinned vertebrate evolution.

    PubMed

    Huxley-Jones, Julie; Pinney, John W; Archer, John; Robertson, David L; Boot-Handford, Raymond P

    2009-04-01

    The extracellular matrix (ECM) is a complex substrate that is involved in and influences a spectrum of behaviours such as growth and differentiation and is the basis for the structure of tissues. Although a characteristic of all metazoans, the ECM has elaborated into a variety of tissues unique to vertebrates, such as bone, tendon and cartilage. Here we review recent advances in our understanding of the molecular evolution of the ECM. Furthermore, we demonstrate that ECM genes represent a pivotal family of proteins the evolution of which appears to have played an important role in the evolution of vertebrates.

  9. Genetic algorithm dynamics on a rugged landscape

    NASA Astrophysics Data System (ADS)

    Bornholdt, Stefan

    1998-04-01

    The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.

  10. Algorithmic Mechanism Design of Evolutionary Computation.

    PubMed

    Pei, Yan

    2015-01-01

    We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

  11. Algorithmic Mechanism Design of Evolutionary Computation

    PubMed Central

    2015-01-01

    We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm. PMID:26257777

  12. Laser-induced autofluorescence properties of base-cell lesions: analysis and algorithms for diagnosis and differentiation

    NASA Astrophysics Data System (ADS)

    Borisova, E.; Troyanova, P.; Avramov, L.

    2006-09-01

    The goals of this work were investigation of base-cell skin lesions by the method of laser-induced autofluorescence spectroscopy. Fluorescence spectra were obtained from benign base-cell papilloma and malignant base-cell carcinoma, as well as from healthy skin areas near to the lesions that were used posteriori to reveal changes between healthy and lesion skin spectra. Preliminarily lesions were classified by dermatoscopic method (MoleMax II, DERMA Instruments). All suspicious lesions were excised and were investigated histologically. The experimental set-up consists of a nitrogen laser (337 nm, 14 μJ, 10 Hz), lenses, filters, optical fibers, and a microspectrometer (PC2000, "Ocean Optics"). A computer controls this system. Spectrum of healthy skin consists of one main maximum at 470-500 nm spectral region and secondary maxima at in the regions around 400 and 440 nm. In cases of papilloma and base-cell carcinoma an intensity decrease was observed, related to accumulation of pigments in these cutaneous lesions. An relative increase of the fluorescence peak at 440 nm were registered in the case of base-cell carcinoma, related to metabolism activity increase, and appearance of green fluorescence, related to increase of keratin content in benign papilloma lesions were detected. The results, obtained were used to develop multispectral diagnostic algorithm of these base-cell lesions. An sensitivity of 89,4% and 91,0% and specificity of 99,6% and 97,4% for differentiation between normal skin and papilloma and carcinoma respectively were obtained. The capability of the human skin fluorescence spectroscopy for early diagnosis and differentiation of cutaneous lesions is shown.

  13. Differentially Private Empirical Risk Minimization

    PubMed Central

    Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D.

    2011-01-01

    Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. PMID:21892342

  14. Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.

    PubMed

    Kashyap, Kanchan L; Bajpai, Manish K; Khanna, Pritee; Giakos, George

    2018-01-01

    Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function. Copyright © 2017 John Wiley & Sons, Ltd.

  15. Research on numerical algorithms for large space structures

    NASA Technical Reports Server (NTRS)

    Denman, E. D.

    1981-01-01

    Numerical algorithms for analysis and design of large space structures are investigated. The sign algorithm and its application to decoupling of differential equations are presented. The generalized sign algorithm is given and its application to several problems discussed. The Laplace transforms of matrix functions and the diagonalization procedure for a finite element equation are discussed. The diagonalization of matrix polynomials is considered. The quadrature method and Laplace transforms is discussed and the identification of linear systems by the quadrature method investigated.

  16. An algorithmic approach to the brain biopsy--part I.

    PubMed

    Kleinschmidt-DeMasters, B K; Prayson, Richard A

    2006-11-01

    The formulation of appropriate differential diagnoses for a slide is essential to the practice of surgical pathology but can be particularly challenging for residents and fellows. Algorithmic flow charts can help the less experienced pathologist to systematically consider all possible choices and eliminate incorrect diagnoses. They can assist pathologists-in-training in developing orderly, sequential, and logical thinking skills when confronting difficult cases. To present an algorithmic flow chart as an approach to formulating differential diagnoses for lesions seen in surgical neuropathology. An algorithmic flow chart to be used in teaching residents. Algorithms are not intended to be final diagnostic answers on any given case. Algorithms do not substitute for training received from experienced mentors nor do they substitute for comprehensive reading by trainees of reference textbooks. Algorithmic flow diagrams can, however, direct the viewer to the correct spot in reference texts for further in-depth reading once they hone down their diagnostic choices to a smaller number of entities. The best feature of algorithms is that they remind the user to consider all possibilities on each case, even if they can be quickly eliminated from further consideration. In Part I, we assist the resident in learning how to handle brain biopsies in general and how to distinguish nonneoplastic lesions that mimic tumors from true neoplasms.

  17. Bell-polynomial approach and Wronskian determinant solutions for three sets of differential-difference nonlinear evolution equations with symbolic computation

    NASA Astrophysics Data System (ADS)

    Qin, Bo; Tian, Bo; Wang, Yu-Feng; Shen, Yu-Jia; Wang, Ming

    2017-10-01

    Under investigation in this paper are the Belov-Chaltikian (BC), Leznov and Blaszak-Marciniak (BM) lattice equations, which are associated with the conformal field theory, UToda(m_1,m_2) system and r-matrix, respectively. With symbolic computation, the Bell-polynomial approach is developed to directly bilinearize those three sets of differential-difference nonlinear evolution equations (NLEEs). This Bell-polynomial approach does not rely on any dependent variable transformation, which constitutes the key step and main difficulty of the Hirota bilinear method, and thus has the advantage in the bilinearization of the differential-difference NLEEs. Based on the bilinear forms obtained, the N-soliton solutions are constructed in terms of the N × N Wronskian determinant. Graphic illustrations demonstrate that those solutions, more general than the existing results, permit some new properties, such as the solitonic propagation and interactions for the BC lattice equations, and the nonnegative dark solitons for the BM lattice equations.

  18. Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

    PubMed Central

    Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

    2014-01-01

    Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

  19. Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

    PubMed

    Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

    2014-01-01

    Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

  20. Stellar differential rotation and coronal time-scales

    NASA Astrophysics Data System (ADS)

    Gibb, G. P. S.; Jardine, M. M.; Mackay, D. H.

    2014-10-01

    We investigate the time-scales of evolution of stellar coronae in response to surface differential rotation and diffusion. To quantify this, we study both the formation time and lifetime of a magnetic flux rope in a decaying bipolar active region. We apply a magnetic flux transport model to prescribe the evolution of the stellar photospheric field, and use this to drive the evolution of the coronal magnetic field via a magnetofrictional technique. Increasing the differential rotation (i.e. decreasing the equator-pole lap time) decreases the flux rope formation time. We find that the formation time is dependent upon the lap time and the surface diffusion time-scale through the relation τ_Form ∝ √{τ_Lapτ_Diff}. In contrast, the lifetimes of flux ropes are proportional to the lap time (τLife∝τLap). With this, flux ropes on stars with a differential rotation of more than eight times the solar value have a lifetime of less than 2 d. As a consequence, we propose that features such as solar-like quiescent prominences may not be easily observable on such stars, as the lifetimes of the flux ropes which host the cool plasma are very short. We conclude that such high differential rotation stars may have very dynamical coronae.

  1. An adaptive sharing elitist evolution strategy for multiobjective optimization.

    PubMed

    Costa, Lino; Oliveira, Pedro

    2003-01-01

    Almost all approaches to multiobjective optimization are based on Genetic Algorithms (GAs), and implementations based on Evolution Strategies (ESs) are very rare. Thus, it is crucial to investigate how ESs can be extended to multiobjective optimization, since they have, in the past, proven to be powerful single objective optimizers. In this paper, we present a new approach to multiobjective optimization, based on ESs. We call this approach the Multiobjective Elitist Evolution Strategy (MEES) as it incorporates several mechanisms, like elitism, that improve its performance. When compared with other algorithms, MEES shows very promising results in terms of performance.

  2. Over 20 years of reaction access systems from MDL: a novel reaction substructure search algorithm.

    PubMed

    Chen, Lingran; Nourse, James G; Christie, Bradley D; Leland, Burton A; Grier, David L

    2002-01-01

    From REACCS, to MDL ISIS/Host Reaction Gateway, and most recently to MDL Relational Chemistry Server, a new product based on Oracle data cartridge technology, MDL's reaction database management and retrieval systems have undergone great changes. The evolution of the system architecture is briefly discussed. The evolution of MDL reaction substructure search (RSS) algorithms is detailed. This article mainly describes a novel RSS algorithm. This algorithm is based on a depth-first search approach and is able to fully and prospectively use reaction specific information, such as reacting center and atom-atom mapping (AAM) information. The new algorithm has been used in the recently released MDL Relational Chemistry Server and allows the user to precisely find reaction instances in databases while minimizing unrelated hits. Finally, the existing and new RSS algorithms are compared with several examples.

  3. Artificial evolution by viability rather than competition.

    PubMed

    Maesani, Andrea; Fernando, Pradeep Ruben; Floreano, Dario

    2014-01-01

    Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions. Here we present an alternative abstraction of artificial evolution, which does not require the formulation of a composite fitness function. Inspired from viability theory in dynamical systems, natural evolution and ethology, the proposed method puts emphasis on the elimination of individuals that do not meet a set of changing criteria, which are defined on the problem objectives and constraints. Experimental results show that the proposed method maintains higher diversity in the evolving population and generates more unique solutions when compared to classical competition-based evolutionary algorithms. Our findings suggest that incorporating viability principles into evolutionary algorithms can significantly improve the applicability and effectiveness of evolutionary methods to numerous complex problems of science and engineering, ranging from protein structure prediction to aircraft wing design.

  4. Conceptual Comparison of Population Based Metaheuristics for Engineering Problems

    PubMed Central

    Green, Paul

    2015-01-01

    Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported. The comparison of the numerical results with those of other metaheuristic techniques demonstrates the promising performance of the algorithm as a robust optimization tool for practical purposes. PMID:25874265

  5. Conceptual comparison of population based metaheuristics for engineering problems.

    PubMed

    Adekanmbi, Oluwole; Green, Paul

    2015-01-01

    Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported. The comparison of the numerical results with those of other metaheuristic techniques demonstrates the promising performance of the algorithm as a robust optimization tool for practical purposes.

  6. Moran-evolution of cooperation: From well-mixed to heterogeneous complex networks

    NASA Astrophysics Data System (ADS)

    Sarkar, Bijan

    2018-05-01

    Configurational arrangement of network architecture and interaction character of individuals are two most influential factors on the mechanisms underlying the evolutionary outcome of cooperation, which is explained by the well-established framework of evolutionary game theory. In the current study, not only qualitatively but also quantitatively, we measure Moran-evolution of cooperation to support an analytical agreement based on the consequences of the replicator equation in a finite population. The validity of the measurement has been double-checked in the well-mixed network by the Langevin stochastic differential equation and the Gillespie-algorithmic version of Moran-evolution, while in a structured network, the measurement of accuracy is verified by the standard numerical simulation. Considering the Birth-Death and Death-Birth updating rules through diffusion of individuals, the investigation is carried out in the wide range of game environments those relate to the various social dilemmas where we are able to draw a new rigorous mathematical track to tackle the heterogeneity of complex networks. The set of modified criteria reveals the exact fact about the emergence and maintenance of cooperation in the structured population. We find that in general, nature promotes the environment of coexistent traits.

  7. Differential expression of non-coding RNAs and continuous evolution of the X chromosome in testicular transcriptome of two mouse species.

    PubMed

    Homolka, David; Ivanek, Robert; Forejt, Jiri; Jansa, Petr

    2011-02-14

    Tight regulation of testicular gene expression is a prerequisite for male reproductive success, while differentiation of gene activity in spermatogenesis is important during speciation. Thus, comparison of testicular transcriptomes between closely related species can reveal unique regulatory patterns and shed light on evolutionary constraints separating the species. Here, we compared testicular transcriptomes of two closely related mouse species, Mus musculus and Mus spretus, which diverged more than one million years ago. We analyzed testicular expression using tiling arrays overlapping Chromosomes 2, X, Y and mitochondrial genome. An excess of differentially regulated non-coding RNAs was found on Chromosome 2 including the intronic antisense RNAs, intergenic RNAs and premature forms of Piwi-interacting RNAs (piRNAs). Moreover, striking difference was found in the expression of X-linked G6pdx gene, the parental gene of the autosomal retrogene G6pd2. The prevalence of non-coding RNAs among differentially expressed transcripts indicates their role in species-specific regulation of spermatogenesis. The postmeiotic expression of G6pdx in Mus spretus points towards the continuous evolution of X-chromosome silencing and provides an example of expression change accompanying the out-of-the X-chromosomal retroposition.

  8. Mapping the Geometric Evolution of Protein Folding Motor.

    PubMed

    Jerath, Gaurav; Hazam, Prakash Kishore; Shekhar, Shashi; Ramakrishnan, Vibin

    2016-01-01

    Polypeptide chain has an invariant main-chain and a variant side-chain sequence. How the side-chain sequence determines fold in terms of its chemical constitution has been scrutinized extensively and verified periodically. However, a focussed investigation on the directive effect of side-chain geometry may provide important insights supplementing existing algorithms in mapping the geometrical evolution of protein chains and its structural preferences. Geometrically, folding of protein structure may be envisaged as the evolution of its geometric variables: ϕ, and ψ dihedral angles of polypeptide main-chain directed by χ1, and χ2 of side chain. In this work, protein molecule is metaphorically modelled as a machine with 4 rotors ϕ, ψ, χ1 and χ2, with its evolution to the functional fold is directed by combinations of its rotor directions. We observe that differential rotor motions lead to different secondary structure formations and the combinatorial pattern is unique and consistent for particular secondary structure type. Further, we found that combination of rotor geometries of each amino acid is unique which partly explains how different amino acid sequence combinations have unique structural evolution and functional adaptation. Quantification of these amino acid rotor preferences, resulted in the generation of 3 substitution matrices, which later on plugged in the BLAST tool, for evaluating their efficiency in aligning sequences. We have employed BLOSUM62 and PAM30 as standard for primary evaluation. Generation of substitution matrices is a logical extension of the conceptual framework we attempted to build during the development of this work. Optimization of matrices following the conventional routines and possible application with biologically relevant data sets are beyond the scope of this manuscript, though it is a part of the larger project design.

  9. Coagulation algorithms with size binning

    NASA Technical Reports Server (NTRS)

    Statton, David M.; Gans, Jason; Williams, Eric

    1994-01-01

    The Smoluchowski equation describes the time evolution of an aerosol particle size distribution due to aggregation or coagulation. Any algorithm for computerized solution of this equation requires a scheme for describing the continuum of aerosol particle sizes as a discrete set. One standard form of the Smoluchowski equation accomplishes this by restricting the particle sizes to integer multiples of a basic unit particle size (the monomer size). This can be inefficient when particle concentrations over a large range of particle sizes must be calculated. Two algorithms employing a geometric size binning convention are examined: the first assumes that the aerosol particle concentration as a function of size can be considered constant within each size bin; the second approximates the concentration as a linear function of particle size within each size bin. The output of each algorithm is compared to an analytical solution in a special case of the Smoluchowski equation for which an exact solution is known . The range of parameters more appropriate for each algorithm is examined.

  10. Differentially private distributed logistic regression using private and public data.

    PubMed

    Ji, Zhanglong; Jiang, Xiaoqian; Wang, Shuang; Xiong, Li; Ohno-Machado, Lucila

    2014-01-01

    Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.

  11. Evolution of epigenetic regulation in vertebrate genomes

    PubMed Central

    Lowdon, Rebecca F.; Jang, Hyo Sik; Wang, Ting

    2016-01-01

    Empirical models of sequence evolution have spurred progress in the field of evolutionary genetics for decades. We are now realizing the importance and complexity of the eukaryotic epigenome. While epigenome analysis has been applied to genomes from single cell eukaryotes to human, comparative analyses are still relatively few, and computational algorithms to quantify epigenome evolution remain scarce. Accordingly, a quantitative model of epigenome evolution remains to be established. Here we review the comparative epigenomics literature and synthesize its overarching themes. We also suggest one mechanism, transcription factor binding site turnover, which relates sequence evolution to epigenetic conservation or divergence. Lastly, we propose a framework for how the field can move forward to build a coherent quantitative model of epigenome evolution. PMID:27080453

  12. Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security

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

    Harmer, Paul K; Temple, Michael A; Buckner, Mark A

    2011-01-01

    Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identicalmore » classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.« less

  13. Extended Kalman smoother with differential evolution technique for denoising of ECG signal.

    PubMed

    Panigrahy, D; Sahu, P K

    2016-09-01

    Electrocardiogram (ECG) signal gives a lot of information on the physiology of heart. In reality, noise from various sources interfere with the ECG signal. To get the correct information on physiology of the heart, noise cancellation of the ECG signal is required. In this paper, the effectiveness of extended Kalman smoother (EKS) with the differential evolution (DE) technique for noise cancellation of the ECG signal is investigated. DE is used as an automatic parameter selection method for the selection of ten optimized components of the ECG signal, and those are used to create the ECG signal according to the real ECG signal. These parameters are used by the EKS for the development of the state equation and also for initialization of the parameters of EKS. EKS framework is used for denoising the ECG signal from the single channel. The effectiveness of proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better signal to noise ratio (SNR) improvement, lesser mean square error (MSE) and percent of distortion (PRD) compared to other well-known methods.

  14. A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

    PubMed Central

    Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

    2012-01-01

    Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383

  15. A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.

    PubMed

    Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

    2012-01-01

    Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

  16. Genetic algorithm enhanced by machine learning in dynamic aperture optimization

    NASA Astrophysics Data System (ADS)

    Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

    2018-05-01

    With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

  17. Lower bound on the time complexity of local adiabatic evolution

    NASA Astrophysics Data System (ADS)

    Chen, Zhenghao; Koh, Pang Wei; Zhao, Yan

    2006-11-01

    The adiabatic theorem of quantum physics has been, in recent times, utilized in the design of local search quantum algorithms, and has been proven to be equivalent to standard quantum computation, that is, the use of unitary operators [D. Aharonov in Proceedings of the 45th Annual Symposium on the Foundations of Computer Science, 2004, Rome, Italy (IEEE Computer Society Press, New York, 2004), pp. 42-51]. Hence, the study of the time complexity of adiabatic evolution algorithms gives insight into the computational power of quantum algorithms. In this paper, we present two different approaches of evaluating the time complexity for local adiabatic evolution using time-independent parameters, thus providing effective tests (not requiring the evaluation of the entire time-dependent gap function) for the time complexity of newly developed algorithms. We further illustrate our tests by displaying results from the numerical simulation of some problems, viz. specially modified instances of the Hamming weight problem.

  18. "How do you know those particles are from cigarettes?": An algorithm to help differentiate second-hand tobacco smoke from background sources of household fine particulate matter.

    PubMed

    Dobson, Ruaraidh; Semple, Sean

    2018-06-18

    Second-hand smoke (SHS) at home is a target for public health interventions, such as air quality feedback interventions using low-cost particle monitors. However, these monitors also detect fine particles generated from non-SHS sources. The Dylos DC1700 reports particle counts in the coarse and fine size ranges. As tobacco smoke produces far more fine particles than coarse ones, and tobacco is generally the greatest source of particulate pollution in a smoking home, the ratio of coarse to fine particles may provide a useful method to identify the presence of SHS in homes. An algorithm was developed to differentiate smoking from smoke-free homes. Particle concentration data from 116 smoking homes and 25 non-smoking homes were used to test this algorithm. The algorithm correctly classified the smoking status of 135 of the 141 homes (96%), comparing favourably with a test of mean mass concentration. Applying this algorithm to Dylos particle count measurements may help identify the presence of SHS in homes or other indoor environments. Future research should adapt it to detect individual smoking periods within a 24 h or longer measurement period. Copyright © 2018 Elsevier Inc. All rights reserved.

  19. Intra-Tumor Genetic Heterogeneity in Wilms Tumor: Clonal Evolution and Clinical Implications.

    PubMed

    Cresswell, George D; Apps, John R; Chagtai, Tasnim; Mifsud, Borbala; Bentley, Christopher C; Maschietto, Mariana; Popov, Sergey D; Weeks, Mark E; Olsen, Øystein E; Sebire, Neil J; Pritchard-Jones, Kathy; Luscombe, Nicholas M; Williams, Richard D; Mifsud, William

    2016-07-01

    The evolution of pediatric solid tumors is poorly understood. There is conflicting evidence of intra-tumor genetic homogeneity vs. heterogeneity (ITGH) in a small number of studies in pediatric solid tumors. A number of copy number aberrations (CNA) are proposed as prognostic biomarkers to stratify patients, for example 1q+ in Wilms tumor (WT); current clinical trials use only one sample per tumor to profile this genetic biomarker. We multisampled 20 WT cases and assessed genome-wide allele-specific CNA and loss of heterozygosity, and inferred tumor evolution, using Illumina CytoSNP12v2.1 arrays, a custom analysis pipeline, and the MEDICC algorithm. We found remarkable diversity of ITGH and evolutionary trajectories in WT. 1q+ is heterogeneous in the majority of tumors with this change, with variable evolutionary timing. We estimate that at least three samples per tumor are needed to detect >95% of cases with 1q+. In contrast, somatic 11p15 LOH is uniformly an early event in WT development. We find evidence of two separate tumor origins in unilateral disease with divergent histology, and in bilateral WT. We also show subclonal changes related to differential response to chemotherapy. Rational trial design to include biomarkers in risk stratification requires tumor multisampling and reliable delineation of ITGH and tumor evolution. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  20. Exploring equivalence domain in nonlinear inverse problems using Covariance Matrix Adaption Evolution Strategy (CMAES) and random sampling

    NASA Astrophysics Data System (ADS)

    Grayver, Alexander V.; Kuvshinov, Alexey V.

    2016-05-01

    This paper presents a methodology to sample equivalence domain (ED) in nonlinear partial differential equation (PDE)-constrained inverse problems. For this purpose, we first applied state-of-the-art stochastic optimization algorithm called Covariance Matrix Adaptation Evolution Strategy (CMAES) to identify low-misfit regions of the model space. These regions were then randomly sampled to create an ensemble of equivalent models and quantify uncertainty. CMAES is aimed at exploring model space globally and is robust on very ill-conditioned problems. We show that the number of iterations required to converge grows at a moderate rate with respect to number of unknowns and the algorithm is embarrassingly parallel. We formulated the problem by using the generalized Gaussian distribution. This enabled us to seamlessly use arbitrary norms for residual and regularization terms. We show that various regularization norms facilitate studying different classes of equivalent solutions. We further show how performance of the standard Metropolis-Hastings Markov chain Monte Carlo algorithm can be substantially improved by using information CMAES provides. This methodology was tested by using individual and joint inversions of magneotelluric, controlled-source electromagnetic (EM) and global EM induction data.

  1. The threshold algorithm: Description of the methodology and new developments

    NASA Astrophysics Data System (ADS)

    Neelamraju, Sridhar; Oligschleger, Christina; Schön, J. Christian

    2017-10-01

    Understanding the dynamics of complex systems requires the investigation of their energy landscape. In particular, the flow of probability on such landscapes is a central feature in visualizing the time evolution of complex systems. To obtain such flows, and the concomitant stable states of the systems and the generalized barriers among them, the threshold algorithm has been developed. Here, we describe the methodology of this approach starting from the fundamental concepts in complex energy landscapes and present recent new developments, the threshold-minimization algorithm and the molecular dynamics threshold algorithm. For applications of these new algorithms, we draw on landscape studies of three disaccharide molecules: lactose, maltose, and sucrose.

  2. Genome differentiation of Drosophila melanogaster from a microclimate contrast in Evolution Canyon, Israel

    PubMed Central

    Hübner, Sariel; Rashkovetsky, Eugenia; Kim, Young Bun; Oh, Jung Hun; Michalak, Katarzyna; Weiner, Dmitry; Korol, Abraham B.; Nevo, Eviatar; Michalak, Pawel

    2013-01-01

    The opposite slopes of “Evolution Canyon” in Israel have served as a natural model system of adaptation to a microclimate contrast. Long-term studies of Drosophila melanogaster populations inhabiting the canyon have exhibited significant interslope divergence in thermal and drought stress resistance, candidate genes, mobile elements, habitat choice, mating discrimination, and wing-shape variation, all despite close physical proximity of the contrasting habitats, as well as substantial interslope migration. To examine patterns of genetic differentiation at the genome-wide level, we used high coverage sequencing of the flies’ genomes. A total of 572 genes were significantly different in allele frequency between the slopes, 106 out of which were associated with 74 significantly overrepresented gene ontology (GO) terms, particularly so with response to stimulus and developmental and reproductive processes, thus corroborating previous observations of interslope divergence in stress response, life history, and mating functions. There were at least 37 chromosomal “islands” of interslope divergence and low sequence polymorphism, plausible signatures of selective sweeps, more abundant in flies derived from one (north-facing) of the slopes. Positive correlation between local recombination rate and the level of nucleotide polymorphism was also found. PMID:24324170

  3. Ultrasound speckle reduction based on fractional order differentiation.

    PubMed

    Shao, Dangguo; Zhou, Ting; Liu, Fan; Yi, Sanli; Xiang, Yan; Ma, Lei; Xiong, Xin; He, Jianfeng

    2017-07-01

    Ultrasound images show a granular pattern of noise known as speckle that diminishes their quality and results in difficulties in diagnosis. To preserve edges and features, this paper proposes a fractional differentiation-based image operator to reduce speckle in ultrasound. An image de-noising model based on fractional partial differential equations with balance relation between k (gradient modulus threshold that controls the conduction) and v (the order of fractional differentiation) was constructed by the effective combination of fractional calculus theory and a partial differential equation, and the numerical algorithm of it was achieved using a fractional differential mask operator. The proposed algorithm has better speckle reduction and structure preservation than the three existing methods [P-M model, the speckle reducing anisotropic diffusion (SRAD) technique, and the detail preserving anisotropic diffusion (DPAD) technique]. And it is significantly faster than bilateral filtering (BF) in producing virtually the same experimental results. Ultrasound phantom testing and in vivo imaging show that the proposed method can improve the quality of an ultrasound image in terms of tissue SNR, CNR, and FOM values.

  4. Soft gluon evolution and non-global logarithms

    NASA Astrophysics Data System (ADS)

    Martínez, René Ángeles; De Angelis, Matthew; Forshaw, Jeffrey R.; Plätzer, Simon; Seymour, Michael H.

    2018-05-01

    We consider soft-gluon evolution at the amplitude level. Our evolution algorithm applies to generic hard-scattering processes involving any number of coloured partons and we present a reformulation of the algorithm in such a way as to make the cancellation of infrared divergences explicit. We also emphasise the special role played by a Lorentz-invariant evolution variable, which coincides with the transverse momentum of the latest emission in a suitably defined dipole zero-momentum frame. Handling large colour matrices presents the most significant challenge to numerical implementations and we present a means to expand systematically about the leading colour approximation. Specifically, we present a systematic procedure to calculate the resulting colour traces, which is based on the colour flow basis. Identifying the leading contribution leads us to re-derive the Banfi-Marchesini-Smye equation. However, our formalism is more general and can systematically perform resummation of contributions enhanced by the t'Hooft coupling α s N ˜ 1, along with successive perturbations that are parametrically suppressed by powers of 1 /N . We also discuss how our approach relates to earlier work.

  5. Numerical simulations of electrohydrodynamic evolution of thin polymer films

    NASA Astrophysics Data System (ADS)

    Borglum, Joshua Christopher

    Recently developed needleless electrospinning and electrolithography are two successful techniques that have been utilized extensively for low-cost, scalable, and continuous nano-fabrication. Rational understanding of the electrohydrodynamic principles underneath these nano-manufacturing methods is crucial to fabrication of continuous nanofibers and patterned thin films. This research project is to formulate robust, high-efficiency finite-difference Fourier spectral methods to simulate the electrohydrodynamic evolution of thin polymer films. Two thin-film models were considered and refined. The first was based on reduced lubrication theory; the second further took into account the effect of solvent drying and dewetting of the substrate. Fast Fourier Transform (FFT) based spectral method was integrated into the finite-difference algorithms for fast, accurately solving the governing nonlinear partial differential equations. The present methods have been used to examine the dependencies of the evolving surface features of the thin films upon the model parameters. The present study can be used for fast, controllable nanofabrication.

  6. Optimal layout design of obstacles for panic evacuation using differential evolution

    NASA Astrophysics Data System (ADS)

    Zhao, Yongxiang; Li, Meifang; Lu, Xin; Tian, Lijun; Yu, Zhiyong; Huang, Kai; Wang, Yana; Li, Ting

    2017-01-01

    To improve the pedestrian outflow in panic situations by suitably placing an obstacle in front of the exit, it is vital to understand the physical mechanism behind the evacuation efficiency enhancement. In this paper, a robust differential evolution is firstly employed to optimize the geometrical parameters of different shaped obstacles in order to achieve an optimal evacuation efficiency. Moreover, it is found that all the geometrical parameters of obstacles could markedly influence the evacuation efficiency of pedestrians, and the best way for achieving an optimal pedestrian outflow is to slightly shift the obstacle from the center of the exit which is consistent with findings of extant literature. Most importantly, by analyzing the profiles of density, velocity and specific flow, as well as the spatial distribution of crowd pressure, we have proven that placing an obstacle in panic situations does not reduce or absorb the pressure in the region of exit, on the contrary, promotes the pressure to a much higher level, hence the physical mechanism behind the evacuation efficiency enhancement is not a pressure decrease in the region of exit, but a significant reduction of high density region by effective separation in space which finally causes the increasing of escape speed and evacuation outflow. Finally, it is clearly demonstrated that the panel-like obstacle is considerably more robust and stable than the pillar-like obstacle to guarantee the enhancement of evacuation efficiency under different initial pedestrian distributions, different initial crowd densities as well as different desired velocities.

  7. The (De-)evolution of Evolution Games: A Content Analysis of the Representation of Evolution Through Natural Selection in Digital Games

    NASA Astrophysics Data System (ADS)

    Leith, Alex P.; Ratan, Rabindra A.; Wohn, Donghee Yvette

    2016-08-01

    Given the diversity and complexity of education game mechanisms and topics, this article contributes to a theoretical understanding of how game mechanisms "map" to educational topics through inquiry-based learning. Namely, the article examines the presence of evolution through natural selection (ENS) in digital games. ENS is a fundamentally important and widely misunderstood theory. This analysis of ENS portrayal in digital games provides insight into the use of games in teaching ENS. Systematic database search results were coded for the three principles of ENS: phenotypic variation, differential fitness, and fitness heritability. Though thousands of games use the term evolution, few presented elements of evolution, and even fewer contained all principles of ENS. Games developed to specifically teach evolution were difficult to find through Web searches. These overall deficiencies in ENS games reflect the inherent incompatibility between game control elements and the automatic process of ENS.

  8. Evolution, functional differentiation, and co-expression of the RLK gene family revealed in Jilin ginseng, Panax ginseng C.A. Meyer.

    PubMed

    Lin, Yanping; Wang, Kangyu; Li, Xiangyu; Sun, Chunyu; Yin, Rui; Wang, Yanfang; Wang, Yi; Zhang, Meiping

    2018-02-21

    Most genes in a genome exist in the form of a gene family; therefore, it is necessary to have knowledge of how a gene family functions to comprehensively understand organismal biology. The receptor-like kinase (RLK)-encoding gene family is one of the most important gene families in plants. It plays important roles in biotic and abiotic stress tolerances, and growth and development. However, little is known about the functional differentiation and relationships among the gene members within a gene family in plants. This study has isolated 563 RLK genes (designated as PgRLK genes) expressed in Jilin ginseng (Panax ginseng C.A. Meyer), investigated their evolution, and deciphered their functional diversification and relationships. The PgRLK gene family is highly diverged and formed into eight types. The LRR type is the earliest and most prevalent, while only the Lec type originated after P. ginseng evolved. Furthermore, although the members of the PgRLK gene family all encode receptor-like protein kinases and share conservative domains, they are functionally very diverse, participating in numerous biological processes. The expressions of different members of the PgRLK gene family are extremely variable within a tissue, at a developmental stage and in the same cultivar, but most of the genes tend to express correlatively, forming a co-expression network. These results not only provide a deeper and comprehensive understanding of the evolution, functional differentiation and correlation of a gene family in plants, but also an RLK genic resource useful for enhanced ginseng genetic improvement.

  9. Evolution of synchronization and desynchronization in digital organisms.

    PubMed

    Knoester, David B; McKinley, Philip K

    2011-01-01

    We present a study in the evolution of temporal behavior, specifically synchronization and desynchronization, through digital evolution and group selection. In digital evolution, a population of self-replicating computer programs exists in a user-defined computational environment and is subject to instruction-level mutations and natural selection. Group selection links the survival of the individual to the survival of its group, thus encouraging cooperation. Previous approaches to engineering synchronization and desynchronization algorithms have taken inspiration from nature: In the well-known firefly model, the only form of communication between agents is in the form of flash messages among neighbors. Here we demonstrate that populations of digital organisms, provided with a similar mechanism and minimal information about their environment, are capable of evolving algorithms for synchronization and desynchronization, and that the evolved behaviors are robust to message loss. We further describe how the evolved behavior for synchronization mimics that of the well-known Ermentrout model for firefly synchronization in biology. In addition to discovering self-organizing behaviors for distributed computing systems, this result indicates that digital evolution may be used to further our understanding of synchronization in biology.

  10. Structure and structure-preserving algorithms for plasma physics

    NASA Astrophysics Data System (ADS)

    Morrison, P. J.

    2016-10-01

    Conventional simulation studies of plasma physics are based on numerically solving the underpinning differential (or integro-differential) equations. Usual algorithms in general do not preserve known geometric structure of the physical systems, such as the local energy-momentum conservation law, Casimir invariants, and the symplectic structure (Poincaré invariants). As a consequence, numerical errors may accumulate coherently with time and long-term simulation results may be unreliable. Recently, a series of geometric algorithms that preserve the geometric structures resulting from the Hamiltonian and action principle (HAP) form of theoretical models in plasma physics have been developed by several authors. The superiority of these geometric algorithms has been demonstrated with many test cases. For example, symplectic integrators for guiding-center dynamics have been constructed to preserve the noncanonical symplectic structures and bound the energy-momentum errors for all simulation time-steps; variational and symplectic algorithms have been discovered and successfully applied to the Vlasov-Maxwell system, MHD, and other magnetofluid equations as well. Hamiltonian truncations of the full Vlasov-Maxwell system have opened the field of discrete gyrokinetics and led to the GEMPIC algorithm. The vision that future numerical capabilities in plasma physics should be based on structure-preserving geometric algorithms will be presented. It will be argued that the geometric consequences of HAP form and resulting geometric algorithms suitable for plasma physics studies cannot be adapted from existing mathematical literature but, rather, need to be discovered and worked out by theoretical plasma physicists. The talk will review existing HAP structures of plasma physics for a variety of models, and how they have been adapted for numerical implementation. Supported by DOE DE-FG02-04ER-54742.

  11. An Investigation of Generalized Differential Evolution Metaheuristic for Multiobjective Optimal Crop-Mix Planning Decision

    PubMed Central

    Olugbara, Oludayo

    2014-01-01

    This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem. PMID:24883369

  12. Macroscopic dielectric function within time-dependent density functional theory—Real time evolution versus the Casida approach

    NASA Astrophysics Data System (ADS)

    Sander, Tobias; Kresse, Georg

    2017-02-01

    Linear optical properties can be calculated by solving the time-dependent density functional theory equations. Linearization of the equation of motion around the ground state orbitals results in the so-called Casida equation, which is formally very similar to the Bethe-Salpeter equation. Alternatively one can determine the spectral functions by applying an infinitely short electric field in time and then following the evolution of the electron orbitals and the evolution of the dipole moments. The long wavelength response function is then given by the Fourier transformation of the evolution of the dipole moments in time. In this work, we compare the results and performance of these two approaches for the projector augmented wave method. To allow for large time steps and still rely on a simple difference scheme to solve the differential equation, we correct for the errors in the frequency domain, using a simple analytic equation. In general, we find that both approaches yield virtually indistinguishable results. For standard density functionals, the time evolution approach is, with respect to the computational performance, clearly superior compared to the solution of the Casida equation. However, for functionals including nonlocal exchange, the direct solution of the Casida equation is usually much more efficient, even though it scales less beneficial with the system size. We relate this to the large computational prefactors in evaluating the nonlocal exchange, which renders the time evolution algorithm fairly inefficient.

  13. Scaled Runge-Kutta algorithms for handling dense output

    NASA Technical Reports Server (NTRS)

    Horn, M. K.

    1981-01-01

    Low order Runge-Kutta algorithms are developed which determine the solution of a system of ordinary differential equations at any point within a given integration step, as well as at the end of each step. The scaled Runge-Kutta methods are designed to be used with existing Runge-Kutta formulas, using the derivative evaluations of these defining algorithms as the core of the system. For a slight increase in computing time, the solution may be generated within the integration step, improving the efficiency of the Runge-Kutta algorithms, since the step length need no longer be severely reduced to coincide with the desired output point. Scaled Runge-Kutta algorithms are presented for orders 3 through 5, along with accuracy comparisons between the defining algorithms and their scaled versions for a test problem.

  14. Differentially private distributed logistic regression using private and public data

    PubMed Central

    2014-01-01

    Background Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. Methodology In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. Experiments and results We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Conclusion Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee. PMID:25079786

  15. From differential to difference equations for first order ODEs

    NASA Technical Reports Server (NTRS)

    Freed, Alan D.; Walker, Kevin P.

    1991-01-01

    When constructing an algorithm for the numerical integration of a differential equation, one should first convert the known ordinary differential equation (ODE) into an ordinary difference equation. Given this difference equation, one can develop an appropriate numerical algorithm. This technical note describes the derivation of two such ordinary difference equations applicable to a first order ODE. The implicit ordinary difference equation has the same asymptotic expansion as the ODE itself, whereas the explicit ordinary difference equation has an asymptotic that is similar in structure but different in value when compared with that of the ODE.

  16. Differential Expression of Non-Coding RNAs and Continuous Evolution of the X Chromosome in Testicular Transcriptome of Two Mouse Species

    PubMed Central

    Homolka, David; Ivanek, Robert; Forejt, Jiri; Jansa, Petr

    2011-01-01

    Background Tight regulation of testicular gene expression is a prerequisite for male reproductive success, while differentiation of gene activity in spermatogenesis is important during speciation. Thus, comparison of testicular transcriptomes between closely related species can reveal unique regulatory patterns and shed light on evolutionary constraints separating the species. Methodology/Principal Findings Here, we compared testicular transcriptomes of two closely related mouse species, Mus musculus and Mus spretus, which diverged more than one million years ago. We analyzed testicular expression using tiling arrays overlapping Chromosomes 2, X, Y and mitochondrial genome. An excess of differentially regulated non-coding RNAs was found on Chromosome 2 including the intronic antisense RNAs, intergenic RNAs and premature forms of Piwi-interacting RNAs (piRNAs). Moreover, striking difference was found in the expression of X-linked G6pdx gene, the parental gene of the autosomal retrogene G6pd2. Conclusions/Significance The prevalence of non-coding RNAs among differentially expressed transcripts indicates their role in species-specific regulation of spermatogenesis. The postmeiotic expression of G6pdx in Mus spretus points towards the continuous evolution of X-chromosome silencing and provides an example of expression change accompanying the out-of-the X-chromosomal retroposition. PMID:21347268

  17. Lie symmetries for systems of evolution equations

    NASA Astrophysics Data System (ADS)

    Paliathanasis, Andronikos; Tsamparlis, Michael

    2018-01-01

    The Lie symmetries for a class of systems of evolution equations are studied. The evolution equations are defined in a bimetric space with two Riemannian metrics corresponding to the space of the independent and dependent variables of the differential equations. The exact relation of the Lie symmetries with the collineations of the bimetric space is determined.

  18. Differential correlation for sequencing data.

    PubMed

    Siska, Charlotte; Kechris, Katerina

    2017-01-19

    Several methods have been developed to identify differential correlation (DC) between pairs of molecular features from -omics studies. Most DC methods have only been tested with microarrays and other platforms producing continuous and Gaussian-like data. Sequencing data is in the form of counts, often modeled with a negative binomial distribution making it difficult to apply standard correlation metrics. We have developed an R package for identifying DC called Discordant which uses mixture models for correlations between features and the Expectation Maximization (EM) algorithm for fitting parameters of the mixture model. Several correlation metrics for sequencing data are provided and tested using simulations. Other extensions in the Discordant package include additional modeling for different types of differential correlation, and faster implementation, using a subsampling routine to reduce run-time and address the assumption of independence between molecular feature pairs. With simulations and breast cancer miRNA-Seq and RNA-Seq data, we find that Spearman's correlation has the best performance among the tested correlation methods for identifying differential correlation. Application of Spearman's correlation in the Discordant method demonstrated the most power in ROC curves and sensitivity/specificity plots, and improved ability to identify experimentally validated breast cancer miRNA. We also considered including additional types of differential correlation, which showed a slight reduction in power due to the additional parameters that need to be estimated, but more versatility in applications. Finally, subsampling within the EM algorithm considerably decreased run-time with negligible effect on performance. A new method and R package called Discordant is presented for identifying differential correlation with sequencing data. Based on comparisons with different correlation metrics, this study suggests Spearman's correlation is appropriate for sequencing data

  19. Generalized Grover's Algorithm for Multiple Phase Inversion States

    NASA Astrophysics Data System (ADS)

    Byrnes, Tim; Forster, Gary; Tessler, Louis

    2018-02-01

    Grover's algorithm is a quantum search algorithm that proceeds by repeated applications of the Grover operator and the Oracle until the state evolves to one of the target states. In the standard version of the algorithm, the Grover operator inverts the sign on only one state. Here we provide an exact solution to the problem of performing Grover's search where the Grover operator inverts the sign on N states. We show the underlying structure in terms of the eigenspectrum of the generalized Hamiltonian, and derive an appropriate initial state to perform the Grover evolution. This allows us to use the quantum phase estimation algorithm to solve the search problem in this generalized case, completely bypassing the Grover algorithm altogether. We obtain a time complexity of this case of √{D /Mα }, where D is the search space dimension, M is the number of target states, and α ≈1 , which is close to the optimal scaling.

  20. Conical differentiability for evolution variational inequalities

    NASA Astrophysics Data System (ADS)

    Jarušek, Jiří; Krbec, Miroslav; Rao, Murali; Sokołowski, Jan

    The conical differentiability of solutions to the parabolic variational inequality with respect to the right-hand side is proved in the paper. From one side the result is based on the Lipschitz continuity in H {1}/{2},1 (Q) of solutions to the variational inequality with respect to the right-hand side. On the other side, in view of the polyhedricity of the convex cone K={v∈ H;v |Σ c⩾0,v |Σ d=0}, we prove new results on sensitivity analysis of parabolic variational inequalities. Therefore, we have a positive answer to the question raised by Fulbert Mignot (J. Funct. Anal. 22 (1976) 25-32).

  1. A Bayesian algorithm for detecting differentially expressed proteins and its application in breast cancer research

    NASA Astrophysics Data System (ADS)

    Santra, Tapesh; Delatola, Eleni Ioanna

    2016-07-01

    Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.

  2. Differential Activity-Driven Instabilities in Biphasic Active Matter

    NASA Astrophysics Data System (ADS)

    Weber, Christoph A.; Rycroft, Chris H.; Mahadevan, L.

    2018-06-01

    Active stresses can cause instabilities in contractile gels and living tissues. Here we provide a generic hydrodynamic theory that treats these systems as a mixture of two phases of varying activity and different mechanical properties. We find that differential activity between the phases causes a uniform mixture to undergo a demixing instability. We follow the nonlinear evolution of the instability and characterize a phase diagram of the resulting patterns. Our study complements other instability mechanisms in mixtures driven by differential adhesion, differential diffusion, differential growth, and differential motion.

  3. Ionospheric-thermospheric UV tomography: 1. Image space reconstruction algorithms

    NASA Astrophysics Data System (ADS)

    Dymond, K. F.; Budzien, S. A.; Hei, M. A.

    2017-03-01

    We present and discuss two algorithms of the class known as Image Space Reconstruction Algorithms (ISRAs) that we are applying to the solution of large-scale ionospheric tomography problems. ISRAs have several desirable features that make them useful for ionospheric tomography. In addition to producing nonnegative solutions, ISRAs are amenable to sparse-matrix formulations and are fast, stable, and robust. We present the results of our studies of two types of ISRA: the Least Squares Positive Definite and the Richardson-Lucy algorithms. We compare their performance to the Multiplicative Algebraic Reconstruction and Conjugate Gradient Least Squares algorithms. We then discuss the use of regularization in these algorithms and present our new approach based on regularization to a partial differential equation.

  4. Conservative algorithms for non-Maxwellian plasma kinetics

    DOE PAGES

    Le, Hai P.; Cambier, Jean -Luc

    2017-12-08

    Here, we present a numerical model and a set of conservative algorithms for Non-Maxwellian plasma kinetics with inelastic collisions. These algorithms self-consistently solve for the time evolution of an isotropic electron energy distribution function interacting with an atomic state distribution function of an arbitrary number of levels through collisional excitation, deexcitation, as well as ionization and recombination. Electron-electron collisions, responsible for thermalization of the electron distribution, are also included in the model. The proposed algorithms guarantee mass/charge and energy conservation in a single step, and is applied to the case of non-uniform gridding of the energy axis in the phasemore » space of the electron distribution function. Numerical test cases are shown to demonstrate the accuracy of the method and its conservation properties.« less

  5. Wavelength Selection Method Based on Differential Evolution for Precise Quantitative Analysis Using Terahertz Time-Domain Spectroscopy.

    PubMed

    Li, Zhi; Chen, Weidong; Lian, Feiyu; Ge, Hongyi; Guan, Aihong

    2017-12-01

    Quantitative analysis of component mixtures is an important application of terahertz time-domain spectroscopy (THz-TDS) and has attracted broad interest in recent research. Although the accuracy of quantitative analysis using THz-TDS is affected by a host of factors, wavelength selection from the sample's THz absorption spectrum is the most crucial component. The raw spectrum consists of signals from the sample and scattering and other random disturbances that can critically influence the quantitative accuracy. For precise quantitative analysis using THz-TDS, the signal from the sample needs to be retained while the scattering and other noise sources are eliminated. In this paper, a novel wavelength selection method based on differential evolution (DE) is investigated. By performing quantitative experiments on a series of binary amino acid mixtures using THz-TDS, we demonstrate the efficacy of the DE-based wavelength selection method, which yields an error rate below 5%.

  6. Genetic algorithm enhanced by machine learning in dynamic aperture optimization

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

    Li, Yongjun; Cheng, Weixing; Yu, Li Hua

    With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

  7. Genetic algorithm enhanced by machine learning in dynamic aperture optimization

    DOE PAGES

    Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...

    2018-05-29

    With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

  8. Shaping asteroid models using genetic evolution (SAGE)

    NASA Astrophysics Data System (ADS)

    Bartczak, P.; Dudziński, G.

    2018-02-01

    In this work, we present SAGE (shaping asteroid models using genetic evolution), an asteroid modelling algorithm based solely on photometric lightcurve data. It produces non-convex shapes, orientations of the rotation axes and rotational periods of asteroids. The main concept behind a genetic evolution algorithm is to produce random populations of shapes and spin-axis orientations by mutating a seed shape and iterating the process until it converges to a stable global minimum. We tested SAGE on five artificial shapes. We also modelled asteroids 433 Eros and 9 Metis, since ground truth observations for them exist, allowing us to validate the models. We compared the derived shape of Eros with the NEAR Shoemaker model and that of Metis with adaptive optics and stellar occultation observations since other models from various inversion methods were available for Metis.

  9. A nudging-based data assimilation method: the Back and Forth Nudging (BFN) algorithm

    NASA Astrophysics Data System (ADS)

    Auroux, D.; Blum, J.

    2008-03-01

    This paper deals with a new data assimilation algorithm, called Back and Forth Nudging. The standard nudging technique consists in adding to the equations of the model a relaxation term that is supposed to force the observations to the model. The BFN algorithm consists in repeatedly performing forward and backward integrations of the model with relaxation (or nudging) terms, using opposite signs in the direct and inverse integrations, so as to make the backward evolution numerically stable. This algorithm has first been tested on the standard Lorenz model with discrete observations (perfect or noisy) and compared with the variational assimilation method. The same type of study has then been performed on the viscous Burgers equation, comparing again with the variational method and focusing on the time evolution of the reconstruction error, i.e. the difference between the reference trajectory and the identified one over a time period composed of an assimilation period followed by a prediction period. The possible use of the BFN algorithm as an initialization for the variational method has also been investigated. Finally the algorithm has been tested on a layered quasi-geostrophic model with sea-surface height observations. The behaviours of the two algorithms have been compared in the presence of perfect or noisy observations, and also for imperfect models. This has allowed us to reach a conclusion concerning the relative performances of the two algorithms.

  10. Assessing the Suitability of the ClOud Reflection Algorithm (CORA) in Modelling the Evolution of an Artificial Plasma Cloud in the Ionosphere

    NASA Astrophysics Data System (ADS)

    Jackson-Booth, N.

    2016-12-01

    Artificial Ionospheric Modification (AIM) attempts to modify the ionosphere in order to alter the propagation environment. It can be achieved through injecting the ionosphere with aerosols, chemicals or radio signals. The effects of any such release can be detected through the deployment of sensors, including ground based high frequency (HF) sounders. During the Metal Oxide Space Clouds (MOSC) experiment (undertaken in April/May 2013 in the Kwajalein Atoll, part of the Marshall Islands) several oblique ionograms were recorded from a ground based HF system. These ionograms were collected over multiple geometries and allowed the effects on the HF propagation environment to be understood. These ionograms have subsequently been used in the ClOud Reflection Algorithm (CORA) to attempt to model the evolution of the cloud following release. This paper describes the latest validation results from CORA, both from testing against ionograms, but also other independent models of cloud evolution from MOSC. For all testing the various cloud models (including that generated by CORA) were incorporated into a background ionosphere through which a 3D numerical ray trace was run to produce synthetic ionograms that could be compared with the ionograms recorded during MOSC.

  11. The fast debris evolution model

    NASA Astrophysics Data System (ADS)

    Lewis, H. G.; Swinerd, G. G.; Newland, R. J.; Saunders, A.

    2009-09-01

    The 'particles-in-a-box' (PIB) model introduced by Talent [Talent, D.L. Analytic model for orbital debris environmental management. J. Spacecraft Rocket, 29 (4), 508-513, 1992.] removed the need for computer-intensive Monte Carlo simulation to predict the gross characteristics of an evolving debris environment. The PIB model was described using a differential equation that allows the stability of the low Earth orbit (LEO) environment to be tested by a straightforward analysis of the equation's coefficients. As part of an ongoing research effort to investigate more efficient approaches to evolutionary modelling and to develop a suite of educational tools, a new PIB model has been developed. The model, entitled Fast Debris Evolution (FADE), employs a first-order differential equation to describe the rate at which new objects ⩾10 cm are added and removed from the environment. Whilst Talent [Talent, D.L. Analytic model for orbital debris environmental management. J. Spacecraft Rocket, 29 (4), 508-513, 1992.] based the collision theory for the PIB approach on collisions between gas particles and adopted specific values for the parameters of the model from a number of references, the form and coefficients of the FADE model equations can be inferred from the outputs of future projections produced by high-fidelity models, such as the DAMAGE model. The FADE model has been implemented as a client-side, web-based service using JavaScript embedded within a HTML document. Due to the simple nature of the algorithm, FADE can deliver the results of future projections immediately in a graphical format, with complete user-control over key simulation parameters. Historical and future projections for the ⩾10 cm LEO debris environment under a variety of different scenarios are possible, including business as usual, no future launches, post-mission disposal and remediation. A selection of results is presented with comparisons with predictions made using the DAMAGE environment model

  12. Evolution of abdominal wall reconstruction: development of a unified algorithm with improved outcomes.

    PubMed

    Koltz, Peter F; Frey, Jordan D; Bell, Derek E; Girotto, John A; Christiano, Jose G; Langstein, Howard N

    2013-11-01

    Ventral hernia repair (VHR) continues to evolve and now frequently includes some form of component separation (CS) for large defects. To determine the optimal technique for VHR, we evaluated our outcomes before and after we refined and simplified our algorithm for repair. One hundred five consecutive patients undergoing VHR for large midline hernias over 9 years were examined. Patients were divided into those operated on after (group 1) and before (group 2) the institution of our simplified algorithm. Our algorithm emphasizes careful patient selection and a stepwise approach including, but not limited to, bilateral CS if appropriate, preservation of large perforators, retrorectus mesh placement as appropriate, linea alba or midline fascial closure, and vertical panniculectomy. Primary outcomes evaluated included wound infection, dehiscence, and hernia recurrence. Seventy-eight (74.3%) patients underwent repair using our algorithm (group 1), whereas 27 (25.7%) underwent repair before utilization of this algorithm (group 2). Ninety-eight (93.3%) underwent CS, whereas 7 (6.7%) underwent another form of VHR. There was no significant difference in patient age or defect size. The mean follow-up period in days for patients in group 1 and group 2 were 184.02 and 526.06, respectively (P < 0.001). Hernia recurrence in group 1 was 2.6% versus 29.6% in group 2 (P < 0.001). The incidence of wound infection in group 1 was 10.3%, whereas that in group 2 was 33.3% (P < 0.001). The rate of wound dehiscence in group 1 was 17.9% versus 25.9% in group 2 (P < 0.001). Simplifying and unifying our algorithm for VHR, notably with utilization of CS, has yielded improved results. Recurrence and wound healing complications using this approach are favorable compared with published outcomes.

  13. The systems biology simulation core algorithm

    PubMed Central

    2013-01-01

    Background With the increasing availability of high dimensional time course data for metabolites, genes, and fluxes, the mathematical description of dynamical systems has become an essential aspect of research in systems biology. Models are often encoded in formats such as SBML, whose structure is very complex and difficult to evaluate due to many special cases. Results This article describes an efficient algorithm to solve SBML models that are interpreted in terms of ordinary differential equations. We begin our consideration with a formal representation of the mathematical form of the models and explain all parts of the algorithm in detail, including several preprocessing steps. We provide a flexible reference implementation as part of the Systems Biology Simulation Core Library, a community-driven project providing a large collection of numerical solvers and a sophisticated interface hierarchy for the definition of custom differential equation systems. To demonstrate the capabilities of the new algorithm, it has been tested with the entire SBML Test Suite and all models of BioModels Database. Conclusions The formal description of the mathematics behind the SBML format facilitates the implementation of the algorithm within specifically tailored programs. The reference implementation can be used as a simulation backend for Java™-based programs. Source code, binaries, and documentation can be freely obtained under the terms of the LGPL version 3 from http://simulation-core.sourceforge.net. Feature requests, bug reports, contributions, or any further discussion can be directed to the mailing list simulation-core-development@lists.sourceforge.net. PMID:23826941

  14. Enhanced Handover Decision Algorithm in Heterogeneous Wireless Network

    PubMed Central

    Abdullah, Radhwan Mohamed; Zukarnain, Zuriati Ahmad

    2017-01-01

    Transferring a huge amount of data between different network locations over the network links depends on the network’s traffic capacity and data rate. Traditionally, a mobile device may be moved to achieve the operations of vertical handover, considering only one criterion, that is the Received Signal Strength (RSS). The use of a single criterion may cause service interruption, an unbalanced network load and an inefficient vertical handover. In this paper, we propose an enhanced vertical handover decision algorithm based on multiple criteria in the heterogeneous wireless network. The algorithm consists of three technology interfaces: Long-Term Evolution (LTE), Worldwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). It also employs three types of vertical handover decision algorithms: equal priority, mobile priority and network priority. The simulation results illustrate that the three types of decision algorithms outperform the traditional network decision algorithm in terms of handover number probability and the handover failure probability. In addition, it is noticed that the network priority handover decision algorithm produces better results compared to the equal priority and the mobile priority handover decision algorithm. Finally, the simulation results are validated by the analytical model. PMID:28708067

  15. Simulating large atmospheric phase screens using a woofer-tweeter algorithm.

    PubMed

    Buscher, David F

    2016-10-03

    We describe an algorithm for simulating atmospheric wavefront perturbations over ranges of spatial and temporal scales spanning more than 4 orders of magnitude. An open-source implementation of the algorithm written in Python can simulate the evolution of the perturbations more than an order-of-magnitude faster than real time. Testing of the implementation using metrics appropriate to adaptive optics systems and long-baseline interferometers show accuracies at the few percent level or better.

  16. DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

    NASA Astrophysics Data System (ADS)

    Tchagang, Alain B.; Tewfik, Ahmed H.

    2006-12-01

    Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.

  17. Comparison of Three Instructional Sequences for the Addition and Subtraction Algorithms. Technical Report 273.

    ERIC Educational Resources Information Center

    Wiles, Clyde A.

    The study's purpose was to investigate the differential effects on the achievement of second-grade students that could be attributed to three instructional sequences for the learning of the addition and subtraction algorithms. One sequence presented the addition algorithm first (AS), the second presented the subtraction algorithm first (SA), and…

  18. Internal constitution and evolution of the moon.

    NASA Technical Reports Server (NTRS)

    Solomon, S. C.; Toksoz, M. N.

    1973-01-01

    The composition, structure and evolution of the moon's interior are narrowly constrained by a large assortment of physical and chemical data. Models of the thermal evolution of the moon that fit the chronology of igneous activity on the lunar surface, the stress history of the lunar lithosphere implied by the presence of mascons, and the surface concentrations of radioactive elements, involve extensive differentiation early in lunar history. This differentiation may be the result of rapid accretion and large-scale melting or of primary chemical layering during accretion; differences in present-day temperatures for these two possibilities are significant only in the inner 1000 km of the moon and may not be resolvable.

  19. An improved NSGA - II algorithm for mixed model assembly line balancing

    NASA Astrophysics Data System (ADS)

    Wu, Yongming; Xu, Yanxia; Luo, Lifei; Zhang, Han; Zhao, Xudong

    2018-05-01

    Aiming at the problems of assembly line balancing and path optimization for material vehicles in mixed model manufacturing system, a multi-objective mixed model assembly line (MMAL), which is based on optimization objectives, influencing factors and constraints, is established. According to the specific situation, an improved NSGA-II algorithm based on ecological evolution strategy is designed. An environment self-detecting operator, which is used to detect whether the environment changes, is adopted in the algorithm. Finally, the effectiveness of proposed model and algorithm is verified by examples in a concrete mixing system.

  20. Mapping the evolution of scientific fields.

    PubMed

    Herrera, Mark; Roberts, David C; Gulbahce, Natali

    2010-05-04

    Despite the apparent cross-disciplinary interactions among scientific fields, a formal description of their evolution is lacking. Here we describe a novel approach to study the dynamics and evolution of scientific fields using a network-based analysis. We build an idea network consisting of American Physical Society Physics and Astronomy Classification Scheme (PACS) numbers as nodes representing scientific concepts. Two PACS numbers are linked if there exist publications that reference them simultaneously. We locate scientific fields using a community finding algorithm, and describe the time evolution of these fields over the course of 1985-2006. The communities we identify map to known scientific fields, and their age depends on their size and activity. We expect our approach to quantifying the evolution of ideas to be relevant for making predictions about the future of science and thus help to guide its development.

  1. Numerical Algorithms Based on Biorthogonal Wavelets

    NASA Technical Reports Server (NTRS)

    Ponenti, Pj.; Liandrat, J.

    1996-01-01

    Wavelet bases are used to generate spaces of approximation for the resolution of bidimensional elliptic and parabolic problems. Under some specific hypotheses relating the properties of the wavelets to the order of the involved operators, it is shown that an approximate solution can be built. This approximation is then stable and converges towards the exact solution. It is designed such that fast algorithms involving biorthogonal multi resolution analyses can be used to resolve the corresponding numerical problems. Detailed algorithms are provided as well as the results of numerical tests on partial differential equations defined on the bidimensional torus.

  2. A private DNA motif finding algorithm.

    PubMed

    Chen, Rui; Peng, Yun; Choi, Byron; Xu, Jianliang; Hu, Haibo

    2014-08-01

    With the increasing availability of genomic sequence data, numerous methods have been proposed for finding DNA motifs. The discovery of DNA motifs serves a critical step in many biological applications. However, the privacy implication of DNA analysis is normally neglected in the existing methods. In this work, we propose a private DNA motif finding algorithm in which a DNA owner's privacy is protected by a rigorous privacy model, known as ∊-differential privacy. It provides provable privacy guarantees that are independent of adversaries' background knowledge. Our algorithm makes use of the n-gram model and is optimized for processing large-scale DNA sequences. We evaluate the performance of our algorithm over real-life genomic data and demonstrate the promise of integrating privacy into DNA motif finding. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Structure and Evolution of the Lunar Interior

    NASA Technical Reports Server (NTRS)

    Andrews-Hanna, J. C.; Weber, R. C.; Ishihara, Y.; Kamata, S.; Keane, J.; Kiefer, W. S.; Matsuyama, I.; Siegler, M.; Warren, P.

    2017-01-01

    Early in its evolution, the Moon underwent a magma ocean phase leading to its differentiation into a feldspathic crust, cumulate mantle, and iron core. However, far from the simplest view of a uniform plagioclase flotation crust, the present-day crust of the Moon varies greatly in thickness, composition, and physical properties. Recent significant improvements in both data and analysis techniques have yielded fundamental advances in our understanding of the structure and evolution of the lunar interior. The structure of the crust is revealed by gravity, topography, magnetics, seismic, radar, electromagnetic, and VNIR remote sensing data. The mantle structure of the Moon is revealed primarily by seismic and laser ranging data. Together, this data paints a picture of a Moon that is heterogeneous in all directions and across all scales, whose structure is a result of its unique formation, differentiation, and subsequent evolution. This brief review highlights a small number of recent advances in our understanding of lunar structure.

  4. Utility of the k-means clustering algorithm in differentiating apparent diffusion coefficient values of benign and malignant neck pathologies.

    PubMed

    Srinivasan, A; Galbán, C J; Johnson, T D; Chenevert, T L; Ross, B D; Mukherji, S K

    2010-04-01

    Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies. MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADC(L); high, ADC-ADC(H)) and 3 partitions (ADC(L); intermediate, ADC-ADC(I); ADC(H)) by using the K-means clustering algorithm. An unpaired 2-tailed Student t test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies. A statistically significant difference between the mean ADC(L) clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers (P = .03 and .022, respectively) and the 2-cluster model of reader 2 (P = .04), with the other metrics (ADC(H), ADC(I); whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADC(H) and ADC(L) in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: P = .008, area under curve = 0.850; 3 clusters: P = .01, area under curve = 0.825). The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.

  5. [Algorithm of toxigenic genetically altered Vibrio cholerae El Tor biovar strain identification].

    PubMed

    Smirnova, N I; Agafonov, D A; Zadnova, S P; Cherkasov, A V; Kutyrev, V V

    2014-01-01

    Development of an algorithm of genetically altered Vibrio cholerae biovar El Tor strai identification that ensures determination of serogroup, serovar and biovar of the studied isolate based on pheno- and genotypic properties, detection of genetically altered cholera El Tor causative agents, their differentiation by epidemic potential as well as evaluation of variability of key pathogenicity genes. Complex analysis of 28 natural V. cholerae strains was carried out by using traditional microbiological methods, PCR and fragmentary sequencing. An algorithm of toxigenic genetically altered V. cholerae biovar El Tor strain identification was developed that includes 4 stages: determination of serogroup, serovar and biovar based on phenotypic properties, confirmation of serogroup and biovar based on molecular-genetic properties determination of strains as genetically altered, differentiation of genetically altered strains by their epidemic potential and detection of ctxB and tcpA key pathogenicity gene polymorphism. The algorithm is based on the use of traditional microbiological methods, PCR and sequencing of gene fragments. The use of the developed algorithm will increase the effectiveness of detection of genetically altered variants of the cholera El Tor causative agent, their differentiation by epidemic potential and will ensure establishment of polymorphism of genes that code key pathogenicity factors for determination of origins of the strains and possible routes of introduction of the infection.

  6. Evolution of semilocal string networks. II. Velocity estimators

    NASA Astrophysics Data System (ADS)

    Lopez-Eiguren, A.; Urrestilla, J.; Achúcarro, A.; Avgoustidis, A.; Martins, C. J. A. P.

    2017-07-01

    We continue a comprehensive numerical study of semilocal string networks and their cosmological evolution. These can be thought of as hybrid networks comprised of (nontopological) string segments, whose core structure is similar to that of Abelian Higgs vortices, and whose ends have long-range interactions and behavior similar to that of global monopoles. Our study provides further evidence of a linear scaling regime, already reported in previous studies, for the typical length scale and velocity of the network. We introduce a new algorithm to identify the position of the segment cores. This allows us to determine the length and velocity of each individual segment and follow their evolution in time. We study the statistical distribution of segment lengths and velocities for radiation- and matter-dominated evolution in the regime where the strings are stable. Our segment detection algorithm gives higher length values than previous studies based on indirect detection methods. The statistical distribution shows no evidence of (anti)correlation between the speed and the length of the segments.

  7. Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading

    PubMed Central

    Deng, Shangkun; Sakurai, Akito

    2014-01-01

    Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits. PMID:25097891

  8. Integrated model of multiple kernel learning and differential evolution for EUR/USD trading.

    PubMed

    Deng, Shangkun; Sakurai, Akito

    2014-01-01

    Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits.

  9. In silico modelling of directed evolution: Implications for experimental design and stepwise evolution.

    PubMed

    Wedge, David C; Rowe, William; Kell, Douglas B; Knowles, Joshua

    2009-03-07

    We model the process of directed evolution (DE) in silico using genetic algorithms. Making use of the NK fitness landscape model, we analyse the effects of mutation rate, crossover and selection pressure on the performance of DE. A range of values of K, the epistatic interaction of the landscape, are considered, and high- and low-throughput modes of evolution are compared. Our findings suggest that for runs of or around ten generations' duration-as is typical in DE-there is little difference between the way in which DE needs to be configured in the high- and low-throughput regimes, nor across different degrees of landscape epistasis. In all cases, a high selection pressure (but not an extreme one) combined with a moderately high mutation rate works best, while crossover provides some benefit but only on the less rugged landscapes. These genetic algorithms were also compared with a "model-based approach" from the literature, which uses sequential fixing of the problem parameters based on fitting a linear model. Overall, we find that purely evolutionary techniques fare better than do model-based approaches across all but the smoothest landscapes.

  10. Lung evolution as a cipher for physiology

    PubMed Central

    Torday, J. S.; Rehan, V. K.

    2009-01-01

    In the postgenomic era, we need an algorithm to readily translate genes into physiologic principles. The failure to advance biomedicine is due to the false hope raised in the wake of the Human Genome Project (HGP) by the promise of systems biology as a ready means of reconstructing physiology from genes. like the atom in physics, the cell, not the gene, is the smallest completely functional unit of biology. Trying to reassemble gene regulatory networks without accounting for this fundamental feature of evolution will result in a genomic atlas, but not an algorithm for functional genomics. For example, the evolution of the lung can be “deconvoluted” by applying cell-cell communication mechanisms to all aspects of lung biology development, homeostasis, and regeneration/repair. Gene regulatory networks common to these processes predict ontogeny, phylogeny, and the disease-related consequences of failed signaling. This algorithm elucidates characteristics of vertebrate physiology as a cascade of emergent and contingent cellular adaptational responses. By reducing complex physiological traits to gene regulatory networks and arranging them hierarchically in a self-organizing map, like the periodic table of elements in physics, the first principles of physiology will emerge. PMID:19366785

  11. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality.

    PubMed

    Braithwaite, Scott R; Giraud-Carrier, Christophe; West, Josh; Barnes, Michael D; Hanson, Carl Lee

    2016-05-16

    One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data.

  12. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

    PubMed Central

    2016-01-01

    Background One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. PMID:27185366

  13. A parallel algorithm for the two-dimensional time fractional diffusion equation with implicit difference method.

    PubMed

    Gong, Chunye; Bao, Weimin; Tang, Guojian; Jiang, Yuewen; Liu, Jie

    2014-01-01

    It is very time consuming to solve fractional differential equations. The computational complexity of two-dimensional fractional differential equation (2D-TFDE) with iterative implicit finite difference method is O(M(x)M(y)N(2)). In this paper, we present a parallel algorithm for 2D-TFDE and give an in-depth discussion about this algorithm. A task distribution model and data layout with virtual boundary are designed for this parallel algorithm. The experimental results show that the parallel algorithm compares well with the exact solution. The parallel algorithm on single Intel Xeon X5540 CPU runs 3.16-4.17 times faster than the serial algorithm on single CPU core. The parallel efficiency of 81 processes is up to 88.24% compared with 9 processes on a distributed memory cluster system. We do think that the parallel computing technology will become a very basic method for the computational intensive fractional applications in the near future.

  14. Existence and discrete approximation for optimization problems governed by fractional differential equations

    NASA Astrophysics Data System (ADS)

    Bai, Yunru; Baleanu, Dumitru; Wu, Guo-Cheng

    2018-06-01

    We investigate a class of generalized differential optimization problems driven by the Caputo derivative. Existence of weak Carathe ´odory solution is proved by using Weierstrass existence theorem, fixed point theorem and Filippov implicit function lemma etc. Then a numerical approximation algorithm is introduced, and a convergence theorem is established. Finally, a nonlinear programming problem constrained by the fractional differential equation is illustrated and the results verify the validity of the algorithm.

  15. Algorithmic transformation of multi-loop master integrals to a canonical basis with CANONICA

    NASA Astrophysics Data System (ADS)

    Meyer, Christoph

    2018-01-01

    The integration of differential equations of Feynman integrals can be greatly facilitated by using a canonical basis. This paper presents the Mathematica package CANONICA, which implements a recently developed algorithm to automatize the transformation to a canonical basis. This represents the first publicly available implementation suitable for differential equations depending on multiple scales. In addition to the presentation of the package, this paper extends the description of some aspects of the algorithm, including a proof of the uniqueness of canonical forms up to constant transformations.

  16. Selecting materialized views using random algorithm

    NASA Astrophysics Data System (ADS)

    Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi

    2007-04-01

    The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

  17. Using trees to compute approximate solutions to ordinary differential equations exactly

    NASA Technical Reports Server (NTRS)

    Grossman, Robert

    1991-01-01

    Some recent work is reviewed which relates families of trees to symbolic algorithms for the exact computation of series which approximate solutions of ordinary differential equations. It turns out that the vector space whose basis is the set of finite, rooted trees carries a natural multiplication related to the composition of differential operators, making the space of trees an algebra. This algebraic structure can be exploited to yield a variety of algorithms for manipulating vector fields and the series and algebras they generate.

  18. Modeling evolution of the mind and cultures: emotional Sapir-Whorf hypothesis

    NASA Astrophysics Data System (ADS)

    Perlovsky, Leonid I.

    2009-05-01

    Evolution of cultures is ultimately determined by mechanisms of the human mind. The paper discusses the mechanisms of evolution of language from primordial undifferentiated animal cries to contemporary conceptual contents. In parallel with differentiation of conceptual contents, the conceptual contents were differentiated from emotional contents of languages. The paper suggests the neural brain mechanisms involved in these processes. Experimental evidence and theoretical arguments are discussed, including mathematical approaches to cognition and language: modeling fields theory, the knowledge instinct, and the dual model connecting language and cognition. Mathematical results are related to cognitive science, linguistics, and psychology. The paper gives an initial mathematical formulation and mean-field equations for the hierarchical dynamics of both the human mind and culture. In the mind heterarchy operation of the knowledge instinct manifests through mechanisms of differentiation and synthesis. The emotional contents of language are related to language grammar. The conclusion is an emotional version of Sapir-Whorf hypothesis. Cultural advantages of "conceptual" pragmatic cultures, in which emotionality of language is diminished and differentiation overtakes synthesis resulting in fast evolution at the price of self doubts and internal crises are compared to those of traditional cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation. Multi-language, multi-ethnic society might combine the benefits of stability and fast differentiation. Unsolved problems and future theoretical and experimental directions are discussed.

  19. Quantum gates with controlled adiabatic evolutions

    NASA Astrophysics Data System (ADS)

    Hen, Itay

    2015-02-01

    We introduce a class of quantum adiabatic evolutions that we claim may be interpreted as the equivalents of the unitary gates of the quantum gate model. We argue that these gates form a universal set and may therefore be used as building blocks in the construction of arbitrary "adiabatic circuits," analogously to the manner in which gates are used in the circuit model. One implication of the above construction is that arbitrary classical boolean circuits as well as gate model circuits may be directly translated to adiabatic algorithms with no additional resources or complexities. We show that while these adiabatic algorithms fail to exhibit certain aspects of the inherent fault tolerance of traditional quantum adiabatic algorithms, they may have certain other experimental advantages acting as quantum gates.

  20. Evolution and Christian Faith

    NASA Astrophysics Data System (ADS)

    Roughgarden, J. E.

    2006-12-01

    My recent book, Evolution and Christian Faith explores how evolutionary biology can be portrayed from the religious perspective of Christianity. The principal metaphors for evolutionary biology---differential success at breeding and random mutation, probably originate with the dawn of agriculture and clearly occur in the Bible. The central narrative of evolutionary biology can be presented using Biblical passages, providing an account of evolution that is inherently friendly to a Christian perspective. Still, evolutionary biology is far from complete, and problematic areas pertain to species in which the concept of an individual is poorly defined, and to species in which the expression of gender and sexuality depart from Darwin's sexual-selection templates. The present- day controversy in the US about teaching evolution in the schools provides an opportunity to engage the public about science education.

  1. Batch Scheduling for Hybrid Assembly Differentiation Flow Shop to Minimize Total Actual Flow Time

    NASA Astrophysics Data System (ADS)

    Maulidya, R.; Suprayogi; Wangsaputra, R.; Halim, A. H.

    2018-03-01

    A hybrid assembly differentiation flow shop is a three-stage flow shop consisting of Machining, Assembly and Differentiation Stages and producing different types of products. In the machining stage, parts are processed in batches on different (unrelated) machines. In the assembly stage, each part of the different parts is assembled into an assembly product. Finally, the assembled products will further be processed into different types of final products in the differentiation stage. In this paper, we develop a batch scheduling model for a hybrid assembly differentiation flow shop to minimize the total actual flow time defined as the total times part spent in the shop floor from the arrival times until its due date. We also proposed a heuristic algorithm for solving the problems. The proposed algorithm is tested using a set of hypothetic data. The solution shows that the algorithm can solve the problems effectively.

  2. Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.

    ERIC Educational Resources Information Center

    Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

    2003-01-01

    Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

  3. Study of the evolution of nitrogen compounds during grape ripening. application to differentiate grape varieties and cultivated systems.

    PubMed

    Garde-Cerdán, Teresa; Lorenzo, Cándida; Lara, José Félix; Pardo, Francisco; Ancín-Azpilicueta, Carmen; Salinas, M Rosario

    2009-03-25

    The aim of this work was to study the evolution of amino acids and ammonium during grape ripening and to evaluate its application to differentiate grape varieties and cultivated systems (organic and nonorganic). For this purpose, Monastrell, Syrah, Merlot, and Petit Verdot grapes produced using conventional agriculture and Monastrell grape cultivated using organic agriculture, collected during two consecutive harvests at different stages of ripening, were studied. These years of harvest were very different climatic years; even so, the grape varieties presented similar qualitative compositions. Therefore, the percentage of amino acids at harvest moment allowed differentiation of grapes according to variety and cultivated system, regardless of the year. The nitrogen composition could allow estimation of the fermentative aroma potential of grapes. Thus, Syrah was the grape with the greatest aroma potential at harvest. Monastrell nonorganic grape had a concentration of nitrogen compounds superior to that of Monastrell organic grape. In Monastrell, Syrah, and Merlot, traditional varieties in the area, the highest concentration of nitrogen compounds coincided with the highest degrees Baume/total acidity ratio and color index during 2007. Consequently, technological and phenolic maturity of these grape varieties coincided with the maximum composition of nitrogen compounds. However, in 2008, this did not happen because grape ripening was irregular as a consequence of different climatological conditions.

  4. Turbopump Performance Improved by Evolutionary Algorithms

    NASA Technical Reports Server (NTRS)

    Oyama, Akira; Liou, Meng-Sing

    2002-01-01

    The development of design optimization technology for turbomachinery has been initiated using the multiobjective evolutionary algorithm under NASA's Intelligent Synthesis Environment and Revolutionary Aeropropulsion Concepts programs. As an alternative to the traditional gradient-based methods, evolutionary algorithms (EA's) are emergent design-optimization algorithms modeled after the mechanisms found in natural evolution. EA's search from multiple points, instead of moving from a single point. In addition, they require no derivatives or gradients of the objective function, leading to robustness and simplicity in coupling any evaluation codes. Parallel efficiency also becomes very high by using a simple master-slave concept for function evaluations, since such evaluations often consume the most CPU time, such as computational fluid dynamics. Application of EA's to multiobjective design problems is also straightforward because EA's maintain a population of design candidates in parallel. Because of these advantages, EA's are a unique and attractive approach to real-world design optimization problems.

  5. AMOBH: Adaptive Multiobjective Black Hole Algorithm.

    PubMed

    Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen

    2017-01-01

    This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

  6. Community detection in complex networks by using membrane algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

    Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

  7. A multi-level solution algorithm for steady-state Markov chains

    NASA Technical Reports Server (NTRS)

    Horton, Graham; Leutenegger, Scott T.

    1993-01-01

    A new iterative algorithm, the multi-level algorithm, for the numerical solution of steady state Markov chains is presented. The method utilizes a set of recursively coarsened representations of the original system to achieve accelerated convergence. It is motivated by multigrid methods, which are widely used for fast solution of partial differential equations. Initial results of numerical experiments are reported, showing significant reductions in computation time, often an order of magnitude or more, relative to the Gauss-Seidel and optimal SOR algorithms for a variety of test problems. The multi-level method is compared and contrasted with the iterative aggregation-disaggregation algorithm of Takahashi.

  8. Reliability Analysis of Differential Relay as Main Protection Transformer Using Fuzzy Logic Algorithm

    NASA Astrophysics Data System (ADS)

    Mulyadi, Y.; Sucita, T.; Sumarto; Alpani, M.

    2018-02-01

    Electricity supply demand is increasing every year. It makes PT. PLN (Persero) is required to provide optimal customer service and satisfaction. Optimal service depends on the performance of the equipment of the power system owned, especially the transformer. Power transformer is an electrical equipment that transforms electricity from high voltage to low voltage or vice versa. However, in the electrical power system, is inseparable from interference included in the transformer. But, the disturbance can be minimized by the protection system. The main protection transformer is differential relays. Differential relays working system using Kirchoff law where inflows equal outflows. If there are excessive currents that interfere then the relays will work. But, the relay can also experience decreased performance. Therefore, this final project aims to analyze the reliability of the differential relay on the transformer in three different substations. Referring to the standard applied by the transmission line protection officer, the differential relay shall have slope characteristics of 30% in the first slope and 80% in the second slope when using two slopes and 80% when using one slope with an instant time and the corresponding ratio. So, the results obtained on the Siemens differential release have a reliable slope characteristic with a value of 30 on the fuzzy logic system. In a while, ABB a differential relay is only 80% reliable because two experiments are not reliable. For the time, all the differential relays are instant with a value of 0.06 on the fuzzy logic system. For ratios, the differential relays ABB have a better value than others brand with a value of 151 on the fuzzy logic system.

  9. Mapping the Evolution of Scientific Fields

    PubMed Central

    Herrera, Mark; Roberts, David C.; Gulbahce, Natali

    2010-01-01

    Despite the apparent cross-disciplinary interactions among scientific fields, a formal description of their evolution is lacking. Here we describe a novel approach to study the dynamics and evolution of scientific fields using a network-based analysis. We build an idea network consisting of American Physical Society Physics and Astronomy Classification Scheme (PACS) numbers as nodes representing scientific concepts. Two PACS numbers are linked if there exist publications that reference them simultaneously. We locate scientific fields using a community finding algorithm, and describe the time evolution of these fields over the course of 1985–2006. The communities we identify map to known scientific fields, and their age depends on their size and activity. We expect our approach to quantifying the evolution of ideas to be relevant for making predictions about the future of science and thus help to guide its development. PMID:20463949

  10. Efficient spectral-Galerkin algorithms for direct solution for second-order differential equations using Jacobi polynomials

    NASA Astrophysics Data System (ADS)

    Doha, E.; Bhrawy, A.

    2006-06-01

    It is well known that spectral methods (tau, Galerkin, collocation) have a condition number of ( is the number of retained modes of polynomial approximations). This paper presents some efficient spectral algorithms, which have a condition number of , based on the Jacobi?Galerkin methods of second-order elliptic equations in one and two space variables. The key to the efficiency of these algorithms is to construct appropriate base functions, which lead to systems with specially structured matrices that can be efficiently inverted. The complexities of the algorithms are a small multiple of operations for a -dimensional domain with unknowns, while the convergence rates of the algorithms are exponentials with smooth solutions.

  11. Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy.

    PubMed

    Sengupta, Partho P; Huang, Yen-Min; Bansal, Manish; Ashrafi, Ali; Fisher, Matt; Shameer, Khader; Gall, Walt; Dudley, Joel T

    2016-06-01

    Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy. Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions. This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience. © 2016

  12. Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

    PubMed Central

    Svečko, Rajko

    2014-01-01

    This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

  13. Ultrafast adiabatic quantum algorithm for the NP-complete exact cover problem

    PubMed Central

    Wang, Hefeng; Wu, Lian-Ao

    2016-01-01

    An adiabatic quantum algorithm may lose quantumness such as quantum coherence entirely in its long runtime, and consequently the expected quantum speedup of the algorithm does not show up. Here we present a general ultrafast adiabatic quantum algorithm. We show that by applying a sequence of fast random or regular signals during evolution, the runtime can be reduced substantially, whereas advantages of the adiabatic algorithm remain intact. We also propose a randomized Trotter formula and show that the driving Hamiltonian and the proposed sequence of fast signals can be implemented simultaneously. We illustrate the algorithm by solving the NP-complete 3-bit exact cover problem (EC3), where NP stands for nondeterministic polynomial time, and put forward an approach to implementing the problem with trapped ions. PMID:26923834

  14. An accurate algorithm to calculate the Hurst exponent of self-similar processes

    NASA Astrophysics Data System (ADS)

    Fernández-Martínez, M.; Sánchez-Granero, M. A.; Trinidad Segovia, J. E.; Román-Sánchez, I. M.

    2014-06-01

    In this paper, we introduce a new approach which generalizes the GM2 algorithm (introduced in Sánchez-Granero et al. (2008) [52]) as well as fractal dimension algorithms (FD1, FD2 and FD3) (first appeared in Sánchez-Granero et al. (2012) [51]), providing an accurate algorithm to calculate the Hurst exponent of self-similar processes. We prove that this algorithm performs properly in the case of short time series when fractional Brownian motions and Lévy stable motions are considered. We conclude the paper with a dynamic study of the Hurst exponent evolution in the S&P500 index stocks.

  15. Origin and thermal evolution of Mars

    NASA Technical Reports Server (NTRS)

    Schubert, G.; Solomon, Sean C.; Turcotte, D. L.; Drake, M. J.; Sleep, N. H.

    1993-01-01

    The thermal evolution of Mars is governed by subsolidus mantle convection beneath a thick lithosphere. Models of the interior evolution are developed by parameterizing mantle convective heat transport in terms of mantle viscosity, the superadiabatic temperature rise across the mantle and mantle heat production. Geological, geophysical, and geochemical observations of the composition and structure of the interior and of the timing of major events in Martian evolution, such as global differentiation, atmospheric outgassing and the formation of the hemispherical dichotomy and Tharsis, are used to constrain the model computations. Isotope systematics of SNC meteorites suggest core formation essentially contemporaneously with the completion of accretion. Other aspects of this investigation are discussed.

  16. Concepts in solid tumor evolution.

    PubMed

    Sidow, Arend; Spies, Noah

    2015-04-01

    Evolutionary mechanisms in cancer progression give tumors their individuality. Cancer evolution is different from organismal evolution, however, and we discuss where concepts from evolutionary genetics are useful or limited in facilitating an understanding of cancer. Based on these concepts we construct and apply the simplest plausible model of tumor growth and progression. Simulations using this simple model illustrate the importance of stochastic events early in tumorigenesis, highlight the dominance of exponential growth over linear growth and differentiation, and explain the clonal substructure of tumors. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Optimizing Variational Quantum Algorithms Using Pontryagin’s Minimum Principle

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

    Yang, Zhi -Cheng; Rahmani, Armin; Shabani, Alireza

    We use Pontryagin’s minimum principle to optimize variational quantum algorithms. We show that for a fixed computation time, the optimal evolution has a bang-bang (square pulse) form, both for closed and open quantum systems with Markovian decoherence. Our findings support the choice of evolution ansatz in the recently proposed quantum approximate optimization algorithm. Focusing on the Sherrington-Kirkpatrick spin glass as an example, we find a system-size independent distribution of the duration of pulses, with characteristic time scale set by the inverse of the coupling constants in the Hamiltonian. The optimality of the bang-bang protocols and the characteristic time scale ofmore » the pulses provide an efficient parametrization of the protocol and inform the search for effective hybrid (classical and quantum) schemes for tackling combinatorial optimization problems. Moreover, we find that the success rates of our optimal bang-bang protocols remain high even in the presence of weak external noise and coupling to a thermal bath.« less

  18. Optimizing Variational Quantum Algorithms Using Pontryagin’s Minimum Principle

    DOE PAGES

    Yang, Zhi -Cheng; Rahmani, Armin; Shabani, Alireza; ...

    2017-05-18

    We use Pontryagin’s minimum principle to optimize variational quantum algorithms. We show that for a fixed computation time, the optimal evolution has a bang-bang (square pulse) form, both for closed and open quantum systems with Markovian decoherence. Our findings support the choice of evolution ansatz in the recently proposed quantum approximate optimization algorithm. Focusing on the Sherrington-Kirkpatrick spin glass as an example, we find a system-size independent distribution of the duration of pulses, with characteristic time scale set by the inverse of the coupling constants in the Hamiltonian. The optimality of the bang-bang protocols and the characteristic time scale ofmore » the pulses provide an efficient parametrization of the protocol and inform the search for effective hybrid (classical and quantum) schemes for tackling combinatorial optimization problems. Moreover, we find that the success rates of our optimal bang-bang protocols remain high even in the presence of weak external noise and coupling to a thermal bath.« less

  19. Adaptive array technique for differential-phase reflectometry in QUEST

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

    Idei, H., E-mail: idei@triam.kyushu-u.ac.jp; Hanada, K.; Zushi, H.

    2014-11-15

    A Phased Array Antenna (PAA) was considered as launching and receiving antennae in reflectometry to attain good directivity in its applied microwave range. A well-focused beam was obtained in a launching antenna application, and differential-phase evolution was properly measured by using a metal reflector plate in the proof-of-principle experiment at low power test facilities. Differential-phase evolution was also evaluated by using the PAA in the Q-shu University Experiment with Steady State Spherical Tokamak (QUEST). A beam-forming technique was applied in receiving phased-array antenna measurements. In the QUEST device that should be considered as a large oversized cavity, standing wave effectmore » was significantly observed with perturbed phase evolution. A new approach using derivative of measured field on propagating wavenumber was proposed to eliminate the standing wave effect.« less

  20. Multiple shooting algorithms for jump-discontinuous problems in optimal control and estimation

    NASA Technical Reports Server (NTRS)

    Mook, D. J.; Lew, Jiann-Shiun

    1991-01-01

    Multiple shooting algorithms are developed for jump-discontinuous two-point boundary value problems arising in optimal control and optimal estimation. Examples illustrating the origin of such problems are given to motivate the development of the solution algorithms. The algorithms convert the necessary conditions, consisting of differential equations and transversality conditions, into algebraic equations. The solution of the algebraic equations provides exact solutions for linear problems. The existence and uniqueness of the solution are proved.

  1. Optimization in optical systems revisited: Beyond genetic algorithms

    NASA Astrophysics Data System (ADS)

    Gagnon, Denis; Dumont, Joey; Dubé, Louis

    2013-05-01

    Designing integrated photonic devices such as waveguides, beam-splitters and beam-shapers often requires optimization of a cost function over a large solution space. Metaheuristics - algorithms based on empirical rules for exploring the solution space - are specifically tailored to those problems. One of the most widely used metaheuristics is the standard genetic algorithm (SGA), based on the evolution of a population of candidate solutions. However, the stochastic nature of the SGA sometimes prevents access to the optimal solution. Our goal is to show that a parallel tabu search (PTS) algorithm is more suited to optimization problems in general, and to photonics in particular. PTS is based on several search processes using a pool of diversified initial solutions. To assess the performance of both algorithms (SGA and PTS), we consider an integrated photonics design problem, the generation of arbitrary beam profiles using a two-dimensional waveguide-based dielectric structure. The authors acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC).

  2. Comparison of particle swarm optimization and differential evolution for aggregators' profit maximization in the demand response system

    NASA Astrophysics Data System (ADS)

    Wisittipanit, Nuttachat; Wisittipanich, Warisa

    2018-07-01

    Demand response (DR) refers to changes in the electricity use patterns of end-users in response to incentive payment designed to prompt lower electricity use during peak periods. Typically, there are three players in the DR system: an electric utility operator, a set of aggregators and a set of end-users. The DR model used in this study aims to minimize the operator's operational cost and offer rewards to aggregators, while profit-maximizing aggregators compete to sell DR services to the operator and provide compensation to end-users for altering their consumption profiles. This article presents the first application of two metaheuristics in the DR system: particle swarm optimization (PSO) and differential evolution (DE). The objective is to optimize the incentive payments during various periods to satisfy all stakeholders. The results show that DE significantly outperforms PSO, since it can attain better compensation rates, lower operational costs and higher aggregator profits.

  3. Mean field analysis of algorithms for scale-free networks in molecular biology

    PubMed Central

    2017-01-01

    The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k−γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks). PMID:29272285

  4. Mean field analysis of algorithms for scale-free networks in molecular biology.

    PubMed

    Konini, S; Janse van Rensburg, E J

    2017-01-01

    The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k-γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks).

  5. A Cognitive Machine Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis from Restrictive Cardiomyopathy

    PubMed Central

    Sengupta, Partho P.; Huang, Yen-Min; Bansal, Manish; Ashrafi, Ali; Fisher, Matt; Shameer, Khader; Gall, Walt; Dudley, Joel T

    2016-01-01

    Background Associating a patient’s profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography (STE) data sets derived from patients with known constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Methods and Results Clinical and echocardiographic data of 50 patients with CP and 44 with RCM were used for developing an associative memory classifier (AMC) based machine learning algorithm. The STE data was normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve (AUC) of the AMC was evaluated for differentiating CP from RCM. Using only STE variables, AMC achieved a diagnostic AUC of 89·2%, which improved to 96·2% with addition of 4 echocardiographic variables. In comparison, the AUC of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63·7%, respectively. Furthermore, AMC demonstrated greater accuracy and shorter learning curves than other machine learning approaches with accuracy asymptotically approaching 90% after a training fraction of 0·3 and remaining flat at higher training fractions. Conclusions This study demonstrates feasibility of a cognitive machine learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience. PMID:27266599

  6. A fast, parallel algorithm to solve the basic fluvial erosion/transport equations

    NASA Astrophysics Data System (ADS)

    Braun, J.

    2012-04-01

    Quantitative models of landform evolution are commonly based on the solution of a set of equations representing the processes of fluvial erosion, transport and deposition, which leads to predict the geometry of a river channel network and its evolution through time. The river network is often regarded as the backbone of any surface processes model (SPM) that might include other physical processes acting at a range of spatial and temporal scales along hill slopes. The basic laws of fluvial erosion requires the computation of local (slope) and non-local (drainage area) quantities at every point of a given landscape, a computationally expensive operation which limits the resolution of most SPMs. I present here an algorithm to compute the various components required in the parameterization of fluvial erosion (and transport) and thus solve the basic fluvial geomorphic equation, that is very efficient because it is O(n) (the number of required arithmetic operations is linearly proportional to the number of nodes defining the landscape), and is fully parallelizable (the computation cost decreases in a direct inverse proportion to the number of processors used to solve the problem). The algorithm is ideally suited for use on latest multi-core processors. Using this new technique, geomorphic problems can be solved at an unprecedented resolution (typically of the order of 10,000 X 10,000 nodes) while keeping the computational cost reasonable (order 1 sec per time step). Furthermore, I will show that the algorithm is applicable to any regular or irregular representation of the landform, and is such that the temporal evolution of the landform can be discretized by a fully implicit time-marching algorithm, making it unconditionally stable. I will demonstrate that such an efficient algorithm is ideally suited to produce a fully predictive SPM that links observationally based parameterizations of small-scale processes to the evolution of large-scale features of the landscapes on

  7. Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm.

    PubMed

    Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu

    2016-03-01

    The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.

  8. FITTING NONLINEAR ORDINARY DIFFERENTIAL EQUATION MODELS WITH RANDOM EFFECTS AND UNKNOWN INITIAL CONDITIONS USING THE STOCHASTIC APPROXIMATION EXPECTATION–MAXIMIZATION (SAEM) ALGORITHM

    PubMed Central

    Chow, Sy- Miin; Lu, Zhaohua; Zhu, Hongtu; Sherwood, Andrew

    2014-01-01

    The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation–maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed. PMID:25416456

  9. A reconstruction method for cone-beam differential x-ray phase-contrast computed tomography.

    PubMed

    Fu, Jian; Velroyen, Astrid; Tan, Renbo; Zhang, Junwei; Chen, Liyuan; Tapfer, Arne; Bech, Martin; Pfeiffer, Franz

    2012-09-10

    Most existing differential phase-contrast computed tomography (DPC-CT) approaches are based on three kinds of scanning geometries, described by parallel-beam, fan-beam and cone-beam. Due to the potential of compact imaging systems with magnified spatial resolution, cone-beam DPC-CT has attracted significant interest. In this paper, we report a reconstruction method based on a back-projection filtration (BPF) algorithm for cone-beam DPC-CT. Due to the differential nature of phase contrast projections, the algorithm restrains from differentiation of the projection data prior to back-projection, unlike BPF algorithms commonly used for absorption-based CT data. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a micro-focus x-ray tube source. Moreover, the numerical simulation and experimental results demonstrate that the proposed method can deal with several classes of truncated cone-beam datasets. We believe that this feature is of particular interest for future medical cone-beam phase-contrast CT imaging applications.

  10. Unifying time evolution and optimization with matrix product states

    NASA Astrophysics Data System (ADS)

    Haegeman, Jutho; Lubich, Christian; Oseledets, Ivan; Vandereycken, Bart; Verstraete, Frank

    2016-10-01

    We show that the time-dependent variational principle provides a unifying framework for time-evolution methods and optimization methods in the context of matrix product states. In particular, we introduce a new integration scheme for studying time evolution, which can cope with arbitrary Hamiltonians, including those with long-range interactions. Rather than a Suzuki-Trotter splitting of the Hamiltonian, which is the idea behind the adaptive time-dependent density matrix renormalization group method or time-evolving block decimation, our method is based on splitting the projector onto the matrix product state tangent space as it appears in the Dirac-Frenkel time-dependent variational principle. We discuss how the resulting algorithm resembles the density matrix renormalization group (DMRG) algorithm for finding ground states so closely that it can be implemented by changing just a few lines of code and it inherits the same stability and efficiency. In particular, our method is compatible with any Hamiltonian for which ground-state DMRG can be implemented efficiently. In fact, DMRG is obtained as a special case of our scheme for imaginary time evolution with infinite time step.

  11. A General Event Location Algorithm with Applications to Eclipse and Station Line-of-Sight

    NASA Technical Reports Server (NTRS)

    Parker, Joel J. K.; Hughes, Steven P.

    2011-01-01

    A general-purpose algorithm for the detection and location of orbital events is developed. The proposed algorithm reduces the problem to a global root-finding problem by mapping events of interest (such as eclipses, station access events, etc.) to continuous, differentiable event functions. A stepping algorithm and a bracketing algorithm are used to detect and locate the roots. Examples of event functions and the stepping/bracketing algorithms are discussed, along with results indicating performance and accuracy in comparison to commercial tools across a variety of trajectories.

  12. A General Event Location Algorithm with Applications to Eclispe and Station Line-of-Sight

    NASA Technical Reports Server (NTRS)

    Parker, Joel J. K.; Hughes, Steven P.

    2011-01-01

    A general-purpose algorithm for the detection and location of orbital events is developed. The proposed algorithm reduces the problem to a global root-finding problem by mapping events of interest (such as eclipses, station access events, etc.) to continuous, differentiable event functions. A stepping algorithm and a bracketing algorithm are used to detect and locate the roots. Examples of event functions and the stepping/bracketing algorithms are discussed, along with results indicating performance and accuracy in comparison to commercial tools across a variety of trajectories.

  13. Comparison of Different Post-Processing Algorithms for Dynamic Susceptibility Contrast Perfusion Imaging of Cerebral Gliomas.

    PubMed

    Kudo, Kohsuke; Uwano, Ikuko; Hirai, Toshinori; Murakami, Ryuji; Nakamura, Hideo; Fujima, Noriyuki; Yamashita, Fumio; Goodwin, Jonathan; Higuchi, Satomi; Sasaki, Makoto

    2017-04-10

    The purpose of the present study was to compare different software algorithms for processing DSC perfusion images of cerebral tumors with respect to i) the relative CBV (rCBV) calculated, ii) the cutoff value for discriminating low- and high-grade gliomas, and iii) the diagnostic performance for differentiating these tumors. Following approval of institutional review board, informed consent was obtained from all patients. Thirty-five patients with primary glioma (grade II, 9; grade III, 8; and grade IV, 18 patients) were included. DSC perfusion imaging was performed with 3-Tesla MRI scanner. CBV maps were generated by using 11 different algorithms of four commercially available software and one academic program. rCBV of each tumor compared to normal white matter was calculated by ROI measurements. Differences in rCBV value were compared between algorithms for each tumor grade. Receiver operator characteristics analysis was conducted for the evaluation of diagnostic performance of different algorithms for differentiating between different grades. Several algorithms showed significant differences in rCBV, especially for grade IV tumors. When differentiating between low- (II) and high-grade (III/IV) tumors, the area under the ROC curve (Az) was similar (range 0.85-0.87), and there were no significant differences in Az between any pair of algorithms. In contrast, the optimal cutoff values varied between algorithms (range 4.18-6.53). rCBV values of tumor and cutoff values for discriminating low- and high-grade gliomas differed between software packages, suggesting that optimal software-specific cutoff values should be used for diagnosis of high-grade gliomas.

  14. A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia.

    PubMed

    Floares, Alexandru George

    2008-01-01

    Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.

  15. GENERAL: Application of Symplectic Algebraic Dynamics Algorithm to Circular Restricted Three-Body Problem

    NASA Astrophysics Data System (ADS)

    Lu, Wei-Tao; Zhang, Hua; Wang, Shun-Jin

    2008-07-01

    Symplectic algebraic dynamics algorithm (SADA) for ordinary differential equations is applied to solve numerically the circular restricted three-body problem (CR3BP) in dynamical astronomy for both stable motion and chaotic motion. The result is compared with those of Runge-Kutta algorithm and symplectic algorithm under the fourth order, which shows that SADA has higher accuracy than the others in the long-term calculations of the CR3BP.

  16. Source imaging of potential fields through a matrix space-domain algorithm

    NASA Astrophysics Data System (ADS)

    Baniamerian, Jamaledin; Oskooi, Behrooz; Fedi, Maurizio

    2017-01-01

    Imaging of potential fields yields a fast 3D representation of the source distribution of potential fields. Imaging methods are all based on multiscale methods allowing the source parameters of potential fields to be estimated from a simultaneous analysis of the field at various scales or, in other words, at many altitudes. Accuracy in performing upward continuation and differentiation of the field has therefore a key role for this class of methods. We here describe an accurate method for performing upward continuation and vertical differentiation in the space-domain. We perform a direct discretization of the integral equations for upward continuation and Hilbert transform; from these equations we then define matrix operators performing the transformation, which are symmetric (upward continuation) or anti-symmetric (differentiation), respectively. Thanks to these properties, just the first row of the matrices needs to be computed, so to decrease dramatically the computation cost. Our approach allows a simple procedure, with the advantage of not involving large data extension or tapering, as due instead in case of Fourier domain computation. It also allows level-to-drape upward continuation and a stable differentiation at high frequencies; finally, upward continuation and differentiation kernels may be merged into a single kernel. The accuracy of our approach is shown to be important for multi-scale algorithms, such as the continuous wavelet transform or the DEXP (depth from extreme point method), because border errors, which tend to propagate largely at the largest scales, are radically reduced. The application of our algorithm to synthetic and real-case gravity and magnetic data sets confirms the accuracy of our space domain strategy over FFT algorithms and standard convolution procedures.

  17. Competition drives trait evolution and character displacement between Mimulus species along an environmental gradient.

    PubMed

    Kooyers, Nicholas J; James, Brooke; Blackman, Benjamin K

    2017-05-01

    Closely related species may evolve to coexist stably in sympatry through niche differentiation driven by in situ competition, a process termed character displacement. Alternatively, past evolution in allopatry may have already sufficiently reduced niche overlap to permit establishment in sympatry, a process called ecological sorting. The relative importance of each process to niche differentiation is contentious even though they are not mutually exclusive and are both mediated via multivariate trait evolution. We explore how competition has impacted niche differentiation in two monkeyflowers, Mimulus alsinoides and M. guttatus, which often co-occur. Through field observations, common gardens, and competition experiments, we demonstrate that M. alsinoides is restricted to marginal habitats in sympatry and that the impacts of character displacement on niche differentiation are complex. Competition with M. guttatus alters selection gradients and has favored taller M. alsinoides with earlier seasonal flowering at low elevation and floral shape divergence at high elevation. However, no trait exhibits the pattern typically associated with character displacement, higher divergence between species in sympatry than allopatry. Thus, although character displacement was unlikely the process driving initial divergence along niche axes necessary for coexistence, we conclude that competition in sympatry has likely driven trait evolution along additional niche axes. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

  18. Algorithm for Controlling a Centrifugal Compressor

    NASA Technical Reports Server (NTRS)

    Benedict, Scott M.

    2004-01-01

    An algorithm has been developed for controlling a centrifugal compressor that serves as the prime mover in a heatpump system. Experimental studies have shown that the operating conditions for maximum compressor efficiency are close to the boundary beyond which surge occurs. Compressor surge is a destructive condition in which there are instantaneous reversals of flow associated with a high outlet-to-inlet pressure differential. For a given cooling load, the algorithm sets the compressor speed at the lowest possible value while adjusting the inlet guide vane angle and diffuser vane angle to maximize efficiency, subject to an overriding requirement to prevent surge. The onset of surge is detected via the onset of oscillations of the electric current supplied to the compressor motor, associated with surge-induced oscillations of the torque exerted by and on the compressor rotor. The algorithm can be implemented in any of several computer languages.

  19. Explicit expressions for meromorphic solutions of autonomous nonlinear ordinary differential equations

    NASA Astrophysics Data System (ADS)

    Demina, Maria V.; Kudryashov, Nikolay A.

    2011-03-01

    Meromorphic solutions of autonomous nonlinear ordinary differential equations are studied. An algorithm for constructing meromorphic solutions in explicit form is presented. General expressions for meromorphic solutions (including rational, periodic, elliptic) are found for a wide class of autonomous nonlinear ordinary differential equations.

  20. The Brassica oleracea genome reveals the asymmetrical evolution of polyploid genomes

    PubMed Central

    Liu, Shengyi; Liu, Yumei; Yang, Xinhua; Tong, Chaobo; Edwards, David; Parkin, Isobel A. P.; Zhao, Meixia; Ma, Jianxin; Yu, Jingyin; Huang, Shunmou; Wang, Xiyin; Wang, Junyi; Lu, Kun; Fang, Zhiyuan; Bancroft, Ian; Yang, Tae-Jin; Hu, Qiong; Wang, Xinfa; Yue, Zhen; Li, Haojie; Yang, Linfeng; Wu, Jian; Zhou, Qing; Wang, Wanxin; King, Graham J; Pires, J. Chris; Lu, Changxin; Wu, Zhangyan; Sampath, Perumal; Wang, Zhuo; Guo, Hui; Pan, Shengkai; Yang, Limei; Min, Jiumeng; Zhang, Dong; Jin, Dianchuan; Li, Wanshun; Belcram, Harry; Tu, Jinxing; Guan, Mei; Qi, Cunkou; Du, Dezhi; Li, Jiana; Jiang, Liangcai; Batley, Jacqueline; Sharpe, Andrew G; Park, Beom-Seok; Ruperao, Pradeep; Cheng, Feng; Waminal, Nomar Espinosa; Huang, Yin; Dong, Caihua; Wang, Li; Li, Jingping; Hu, Zhiyong; Zhuang, Mu; Huang, Yi; Huang, Junyan; Shi, Jiaqin; Mei, Desheng; Liu, Jing; Lee, Tae-Ho; Wang, Jinpeng; Jin, Huizhe; Li, Zaiyun; Li, Xun; Zhang, Jiefu; Xiao, Lu; Zhou, Yongming; Liu, Zhongsong; Liu, Xuequn; Qin, Rui; Tang, Xu; Liu, Wenbin; Wang, Yupeng; Zhang, Yangyong; Lee, Jonghoon; Kim, Hyun Hee; Denoeud, France; Xu, Xun; Liang, Xinming; Hua, Wei; Wang, Xiaowu; Wang, Jun; Chalhoub, Boulos; Paterson, Andrew H

    2014-01-01

    Polyploidization has provided much genetic variation for plant adaptive evolution, but the mechanisms by which the molecular evolution of polyploid genomes establishes genetic architecture underlying species differentiation are unclear. Brassica is an ideal model to increase knowledge of polyploid evolution. Here we describe a draft genome sequence of Brassica oleracea, comparing it with that of its sister species B. rapa to reveal numerous chromosome rearrangements and asymmetrical gene loss in duplicated genomic blocks, asymmetrical amplification of transposable elements, differential gene co-retention for specific pathways and variation in gene expression, including alternative splicing, among a large number of paralogous and orthologous genes. Genes related to the production of anticancer phytochemicals and morphological variations illustrate consequences of genome duplication and gene divergence, imparting biochemical and morphological variation to B. oleracea. This study provides insights into Brassica genome evolution and will underpin research into the many important crops in this genus. PMID:24852848

  1. Differential pleiotropy and HOX functional organization.

    PubMed

    Sivanantharajah, Lovesha; Percival-Smith, Anthony

    2015-02-01

    Key studies led to the idea that transcription factors are composed of defined modular protein motifs or domains, each with separable, unique function. During evolution, the recombination of these modular domains could give rise to transcription factors with new properties, as has been shown using recombinant molecules. This archetypic, modular view of transcription factor organization is based on the analyses of a few transcription factors such as GAL4, which may represent extreme exemplars rather than an archetype or the norm. Recent work with a set of Homeotic selector (HOX) proteins has revealed differential pleiotropy: the observation that highly-conserved HOX protein motifs and domains make small, additive, tissue specific contributions to HOX activity. Many of these differentially pleiotropic HOX motifs may represent plastic sequence elements called short linear motifs (SLiMs). The coupling of differential pleiotropy with SLiMs, suggests that protein sequence changes in HOX transcription factors may have had a greater impact on morphological diversity during evolution than previously believed. Furthermore, differential pleiotropy may be the genetic consequence of an ensemble nature of HOX transcription factor allostery, where HOX proteins exist as an ensemble of states with the capacity to integrate an extensive array of developmental information. Given a new structural model for HOX functional domain organization, the properties of the archetypic TF may require reassessment. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. [Application of support vector machine-recursive feature elimination algorithm in Raman spectroscopy for differential diagnosis of benign and malignant breast diseases].

    PubMed

    Zhang, Haipeng; Fu, Tong; Zhang, Zhiru; Fan, Zhimin; Zheng, Chao; Han, Bing

    2014-08-01

    To explore the value of application of support vector machine-recursive feature elimination (SVM-RFE) method in Raman spectroscopy for differential diagnosis of benign and malignant breast diseases. Fresh breast tissue samples of 168 patients (all female; ages 22-75) were obtained by routine surgical resection from May 2011 to May 2012 at the Department of Breast Surgery, the First Hospital of Jilin University. Among them, there were 51 normal tissues, 66 benign and 51 malignant breast lesions. All the specimens were assessed by Raman spectroscopy, and the SVM-RFE algorithm was used to process the data and build the mathematical model. Mahalanobis distance and spectral residuals were used as discriminating criteria to evaluate this data-processing method. 1 800 Raman spectra were acquired from the fresh samples of human breast tissues. Based on spectral profiles, the presence of 1 078, 1 267, 1 301, 1 437, 1 653, and 1 743 cm(-1) peaks were identified in the normal tissues; and 1 281, 1 341, 1 381, 1 417, 1 465, 1 530, and 1 637 cm(-1) peaks were found in the benign and malignant tissues. The main characteristic peaks differentiating benign and malignant lesions were 1 340 and 1 480 cm(-1). The accuracy of SVM-RFE in discriminating normal and malignant lesions was 100.0%, while that in the assessment of benign lesions was 93.0%. There are distinct differences among the Raman spectra of normal, benign and malignant breast tissues, and SVM-RFE method can be used to build differentiation model of breast lesions.

  3. Program for solution of ordinary differential equations

    NASA Technical Reports Server (NTRS)

    Sloate, H.

    1973-01-01

    A program for the solution of linear and nonlinear first order ordinary differential equations is described and user instructions are included. The program contains a new integration algorithm for the solution of initial value problems which is particularly efficient for the solution of differential equations with a wide range of eigenvalues. The program in its present form handles up to ten state variables, but expansion to handle up to fifty state variables is being investigated.

  4. Monochromatic-beam-based dynamic X-ray microtomography based on OSEM-TV algorithm.

    PubMed

    Xu, Liang; Chen, Rongchang; Yang, Yiming; Deng, Biao; Du, Guohao; Xie, Honglan; Xiao, Tiqiao

    2017-01-01

    Monochromatic-beam-based dynamic X-ray computed microtomography (CT) was developed to observe evolution of microstructure inside samples. However, the low flux density results in low efficiency in data collection. To increase efficiency, reducing the number of projections should be a practical solution. However, it has disadvantages of low image reconstruction quality using the traditional filtered back projection (FBP) algorithm. In this study, an iterative reconstruction method using an ordered subset expectation maximization-total variation (OSEM-TV) algorithm was employed to address and solve this problem. The simulated results demonstrated that normalized mean square error of the image slices reconstructed by the OSEM-TV algorithm was about 1/4 of that by FBP. Experimental results also demonstrated that the density resolution of OSEM-TV was high enough to resolve different materials with the number of projections less than 100. As a result, with the introduction of OSEM-TV, the monochromatic-beam-based dynamic X-ray microtomography is potentially practicable for the quantitative and non-destructive analysis to the evolution of microstructure with acceptable efficiency in data collection and reconstructed image quality.

  5. Using meta-differential evolution to enhance a calculation of a continuous blood glucose level.

    PubMed

    Koutny, Tomas

    2016-09-01

    We developed a new model of glucose dynamics. The model calculates blood glucose level as a function of transcapillary glucose transport. In previous studies, we validated the model with animal experiments. We used analytical method to determine model parameters. In this study, we validate the model with subjects with type 1 diabetes. In addition, we combine the analytic method with meta-differential evolution. To validate the model with human patients, we obtained a data set of type 1 diabetes study that was coordinated by Jaeb Center for Health Research. We calculated a continuous blood glucose level from continuously measured interstitial fluid glucose level. We used 6 different scenarios to ensure robust validation of the calculation. Over 96% of calculated blood glucose levels fit A+B zones of the Clarke Error Grid. No data set required any correction of model parameters during the time course of measuring. We successfully verified the possibility of calculating a continuous blood glucose level of subjects with type 1 diabetes. This study signals a successful transition of our research from an animal experiment to a human patient. Researchers can test our model with their data on-line at https://diabetes.zcu.cz. Copyright © 2016 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.

  6. GOME Total Ozone and Calibration Error Derived Usign Version 8 TOMS Algorithm

    NASA Technical Reports Server (NTRS)

    Gleason, J.; Wellemeyer, C.; Qin, W.; Ahn, C.; Gopalan, A.; Bhartia, P.

    2003-01-01

    The Global Ozone Monitoring Experiment (GOME) is a hyper-spectral satellite instrument measuring the ultraviolet backscatter at relatively high spectral resolution. GOME radiances have been slit averaged to emulate measurements of the Total Ozone Mapping Spectrometer (TOMS) made at discrete wavelengths and processed using the new TOMS Version 8 Ozone Algorithm. Compared to Differential Optical Absorption Spectroscopy (DOAS) techniques based on local structure in the Huggins Bands, the TOMS uses differential absorption between a pair of wavelengths including the local stiucture as well as the background continuum. This makes the TOMS Algorithm more sensitive to ozone, but it also makes the algorithm more sensitive to instrument calibration errors. While calibration adjustments are not needed for the fitting techniques like the DOAS employed in GOME algorithms, some adjustment is necessary when applying the TOMS Algorithm to GOME. Using spectral discrimination at near ultraviolet wavelength channels unabsorbed by ozone, the GOME wavelength dependent calibration drift is estimated and then checked using pair justification. In addition, the day one calibration offset is estimated based on the residuals of the Version 8 TOMS Algorithm. The estimated drift in the 2b detector of GOME is small through the first four years and then increases rapidly to +5% in normalized radiance at 331 nm relative to 385 nm by mid 2000. The lb detector appears to be quite well behaved throughout this time period.

  7. Simplifying Differential Equations for Multiscale Feynman Integrals beyond Multiple Polylogarithms.

    PubMed

    Adams, Luise; Chaubey, Ekta; Weinzierl, Stefan

    2017-04-07

    In this Letter we exploit factorization properties of Picard-Fuchs operators to decouple differential equations for multiscale Feynman integrals. The algorithm reduces the differential equations to blocks of the size of the order of the irreducible factors of the Picard-Fuchs operator. As a side product, our method can be used to easily convert the differential equations for Feynman integrals which evaluate to multiple polylogarithms to an ϵ form.

  8. Molecular evolution and the latitudinal biodiversity gradient.

    PubMed

    Dowle, E J; Morgan-Richards, M; Trewick, S A

    2013-06-01

    Species density is higher in the tropics (low latitude) than in temperate regions (high latitude) resulting in a latitudinal biodiversity gradient (LBG). The LBG must be generated by differential rates of speciation and/or extinction and/or immigration among regions, but the role of each of these processes is still unclear. Recent studies examining differences in rates of molecular evolution have inferred a direct link between rate of molecular evolution and rate of speciation, and postulated these as important drivers of the LBG. Here we review the molecular genetic evidence and examine the factors that might be responsible for differences in rates of molecular evolution. Critical to this is the directionality of the relationship between speciation rates and rates of molecular evolution.

  9. The cultural evolution of emergent group-level traits.

    PubMed

    Smaldino, Paul E

    2014-06-01

    Many of the most important properties of human groups - including properties that may give one group an evolutionary advantage over another - are properly defined only at the level of group organization. Yet at present, most work on the evolution of culture has focused solely on the transmission of individual-level traits. I propose a conceptual extension of the theory of cultural evolution, particularly related to the evolutionary competition between cultural groups. The key concept in this extension is the emergent group-level trait. This type of trait is characterized by the structured organization of differentiated individuals and constitutes a unit of selection that is qualitatively different from selection on groups as defined by traditional multilevel selection (MLS) theory. As a corollary, I argue that the traditional focus on cooperation as the defining feature of human societies has missed an essential feature of cooperative groups. Traditional models of cooperation assume that interacting with one cooperator is equivalent to interacting with any other. However, human groups involve differential roles, meaning that receiving aid from one individual is often preferred to receiving aid from another. In this target article, I discuss the emergence and evolution of group-level traits and the implications for the theory of cultural evolution, including ramifications for the evolution of human cooperation, technology, and cultural institutions, and for the equivalency of multilevel selection and inclusive fitness approaches.

  10. Algorithm of pulmonary emphysema extraction using thoracic 3D CT images

    NASA Astrophysics Data System (ADS)

    Saita, Shinsuke; Kubo, Mitsuru; Kawata, Yoshiki; Niki, Noboru; Nakano, Yasutaka; Ohmatsu, Hironobu; Tominaga, Keigo; Eguchi, Kenji; Moriyama, Noriyuki

    2007-03-01

    Recently, due to aging and smoking, emphysema patients are increasing. The restoration of alveolus which was destroyed by emphysema is not possible, thus early detection of emphysema is desired. We describe a quantitative algorithm for extracting emphysematous lesions and quantitatively evaluate their distribution patterns using low dose thoracic 3-D CT images. The algorithm identified lung anatomies, and extracted low attenuation area (LAA) as emphysematous lesion candidates. Applying the algorithm to thoracic 3-D CT images and then by follow-up 3-D CT images, we demonstrate its potential effectiveness to assist radiologists and physicians to quantitatively evaluate the emphysematous lesions distribution and their evolution in time interval changes.

  11. A difference tracking algorithm based on discrete sine transform

    NASA Astrophysics Data System (ADS)

    Liu, HaoPeng; Yao, Yong; Lei, HeBing; Wu, HaoKun

    2018-04-01

    Target tracking is an important field of computer vision. The template matching tracking algorithm based on squared difference matching (SSD) and standard correlation coefficient (NCC) matching is very sensitive to the gray change of image. When the brightness or gray change, the tracking algorithm will be affected by high-frequency information. Tracking accuracy is reduced, resulting in loss of tracking target. In this paper, a differential tracking algorithm based on discrete sine transform is proposed to reduce the influence of image gray or brightness change. The algorithm that combines the discrete sine transform and the difference algorithm maps the target image into a image digital sequence. The Kalman filter predicts the target position. Using the Hamming distance determines the degree of similarity between the target and the template. The window closest to the template is determined the target to be tracked. The target to be tracked updates the template. Based on the above achieve target tracking. The algorithm is tested in this paper. Compared with SSD and NCC template matching algorithms, the algorithm tracks target stably when image gray or brightness change. And the tracking speed can meet the read-time requirement.

  12. Designing manufacturable filters for a 16-band plenoptic camera using differential evolution

    NASA Astrophysics Data System (ADS)

    Doster, Timothy; Olson, Colin C.; Fleet, Erin; Yetzbacher, Michael; Kanaev, Andrey; Lebow, Paul; Leathers, Robert

    2017-05-01

    A 16-band plenoptic camera allows for the rapid exchange of filter sets via a 4x4 filter array on the lens's front aperture. This ability to change out filters allows for an operator to quickly adapt to different locales or threat intelligence. Typically, such a system incorporates a default set of 16 equally spaced at-topped filters. Knowing the operating theater or the likely targets of interest it becomes advantageous to tune the filters. We propose using a modified beta distribution to parameterize the different possible filters and differential evolution (DE) to search over the space of possible filter designs. The modified beta distribution allows us to jointly optimize the width, taper and wavelength center of each single- or multi-pass filter in the set over a number of evolutionary steps. Further, by constraining the function parameters we can develop solutions which are not just theoretical but manufacturable. We examine two independent tasks: general spectral sensing and target detection. In the general spectral sensing task we utilize the theory of compressive sensing (CS) and find filters that generate codings which minimize the CS reconstruction error based on a fixed spectral dictionary of endmembers. For the target detection task and a set of known targets, we train the filters to optimize the separation of the background and target signature. We compare our results to the default 16 at-topped non-overlapping filter set which comes with the plenoptic camera and full hyperspectral resolution data which was previously acquired.

  13. Differentiating osteomyelitis from bone infarction in sickle cell disease.

    PubMed

    Wong, A L; Sakamoto, K M; Johnson, E E

    2001-02-01

    This brief review discusses one possible approach to evaluating the sickle cell patient with bone pain. The major differential diagnoses include osteomyelitis and bone infarction. Based on previous studies, we provide an approach to assessing and treating patients with the possible diagnosis of osteomyelitis. An algorithm has been provided, which emphasizes the importance of the initial history and physical examination. Specific radiographic studies are recommended to aid in making the initial assessment and to determine whether the patient has an infarct or osteomyelitis. Differentiating osteomyelitis from infarction in sickle cell patients remains a challenge for the pediatrician. This algorithm can be used as a guide for physicians who evaluate such patients in the acute care setting.

  14. A novel pipeline based FPGA implementation of a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Thirer, Nonel

    2014-05-01

    To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.

  15. Analysis of the Command and Control Segment (CCS) attitude estimation algorithm

    NASA Technical Reports Server (NTRS)

    Stockwell, Catherine

    1993-01-01

    This paper categorizes the qualitative behavior of the Command and Control Segment (CCS) differential correction algorithm as applied to attitude estimation using simultaneous spin axis sun angle and Earth cord length measurements. The categories of interest are the domains of convergence, divergence, and their boundaries. Three series of plots are discussed that show the dependence of the estimation algorithm on the vehicle radius, the sun/Earth angle, and the spacecraft attitude. Common qualitative dynamics to all three series are tabulated and discussed. Out-of-limits conditions for the estimation algorithm are identified and discussed.

  16. Hybrid Differential Dynamic Programming with Stochastic Search

    NASA Technical Reports Server (NTRS)

    Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob

    2016-01-01

    Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASAs Dawn mission. The Dawn trajectory was designed with the DDP-based Static Dynamic Optimal Control algorithm used in the Mystic software. Another recently developed method, Hybrid Differential Dynamic Programming (HDDP) is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

  17. Electric machine differential for vehicle traction control and stability control

    NASA Astrophysics Data System (ADS)

    Kuruppu, Sandun Shivantha

    Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.

  18. Thermal evolution of a differentiated Ganymede and implications for surface features

    NASA Technical Reports Server (NTRS)

    Kirk, R. L.; Stevenson, D. J.

    1987-01-01

    Thermodynamic models are developed for the processes which controlled the evolution of the surface Ganymede, an icy Jovian satellite assumed to have a rock-rich core surrounded by a water-ice mantle. Account is taken of a heat pulse which would have arisen from a Rayleigh-Taylor instability at a deep-seated liquid-solid water interface, rapid fracturing from global stresses imposed by warm ice diapiric upwelling, impacts by large meteorites, and resurfacing by ice flows (rather than core formation). Comparisons are made with existing models for the evolution of Callisto, and the difficulties in defining a mechanism which produced the groove terrain of Ganymede are discussed.

  19. Acoustooptic linear algebra processors - Architectures, algorithms, and applications

    NASA Technical Reports Server (NTRS)

    Casasent, D.

    1984-01-01

    Architectures, algorithms, and applications for systolic processors are described with attention to the realization of parallel algorithms on various optical systolic array processors. Systolic processors for matrices with special structure and matrices of general structure, and the realization of matrix-vector, matrix-matrix, and triple-matrix products and such architectures are described. Parallel algorithms for direct and indirect solutions to systems of linear algebraic equations and their implementation on optical systolic processors are detailed with attention to the pipelining and flow of data and operations. Parallel algorithms and their optical realization for LU and QR matrix decomposition are specifically detailed. These represent the fundamental operations necessary in the implementation of least squares, eigenvalue, and SVD solutions. Specific applications (e.g., the solution of partial differential equations, adaptive noise cancellation, and optimal control) are described to typify the use of matrix processors in modern advanced signal processing.

  20. Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility.

    PubMed

    Yang, Cheng-Hong; Lin, Yu-Da; Chuang, Li-Yeh; Chang, Hsueh-Wei

    2016-10-01

    Positively identifying disease-associated single nucleotide polymorphism (SNP) markers in genome-wide studies entails the complex association analysis of a huge number of SNPs. Such large numbers of SNP barcode (SNP/genotype combinations) continue to pose serious computational challenges, especially for high-dimensional data. We propose a novel exploiting SNP barcode method based on differential evolution, termed IDE (improved differential evolution). IDE uses a "top combination strategy" to improve the ability of differential evolution to explore high-order SNP barcodes in high-dimensional data. We simulate disease data and use real chronic dialysis data to test four global optimization algorithms. In 48 simulated disease models, we show that IDE outperforms existing global optimization algorithms in terms of exploring ability and power to detect the specific SNP/genotype combinations with a maximum difference between cases and controls. In real data, we show that IDE can be used to evaluate the relative effects of each individual SNP on disease susceptibility. IDE generated significant SNP barcode with less computational complexity than the other algorithms, making IDE ideally suited for analysis of high-order SNP barcodes. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Interactive evolution of camouflage.

    PubMed

    Reynolds, Craig

    2011-01-01

    This article presents an abstract computation model of the evolution of camouflage in nature. The 2D model uses evolved textures for prey, a background texture representing the environment, and a visual predator. A human observer, acting as the predator, is shown a cohort of 10 evolved textures overlaid on the background texture. The observer clicks on the five most conspicuous prey to remove ("eat") them. These lower-fitness textures are removed from the population and replaced with newly bred textures. Biological morphogenesis is represented in this model by procedural texture synthesis. Nested expressions of generators and operators form a texture description language. Natural evolution is represented by genetic programming (GP), a variant of the genetic algorithm. GP searches the space of texture description programs for those that appear least conspicuous to the predator.

  2. Complexity of the Quantum Adiabatic Algorithm

    NASA Astrophysics Data System (ADS)

    Hen, Itay

    2013-03-01

    The Quantum Adiabatic Algorithm (QAA) has been proposed as a mechanism for efficiently solving optimization problems on a quantum computer. Since adiabatic computation is analog in nature and does not require the design and use of quantum gates, it can be thought of as a simpler and perhaps more profound method for performing quantum computations that might also be easier to implement experimentally. While these features have generated substantial research in QAA, to date there is still a lack of solid evidence that the algorithm can outperform classical optimization algorihms. Here, we discuss several aspects of the quantum adiabatic algorithm: We analyze the efficiency of the algorithm on several ``hard'' (NP) computational problems. Studying the size dependence of the typical minimum energy gap of the Hamiltonians of these problems using quantum Monte Carlo methods, we find that while for most problems the minimum gap decreases exponentially with the size of the problem, indicating that the QAA is not more efficient than existing classical search algorithms, for other problems there is evidence to suggest that the gap may be polynomial near the phase transition. We also discuss applications of the QAA to ``real life'' problems and how they can be implemented on currently available (albeit prototypical) quantum hardware such as ``D-Wave One'', that impose serious restrictions as to which type of problems may be tested. Finally, we discuss different approaches to find improved implementations of the algorithm such as local adiabatic evolution, adaptive methods, local search in Hamiltonian space and others.

  3. Equation-free multiscale computation: algorithms and applications.

    PubMed

    Kevrekidis, Ioannis G; Samaey, Giovanni

    2009-01-01

    In traditional physicochemical modeling, one derives evolution equations at the (macroscopic, coarse) scale of interest; these are used to perform a variety of tasks (simulation, bifurcation analysis, optimization) using an arsenal of analytical and numerical techniques. For many complex systems, however, although one observes evolution at a macroscopic scale of interest, accurate models are only given at a more detailed (fine-scale, microscopic) level of description (e.g., lattice Boltzmann, kinetic Monte Carlo, molecular dynamics). Here, we review a framework for computer-aided multiscale analysis, which enables macroscopic computational tasks (over extended spatiotemporal scales) using only appropriately initialized microscopic simulation on short time and length scales. The methodology bypasses the derivation of macroscopic evolution equations when these equations conceptually exist but are not available in closed form-hence the term equation-free. We selectively discuss basic algorithms and underlying principles and illustrate the approach through representative applications. We also discuss potential difficulties and outline areas for future research.

  4. Constrained multi-objective optimization of storage ring lattices

    NASA Astrophysics Data System (ADS)

    Husain, Riyasat; Ghodke, A. D.

    2018-03-01

    The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

  5. Malagasy cichlids differentially limit impacts of body shape evolution on oral jaw functional morphology.

    PubMed

    Martinez, Christopher M; Sparks, John S

    2017-09-01

    Patterns of trait covariation, such as integration and modularity, are vital factors that influence the evolution of vertebrate body plans. In functional systems, decoupling of morphological modules buffers functional change in one trait by reducing correlated variation with another. However, for complex morphologies with many-to-one mapping of form to function (MTOM), resistance to functional change may also be achieved by constraining morphological variation within a functionally stable region of morphospace. For this research, we used geometric morphometrics to evaluate the evolution of body shape and its relationship with jaw functional morphology in two independent radiations of endemic Malagasy cichlid (Teleostei: Cichlidae). Our results suggested that the two subfamilies used different strategies to mitigate impacts of body shape variation on a metric of jaw function, maxillary kinematic transmission (MKT): (1) modularity between cranial and postcranial morphologies, and (2) integration of body and jaw evolution, with jaw morphologies varying in a manner that limits change in MKT. This research shows that, unlike modularity, MTOM allows traits to retain strong evolutionary covariation while still reducing impacts on functionality. These results suggest that MTOM, and its influence on the evolution of correlated traits, is likely much more widespread than is currently understood. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

  6. Modelling the evolution and diversity of cumulative culture

    PubMed Central

    Enquist, Magnus; Ghirlanda, Stefano; Eriksson, Kimmo

    2011-01-01

    Previous work on mathematical models of cultural evolution has mainly focused on the diffusion of simple cultural elements. However, a characteristic feature of human cultural evolution is the seemingly limitless appearance of new and increasingly complex cultural elements. Here, we develop a general modelling framework to study such cumulative processes, in which we assume that the appearance and disappearance of cultural elements are stochastic events that depend on the current state of culture. Five scenarios are explored: evolution of independent cultural elements, stepwise modification of elements, differentiation or combination of elements and systems of cultural elements. As one application of our framework, we study the evolution of cultural diversity (in time as well as between groups). PMID:21199845

  7. A Simplified Algorithm for Statistical Investigation of Damage Spreading

    NASA Astrophysics Data System (ADS)

    Gecow, Andrzej

    2009-04-01

    On the way to simulating adaptive evolution of complex system describing a living object or human developed project, a fitness should be defined on node states or network external outputs. Feedbacks lead to circular attractors of these states or outputs which make it difficult to define a fitness. The main statistical effects of adaptive condition are the result of small change tendency and to appear, they only need a statistically correct size of damage initiated by evolutionary change of system. This observation allows to cut loops of feedbacks and in effect to obtain a particular statistically correct state instead of a long circular attractor which in the quenched model is expected for chaotic network with feedback. Defining fitness on such states is simple. We calculate only damaged nodes and only once. Such an algorithm is optimal for investigation of damage spreading i.e. statistical connections of structural parameters of initial change with the size of effected damage. It is a reversed-annealed method—function and states (signals) may be randomly substituted but connections are important and are preserved. The small damages important for adaptive evolution are correctly depicted in comparison to Derrida annealed approximation which expects equilibrium levels for large networks. The algorithm indicates these levels correctly. The relevant program in Pascal, which executes the algorithm for a wide range of parameters, can be obtained from the author.

  8. SLA-aware differentiated QoS in elastic optical networks

    NASA Astrophysics Data System (ADS)

    Agrawal, Anuj; Vyas, Upama; Bhatia, Vimal; Prakash, Shashi

    2017-07-01

    The quality of service (QoS) offered by optical networks can be improved by accurate provisioning of service level specifications (SLSs) included in the service level agreement (SLA). A large number of users coexisting in the network require different services. Thus, a pragmatic network needs to offer a differentiated QoS to a variety of users according to the SLA contracted for different services at varying costs. In conventional wavelength division multiplexed (WDM) optical networks, service differentiation is feasible only for a limited number of users because of its fixed-grid structure. Newly introduced flex-grid based elastic optical networks (EONs) are more adaptive to traffic requirements as compared to the WDM networks because of the flexibility in their grid structure. Thus, we propose an efficient SLA provisioning algorithm with improved QoS for these flex-grid EONs empowered by optical orthogonal frequency division multiplexing (O-OFDM). The proposed algorithm, called SLA-aware differentiated QoS (SADQ), employs differentiation at the level of routing, spectrum allocation, and connection survivability. The proposed SADQ aims to accurately provision the SLA using such multilevel differentiation with an objective to improve the spectrum utilization from the network operator's perspective. SADQ is evaluated for three different CoSs under various traffic demand patterns and for different ratios of the number of requests belonging to the three considered CoSs. We propose two new SLA metrics for the improvement of functional QoS requirements, namely, security, confidentiality and survivability of high class of service (CoS) traffic. Since, to the best of our knowledge, the proposed SADQ is the first scheme in optical networks to employ exhaustive differentiation at the levels of routing, spectrum allocation, and survivability in a single algorithm, we first compare the performance of SADQ in EON and currently deployed WDM networks to assess the

  9. Comparative evaluation of the Bio-Rad Geenius HIV-1/2 Confirmatory Assay and the Bio-Rad Multispot HIV-1/2 Rapid Test as an alternative differentiation assay for CLSI M53 algorithm-I.

    PubMed

    Malloch, L; Kadivar, K; Putz, J; Levett, P N; Tang, J; Hatchette, T F; Kadkhoda, K; Ng, D; Ho, J; Kim, J

    2013-12-01

    The CLSI-M53-A, Criteria for Laboratory Testing and Diagnosis of Human Immunodeficiency Virus (HIV) Infection; Approved Guideline includes an algorithm in which samples that are reactive on a 4th generation EIA screen proceed to a supplemental assay that is able to confirm and differentiate between antibodies to HIV-1 and HIV-2. The recently CE-marked Bio-Rad Geenius HIV-1/2 Confirmatory Assay was evaluated as an alternative to the FDA-approved Bio-Rad Multispot HIV-1/HIV-2 Rapid Test which has been previously validated for use in this new algorithm. This study used reference samples submitted to the Canadian - NLHRS and samples from commercial sources. Data was tabulated in 2×2 tables for statistical analysis; sensitivity, specificity, predictive values, kappa and likelihood ratios. The overall performance of the Geenius and Multispot was very high; sensitivity (100%, 100%), specificity (96.3%, 99.1%), positive (45.3, 181) and negative (0, 0) likelihood ratios respectively, high kappa (0.96) and low bias index (0.0068). The ability to differentiate HIV-1 (99.2%, 100%) and HIV-2 (98.1%, 98.1%) Ab was also very high. The Bio-Rad Geenius HIV-1/2 Confirmatory Assay is a suitable alternative to the validated Multispot for use in the second stage of CLSI M53 algorithm-I. The Geenius has additional features including traceability and sample and cassette barcoding that improve the quality management/assurance of HIV testing. It is anticipated that the CLSI M53 guideline and assays such as the Geenius will reduce the number of indeterminate test results previously associated with the HIV-1 WB and improve the ability to differentiate HIV-2 infections. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

  10. The differential algebra based multiple level fast multipole algorithm for 3D space charge field calculation and photoemission simulation

    DOE PAGES

    None, None

    2015-09-28

    Coulomb interaction between charged particles inside a bunch is one of the most importance collective effects in beam dynamics, becoming even more significant as the energy of the particle beam is lowered to accommodate analytical and low-Z material imaging purposes such as in the time resolved Ultrafast Electron Microscope (UEM) development currently underway at Michigan State University. In addition, space charge effects are the key limiting factor in the development of ultrafast atomic resolution electron imaging and diffraction technologies and are also correlated with an irreversible growth in rms beam emittance due to fluctuating components of the nonlinear electron dynamics.more » In the short pulse regime used in the UEM, space charge effects also lead to virtual cathode formation in which the negative charge of the electrons emitted at earlier times, combined with the attractive surface field, hinders further emission of particles and causes a degradation of the pulse properties. Space charge and virtual cathode effects and their remediation are core issues for the development of the next generation of high-brightness UEMs. Since the analytical models are only applicable for special cases, numerical simulations, in addition to experiments, are usually necessary to accurately understand the space charge effect. In this paper we will introduce a grid-free differential algebra based multiple level fast multipole algorithm, which calculates the 3D space charge field for n charged particles in arbitrary distribution with an efficiency of O(n), and the implementation of the algorithm to a simulation code for space charge dominated photoemission processes.« less

  11. Minimally invasive myotomy for the treatment of esophageal achalasia: evolution of the surgical procedure and the therapeutic algorithm.

    PubMed

    Bresadola, Vittorio; Feo, Carlo V

    2012-04-01

    Achalasia is a rare disease of the esophagus, characterized by the absence of peristalsis in the esophageal body and incomplete relaxation of the lower esophageal sphincter, which may be hypertensive. The cause of this disease is unknown; therefore, the aim of the therapy is to improve esophageal emptying by eliminating the outflow resistance caused by the lower esophageal sphincter. This goal can be accomplished either by pneumatic dilatation or surgical myotomy, which are the only long-term effective therapies for achalasia. Historically, pneumatic dilatation was preferred over surgical myotomy because of the morbidity associated with a thoracotomy or a laparotomy. However, with the development of minimally invasive techniques, the surgical approach has gained widespread acceptance among patients and gastroenterologists and, consequently, the role of surgery has changed. The aim of this study was to review the changes occurred in the surgical treatment of achalasia over the last 2 decades; specifically, the development of minimally invasive techniques with the evolution from a thoracoscopic approach without an antireflux procedure to a laparoscopic myotomy with a partial fundoplication, the changes in the length of the myotomy, and the modification of the therapeutic algorithm.

  12. Implicit, nonswitching, vector-oriented algorithm for steady transonic flow

    NASA Technical Reports Server (NTRS)

    Lottati, I.

    1983-01-01

    A rapid computation of a sequence of transonic flow solutions has to be performed in many areas of aerodynamic technology. The employment of low-cost vector array processors makes the conduction of such calculations economically feasible. However, for a full utilization of the new hardware, the developed algorithms must take advantage of the special characteristics of the vector array processor. The present investigation has the objective to develop an efficient algorithm for solving transonic flow problems governed by mixed partial differential equations on an array processor.

  13. Vesta Evolution from Surface Mineralogy: Mafic and Ultramafic Mineral Distribution

    NASA Technical Reports Server (NTRS)

    DeSanctis, M. C.; Ammannito, E.; Palomba, E.; Longobardo, A.; Mittlefehldt, D. W.; McSween, H. Y; Marchi, S.; Capria, M. T.; Capaccioni, F.; Frigeri, A.; hide

    2014-01-01

    Vesta is the only intact, differentiated, rocky protoplanet and it is the parent body of HED meterorites. Howardite, eucrite and diogenite (HED) meteorites represent regolith, basaltic-crust, lower-crust and possibly ultramafic-mantle samples of asteroid Vesta. Only a few of these meteorites, the orthopyroxene-rich diogenites, contain olivine, a mineral that is a major component of the mantles of differentiated bodies, including Vesta. The HED parent body experienced complex igneous processes that are not yet fully understood and olivine and diogenite distribution is a key measurement to understand Vesta evolution. Here we report on the distribution of olivine and its constraints on vestan evolution models.

  14. Coronary artery segmentation in X-ray angiograms using gabor filters and differential evolution.

    PubMed

    Cervantes-Sanchez, Fernando; Cruz-Aceves, Ivan; Hernandez-Aguirre, Arturo; Solorio-Meza, Sergio; Cordova-Fraga, Teodoro; Aviña-Cervantes, Juan Gabriel

    2018-08-01

    Segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis, since it can help cardiologists in diagnosing and monitoring vascular abnormalities. Due to the main disadvantages of the X-ray angiograms are the nonuniform illumination, and the weak contrast between blood vessels and image background, different vessel enhancement methods have been introduced. In this paper, a novel method for blood vessel enhancement based on Gabor filters tuned using the optimization strategy of Differential evolution (DE) is proposed. Because the Gabor filters are governed by three different parameters, the optimal selection of those parameters is highly desirable in order to maximize the vessel detection rate while reducing the computational cost of the training stage. To obtain the optimal set of parameters for the Gabor filters, the area (Az) under the receiver operating characteristics curve is used as objective function. In the experimental results, the proposed method achieves an A z =0.9388 in a training set of 40 images, and for a test set of 40 images it obtains the highest performance with an A z =0.9538 compared with six state-of-the-art vessel detection methods. Finally, the proposed method achieves an accuracy of 0.9423 for vessel segmentation using the test set. In addition, the experimental results have also shown that the proposed method can be highly suitable for clinical decision support in terms of computational time and vessel segmentation performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Asymptotic (h tending to infinity) absolute stability for BDFs applied to stiff differential equations. [Backward Differentiation Formulas

    NASA Technical Reports Server (NTRS)

    Krogh, F. T.; Stewart, K.

    1984-01-01

    Methods based on backward differentiation formulas (BDFs) for solving stiff differential equations require iterating to approximate the solution of the corrector equation on each step. One hope for reducing the cost of this is to make do with iteration matrices that are known to have errors and to do no more iterations than are necessary to maintain the stability of the method. This paper, following work by Klopfenstein, examines the effect of errors in the iteration matrix on the stability of the method. Application of the results to an algorithm is discussed briefly.

  16. Hybrid Differential Dynamic Programming with Stochastic Search

    NASA Technical Reports Server (NTRS)

    Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob A.

    2016-01-01

    Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASA's Dawn mission. The Dawn trajectory was designed with the DDP-based Static/Dynamic Optimal Control algorithm used in the Mystic software.1 Another recently developed method, Hybrid Differential Dynamic Programming (HDDP),2, 3 is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

  17. 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.

  18. Gene duplication and the evolution of phenotypic diversity in insect societies.

    PubMed

    Chau, Linh M; Goodisman, Michael A D

    2017-12-01

    Gene duplication is an important evolutionary process thought to facilitate the evolution of phenotypic diversity. We investigated if gene duplication was associated with the evolution of phenotypic differences in a highly social insect, the honeybee Apis mellifera. We hypothesized that the genetic redundancy provided by gene duplication could promote the evolution of social and sexual phenotypes associated with advanced societies. We found a positive correlation between sociality and rate of gene duplications across the Apoidea, indicating that gene duplication may be associated with sociality. We also discovered that genes showing biased expression between A. mellifera alternative phenotypes tended to be found more frequently than expected among duplicated genes than singletons. Moreover, duplicated genes had higher levels of caste-, sex-, behavior-, and tissue-biased expression compared to singletons, as expected if gene duplication facilitated phenotypic differentiation. We also found that duplicated genes were maintained in the A. mellifera genome through the processes of conservation, neofunctionalization, and specialization, but not subfunctionalization. Overall, we conclude that gene duplication may have facilitated the evolution of social and sexual phenotypes, as well as tissue differentiation. Thus this study further supports the idea that gene duplication allows species to evolve an increased range of phenotypic diversity. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

  19. Network-based recommendation algorithms: A review

    NASA Astrophysics Data System (ADS)

    Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš

    2016-06-01

    Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.

  20. Convergence analysis of evolutionary algorithms that are based on the paradigm of information geometry.

    PubMed

    Beyer, Hans-Georg

    2014-01-01

    The convergence behaviors of so-called natural evolution strategies (NES) and of the information-geometric optimization (IGO) approach are considered. After a review of the NES/IGO ideas, which are based on information geometry, the implications of this philosophy w.r.t. optimization dynamics are investigated considering the optimization performance on the class of positive quadratic objective functions (the ellipsoid model). Exact differential equations describing the approach to the optimizer are derived and solved. It is rigorously shown that the original NES philosophy optimizing the expected value of the objective functions leads to very slow (i.e., sublinear) convergence toward the optimizer. This is the real reason why state of the art implementations of IGO algorithms optimize the expected value of transformed objective functions, for example, by utility functions based on ranking. It is shown that these utility functions are localized fitness functions that change during the IGO flow. The governing differential equations describing this flow are derived. In the case of convergence, the solutions to these equations exhibit an exponentially fast approach to the optimizer (i.e., linear convergence order). Furthermore, it is proven that the IGO philosophy leads to an adaptation of the covariance matrix that equals in the asymptotic limit-up to a scalar factor-the inverse of the Hessian of the objective function considered.

  1. A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

    PubMed Central

    Tsai, Chun-Wei; Tseng, Shih-Pang; Yang, Chu-Sing

    2014-01-01

    This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA. PMID:24892038

  2. A high-performance genetic algorithm: using traveling salesman problem as a case.

    PubMed

    Tsai, Chun-Wei; Tseng, Shih-Pang; Chiang, Ming-Chao; Yang, Chu-Sing; Hong, Tzung-Pei

    2014-01-01

    This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA.

  3. JavaGenes Molecular Evolution

    NASA Technical Reports Server (NTRS)

    Lohn, Jason; Smith, David; Frank, Jeremy; Globus, Al; Crawford, James

    2007-01-01

    JavaGenes is a general-purpose, evolutionary software system written in Java. It implements several versions of a genetic algorithm, simulated annealing, stochastic hill climbing, and other search techniques. This software has been used to evolve molecules, atomic force field parameters, digital circuits, Earth Observing Satellite schedules, and antennas. This version differs from version 0.7.28 in that it includes the molecule evolution code and other improvements. Except for the antenna code, JaveGenes is available for NASA Open Source distribution.

  4. Multiscale computations with a wavelet-adaptive algorithm

    NASA Astrophysics Data System (ADS)

    Rastigejev, Yevgenii Anatolyevich

    A wavelet-based adaptive multiresolution algorithm for the numerical solution of multiscale problems governed by partial differential equations is introduced. The main features of the method include fast algorithms for the calculation of wavelet coefficients and approximation of derivatives on nonuniform stencils. The connection between the wavelet order and the size of the stencil is established. The algorithm is based on the mathematically well established wavelet theory. This allows us to provide error estimates of the solution which are used in conjunction with an appropriate threshold criteria to adapt the collocation grid. The efficient data structures for grid representation as well as related computational algorithms to support grid rearrangement procedure are developed. The algorithm is applied to the simulation of phenomena described by Navier-Stokes equations. First, we undertake the study of the ignition and subsequent viscous detonation of a H2 : O2 : Ar mixture in a one-dimensional shock tube. Subsequently, we apply the algorithm to solve the two- and three-dimensional benchmark problem of incompressible flow in a lid-driven cavity at large Reynolds numbers. For these cases we show that solutions of comparable accuracy as the benchmarks are obtained with more than an order of magnitude reduction in degrees of freedom. The simulations show the striking ability of the algorithm to adapt to a solution having different scales at different spatial locations so as to produce accurate results at a relatively low computational cost.

  5. The value of electrocardiography for differential diagnosis in wide QRS complex tachycardia.

    PubMed

    Sousa, Pedro A; Pereira, Salomé; Candeias, Rui; de Jesus, Ilídio

    2014-03-01

    Correct diagnosis in wide QRS complex tachycardia remains a challenge. Differential diagnosis between ventricular and supraventricular tachycardia has important therapeutic and prognostic implications, and although data from clinical history and physical examination may suggest a particular origin, it is the 12-lead surface electrocardiogram that usually enables this differentiation. Since 1978, various electrocardiographic criteria have been proposed for the differential diagnosis of wide complex tachycardias, particularly the presence of atrioventricular dissociation, and the axis, duration and morphology of QRS complexes. Despite the wide variety of criteria, diagnosis is still often difficult, and errors can have serious consequences. To reduce such errors, several differential diagnosis algorithms have been proposed since 1991. However, in a small percentage of wide QRS tachycardias the diagnosis remains uncertain and in these the wisest decision is to treat them as ventricular tachycardias. The authors' objective was to review the main electrocardiographic criteria and differential diagnosis algorithms of wide QRS tachycardia. Copyright © 2012 Sociedade Portuguesa de Cardiologia. Published by Elsevier España. All rights reserved.

  6. Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms

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

    Arampatzis, Giorgos, E-mail: garab@math.uoc.gr; Katsoulakis, Markos A., E-mail: markos@math.umass.edu; Plechac, Petr, E-mail: plechac@math.udel.edu

    2012-10-01

    We present a mathematical framework for constructing and analyzing parallel algorithms for lattice kinetic Monte Carlo (KMC) simulations. The resulting algorithms have the capacity to simulate a wide range of spatio-temporal scales in spatially distributed, non-equilibrium physiochemical processes with complex chemistry and transport micro-mechanisms. Rather than focusing on constructing exactly the stochastic trajectories, our approach relies on approximating the evolution of observables, such as density, coverage, correlations and so on. More specifically, we develop a spatial domain decomposition of the Markov operator (generator) that describes the evolution of all observables according to the kinetic Monte Carlo algorithm. This domain decompositionmore » corresponds to a decomposition of the Markov generator into a hierarchy of operators and can be tailored to specific hierarchical parallel architectures such as multi-core processors or clusters of Graphical Processing Units (GPUs). Based on this operator decomposition, we formulate parallel Fractional step kinetic Monte Carlo algorithms by employing the Trotter Theorem and its randomized variants; these schemes, (a) are partially asynchronous on each fractional step time-window, and (b) are characterized by their communication schedule between processors. The proposed mathematical framework allows us to rigorously justify the numerical and statistical consistency of the proposed algorithms, showing the convergence of our approximating schemes to the original serial KMC. The approach also provides a systematic evaluation of different processor communicating schedules. We carry out a detailed benchmarking of the parallel KMC schemes using available exact solutions, for example, in Ising-type systems and we demonstrate the capabilities of the method to simulate complex spatially distributed reactions at very large scales on GPUs. Finally, we discuss work load balancing between processors and propose a re

  7. PWFQ: a priority-based weighted fair queueing algorithm for the downstream transmission of EPON

    NASA Astrophysics Data System (ADS)

    Xu, Sunjuan; Ye, Jiajun; Zou, Junni

    2005-11-01

    In the downstream direction of EPON, all ethernet frames share one downlink channel from the OLT to destination ONUs. To guarantee differentiated services, a scheduling algorithm is needed to solve the link-sharing issue. In this paper, we first review the classical WFQ algorithm and point out the shortcomings existing in the fair queueing principle of WFQ algorithm for EPON. Then we propose a novel scheduling algorithm called Priority-based WFQ (PWFQ) algorithm which distributes bandwidth based on priority. PWFQ algorithm can guarantee the quality of real-time services whether under light load or under heavy load. Simulation results also show that PWFQ algorithm not only can improve delay performance of real-time services, but can also meet the worst-case delay bound requirements.

  8. Phase Reconstruction from FROG Using Genetic Algorithms[Frequency-Resolved Optical Gating

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

    Omenetto, F.G.; Nicholson, J.W.; Funk, D.J.

    1999-04-12

    The authors describe a new technique for obtaining the phase and electric field from FROG measurements using genetic algorithms. Frequency-Resolved Optical Gating (FROG) has gained prominence as a technique for characterizing ultrashort pulses. FROG consists of a spectrally resolved autocorrelation of the pulse to be measured. Typically a combination of iterative algorithms is used, applying constraints from experimental data, and alternating between the time and frequency domain, in order to retrieve an optical pulse. The authors have developed a new approach to retrieving the intensity and phase from FROG data using a genetic algorithm (GA). A GA is a generalmore » parallel search technique that operates on a population of potential solutions simultaneously. Operators in a genetic algorithm, such as crossover, selection, and mutation are based on ideas taken from evolution.« less

  9. Vector network analyzer ferromagnetic resonance spectrometer with field differential detection

    NASA Astrophysics Data System (ADS)

    Tamaru, S.; Tsunegi, S.; Kubota, H.; Yuasa, S.

    2018-05-01

    This work presents a vector network analyzer ferromagnetic resonance (VNA-FMR) spectrometer with field differential detection. This technique differentiates the S-parameter by applying a small binary modulation field in addition to the DC bias field to the sample. By setting the modulation frequency sufficiently high, slow sensitivity fluctuations of the VNA, i.e., low-frequency components of the trace noise, which limit the signal-to-noise ratio of the conventional VNA-FMR spectrometer, can be effectively removed, resulting in a very clean FMR signal. This paper presents the details of the hardware implementation and measurement sequence as well as the data processing and analysis algorithms tailored for the FMR spectrum obtained with this technique. Because the VNA measures a complex S-parameter, it is possible to estimate the Gilbert damping parameter from the slope of the phase variation of the S-parameter with respect to the bias field. We show that this algorithm is more robust against noise than the conventional algorithm based on the linewidth.

  10. Differential carrier phase recovery for QPSK optical coherent systems with integrated tunable lasers.

    PubMed

    Fatadin, Irshaad; Ives, David; Savory, Seb J

    2013-04-22

    The performance of a differential carrier phase recovery algorithm is investigated for the quadrature phase shift keying (QPSK) modulation format with an integrated tunable laser. The phase noise of the widely-tunable laser measured using a digital coherent receiver is shown to exhibit significant drift compared to a standard distributed feedback (DFB) laser due to enhanced low frequency noise component. The simulated performance of the differential algorithm is compared to the Viterbi-Viterbi phase estimation at different baud rates using the measured phase noise for the integrated tunable laser.

  11. A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

    PubMed

    Liu, Chun-Han; Liu, Lian

    2017-05-08

    BACKGROUND Alzheimer's disease (AD) is the most common type of dementia. The objective of this paper is to propose a novel method to identify differential pathways in hippocampus AD. MATERIAL AND METHODS We proposed a combined method by merging existed methods. Firstly, pathways were identified by four known methods (DAVID, the neaGUI package, the pathway-based co-expressed method, and the pathway network approach), and differential pathways were evaluated through setting weight thresholds. Subsequently, we combined all pathways by a rank-based algorithm and called the method the combined method. Finally, common differential pathways across two or more of five methods were selected. RESULTS Pathways obtained from different methods were also different. The combined method obtained 1639 pathways and 596 differential pathways, which included all pathways gained from the four existing methods; hence, the novel method solved the problem of inconsistent results. Besides, a total of 13 common pathways were identified, such as metabolism, immune system, and cell cycle. CONCLUSIONS We have proposed a novel method by combining four existing methods based on a rank product algorithm, and identified 13 significant differential pathways based on it. These differential pathways might provide insight into treatment and diagnosis of hippocampus AD.

  12. A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

    NASA Astrophysics Data System (ADS)

    Liu, Likun

    2018-01-01

    In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

  13. Evolution of a terrestrial magma ocean: Thermodynamics, kinetics, rheology, convection, differentiation

    NASA Technical Reports Server (NTRS)

    Solomatov, V. S.; Stevenson, D. J.

    1992-01-01

    The evolution of an initially totally molten magma ocean is constrained on the basis of analysis of various physical problems in the magma ocean. First of all an equilibrium thermodynamics of the magma ocean is developed in the melting temperature range. The equilibrium thermodynamical parameters are found as functions only of temperature and pressure and are used in the subsequent models of kinetics and convection. Kinematic processes determine the crystal size and also determine a non-equilibrium thermodynamics of the system. Rheology controls all dynamical regimes of the magma ocean. The thermal convection models for different rheological laws are developed for both the laminar convection and for turbulent convection in the case of equilibrium thermodynamics of the multiphase system. The evolution is estimated on the basis of all the above analysis.

  14. Vision technology/algorithms for space robotics applications

    NASA Technical Reports Server (NTRS)

    Krishen, Kumar; Defigueiredo, Rui J. P.

    1987-01-01

    The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed.

  15. Any Two Learning Algorithms Are (Almost) Exactly Identical

    NASA Technical Reports Server (NTRS)

    Wolpert, David H.

    2000-01-01

    This paper shows that if one is provided with a loss function, it can be used in a natural way to specify a distance measure quantifying the similarity of any two supervised learning algorithms, even non-parametric algorithms. Intuitively, this measure gives the fraction of targets and training sets for which the expected performance of the two algorithms differs significantly. Bounds on the value of this distance are calculated for the case of binary outputs and 0-1 loss, indicating that any two learning algorithms are almost exactly identical for such scenarios. As an example, for any two algorithms A and B, even for small input spaces and training sets, for less than 2e(-50) of all targets will the difference between A's and B's generalization performance of exceed 1%. In particular, this is true if B is bagging applied to A, or boosting applied to A. These bounds can be viewed alternatively as telling us, for example, that the simple English phrase 'I expect that algorithm A will generalize from the training set with an accuracy of at least 75% on the rest of the target' conveys 20,000 bytes of information concerning the target. The paper ends by discussing some of the subtleties of extending the distance measure to give a full (non-parametric) differential geometry of the manifold of learning algorithms.

  16. Differentiation and Evolution of the W Chromosome in the Fish Species of Megaleporinus (Characiformes, Anostomidae).

    PubMed

    Caetano de Barros, Lucas; Piscor, Diovani; Parise-Maltempi, Patricia P; Feldberg, Eliana

    2018-06-08

    The W chromosome of Megaleporinus trifasciatus was isolated in order to analyze its behavior in the karyotype of this and other species of the family, including forms with differentiated and undifferentiated sex chromosomes. The chromosome was microdissected, and the WMt probe was prepared for the chromosome painting procedure. M. trifasciatus was also cross-hybridized (cross-FISH) using existing probes available for M. macrocephalus (WMm) and M. elongatus (WMe). Two Leporinus species and Semaprochilodus taeniurus, representing a clade close to the Anostomidae, were also cross-hybridized with the objective to better understand the evolution of the sex chromosomes. In the metaphase of female M. trifasciatus, the WMt probe highlighted the whole long arm of the W chromosome and a small, distal portion of the long arm of the Z chromosome. In males, the probe highlighted the distal portion of the long arm of the Z chromosomes. The hybridization of female M. trifasciatus with the WMe and WMm probes revealed a pattern similar to that encountered using the WMt probe. The WMt, WMm, and WMe probes revealed broad similarities among the species of the genus Megaleporinus, which has a ZZ/ZW system of sex chromosomes, with only minor alterations becoming apparent when analyzed separately. © 2018 S. Karger AG, Basel.

  17. Historical feature pattern extraction based network attack situation sensing algorithm.

    PubMed

    Zeng, Yong; Liu, Dacheng; Lei, Zhou

    2014-01-01

    The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

  18. Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

    PubMed Central

    Zeng, Yong; Liu, Dacheng; Lei, Zhou

    2014-01-01

    The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously. PMID:24892054

  19. Differential invariants in nonclassical models of hydrodynamics

    NASA Astrophysics Data System (ADS)

    Bublik, Vasily V.

    2017-10-01

    In this paper, differential invariants are used to construct solutions for equations of the dynamics of a viscous heat-conducting gas and the dynamics of a viscous incompressible fluid modified by nanopowder inoculators. To describe the dynamics of a viscous heat-conducting gas, we use the complete system of Navier—Stokes equations with allowance for heat fluxes. Mathematical description of the dynamics of liquid metals under high-energy external influences (laser radiation or plasma flow) includes, in addition to the Navier—Stokes system of an incompressible viscous fluid, also heat fluxes and processes of nonequilibrium crystallization of a deformable fluid. Differentially invariant solutions are a generalization of partially invariant solutions, and their active study for various models of continuous medium mechanics is just beginning. Differentially invariant solutions can also be considered as solutions with differential constraints; therefore, when developing them, the approaches and methods developed by the science schools of academicians N. N. Yanenko and A. F. Sidorov will be actively used. In the construction of partially invariant and differentially invariant solutions, there are overdetermined systems of differential equations that require a compatibility analysis. The algorithms for reducing such systems to involution in a finite number of steps are described by Cartan, Finikov, Kuranishi, and other authors. However, the difficultly foreseeable volume of intermediate calculations complicates their practical application. Therefore, the methods of computer algebra are actively used here, which largely helps in solving this difficult problem. It is proposed to use the constructed exact solutions as tests for formulas, algorithms and their software implementations when developing and creating numerical methods and computational program complexes. This combination of effective numerical methods, capable of solving a wide class of problems, with

  20. Progressive Recombination Suppression and Differentiation in Recently Evolved Neo-sex Chromosomes

    PubMed Central

    Natri, Heini M.; Shikano, Takahito; Merilä, Juha

    2013-01-01

    Recombination suppression leads to the structural and functional differentiation of sex chromosomes and is thus a crucial step in the process of sex chromosome evolution. Despite extensive theoretical work, the exact processes and mechanisms of recombination suppression and differentiation are not well understood. In threespine sticklebacks (Gasterosteus aculeatus), a different sex chromosome system has recently evolved by a fusion between the Y chromosome and an autosome in the Japan Sea lineage, which diverged from the ancestor of other lineages approximately 2 Ma. We investigated the evolutionary dynamics and differentiation processes of sex chromosomes based on comparative analyses of these divergent lineages using 63 microsatellite loci. Both chromosome-wide differentiation patterns and phylogenetic inferences with X and Y alleles indicated that the ancestral sex chromosomes were extensively differentiated before the divergence of these lineages. In contrast, genetic differentiation appeared to have proceeded only in a small region of the neo-sex chromosomes. The recombination maps constructed for the Japan Sea lineage indicated that recombination has been suppressed or reduced over a large region spanning the ancestral and neo-sex chromosomes. Chromosomal regions exhibiting genetic differentiation and suppressed or reduced recombination were detected continuously and sequentially in the neo-sex chromosomes, suggesting that differentiation has gradually spread from the fusion point following the extension of recombination suppression. Our study illustrates an ongoing process of sex chromosome differentiation, providing empirical support for the theoretical model postulating that recombination suppression and differentiation proceed in a gradual manner in the very early stage of sex chromosome evolution. PMID:23436913