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Function fitting for modeling seasonal normalized difference vegetation index time series and early forecasting of soybean yield 被引量:2
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作者 Alexey Stepanov Konstantin Dubrovin Aleksei Sorokin 《The Crop Journal》 SCIE CSCD 2022年第5期1452-1459,共8页
Forecasting crop yields based on remote sensing data is one of the most important tasks in agriculture.Soybean is the main crop in the Russian Far East.It is desirable to forecast soybean yield as early as possible wh... Forecasting crop yields based on remote sensing data is one of the most important tasks in agriculture.Soybean is the main crop in the Russian Far East.It is desirable to forecast soybean yield as early as possible while maintaining high accuracy.This study aimed to investigate seasonal time series of the normalized difference vegetation index(NDVI) to achieve early forecasting of soybean yield.This research used data from the Moderate Resolution Image Spectroradiometer(MODIS),an arable-land mask obtained from the VEGA-Science web service,and soybean yield data for 2008-2017 for the Jewish Autonomous Region(JAR) districts.Four approximating functions were fitted to model the NDVI time series:Gaussian,double logistic(DL),and quadratic and cubic polynomials.In the period from calendar weeks 22-42(end of May to mid-October),averaged over two districts,the model using the DL function showed the highest accuracy(mean absolute percentage error-4.0%,root mean square error(RMSE)-0.029,P <0.01).The yield forecast accuracy of prediction in the period of weeks 25-30 in JAR municipalities using the parameters of the Gaussian function was higher(P <0.05) than that using the other functions.The mean forecast error for the Gaussian function was 14.9% in week 25(RMSE was0.21 t ha) and 5.1%-12.9% in weeks 26-30(RMSE varied from 0.06 to 0.15 t ha) according to the2013-2017 data.In weeks 31-32,the error was 5.0%-5.4%(RMSE was 0.07 t ha) using the Gaussian parameters and 7.4%-7.7%(RMSE was 0.09-0.11 t ha) for the DL function.When the method was applied to municipal districts of other soy-producing regions of the Russian Far East.RMSE was0.14-0.32 t hain weeks 25-26 and did not exceed 0.20 t hain subsequent weeks. 展开更多
关键词 NDVI function fitting Early prediction YIELD SOYBEAN
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Performance on the Functional Movement Screen in older active adults 被引量:3
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作者 Ulrike H.Mitchell A.Wayne Johnson +2 位作者 Pat R.Vehrs J.Brent Feland Sterling C.Hilton 《Journal of Sport and Health Science》 SCIE 2016年第1期119-125,共7页
Background:The Functional Movement Screen(FMS^(TM)) has become increasingly popular for identifying functional limitations in basic functional movements.This exploratory and descriptive study was undertaken to confirm... Background:The Functional Movement Screen(FMS^(TM)) has become increasingly popular for identifying functional limitations in basic functional movements.This exploratory and descriptive study was undertaken to confirm feasibility of performing the FMS^(TM) in older active adults,assess prevalence of asymmetries and to evaluate the relationship between functional movement ability,age,physical activity levels and body mass index(BMI).Methods:This is an observational study;97 men(n = 53) and women(n = 44) between the ages of 52 and 83 participated.BMI was computed and self-reported physical activity levels were obtained.Subjects were grouped by age(5-year intervals),BMI(normal,over-weight,and obese)and sex.Each participant's performance on the FMS^(TM) was digitally recorded for later analysis.Results:The youngest age group(50–54 years) scored highest in all seven tests and the oldest age group(75+) scored lowest in most of the tests compared to all other age groups.The subjects in the 'normal weight' group performed no different than those who were in the 'overweight' group;both groups performed better than the 'obese' group.Of the 97 participants 54 had at least one asymmetry.The pairwise correlations between the total FMS^(TM) score and age(r =-0.531),BMI(r =-0.270),and the measure of activity level(r = 0.287) were significant(p < 0.01 for all).Conclusion:FMS^(TM) scores decline with increased BMI,increased age,and decreased activity level.The screen identifies range of motion-and strength-related asymmetries.The FMS^(TM) can be used to assess functional limitations and asymmetries.Future research should evaluate if a higher total FMS^(TM) score is related to fewer falls or injuries in the older population. 展开更多
关键词 Age BMI Fitness level FMS^(TM) functional fitness functional limitations
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The Application of Multiquadric Function Fitting to Borehole Strain Time Series Data Processing 被引量:1
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作者 Peng Zhao Zhang Lei +1 位作者 Chen Zhiyao Lv Pingji 《Earthquake Research in China》 CSCD 2017年第2期239-246,共8页
Based on the existing continuous borehole strain observation,the multiquadric function fitting method was used to deal with time series data. The impact of difference kernel function parameters was discussed to obtain... Based on the existing continuous borehole strain observation,the multiquadric function fitting method was used to deal with time series data. The impact of difference kernel function parameters was discussed to obtain a valuable fitting result,from which the physical connotation of the original data and its possible applications were analyzed.Meanwhile,a brief comparison was made between the results of multiquadric function fitting and polynomial fitting. 展开更多
关键词 Multiquadric function fitting Kernel function Borehole strain time series
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Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks 被引量:2
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作者 P.Muthukrishnan P.Muthu Kannan 《Computers, Materials & Continua》 SCIE EI 2023年第1期1107-1122,共16页
Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mo... Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mobility patterns,adequate topologymodifications,and wireless communication.Despite the benefits of VANET,scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques.It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes.The main drawback of VANET network is the network unsteadiness that results in minimum lifetime.In order to avoid reduced network lifetime in VANET,this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization(EMCMHR-LM)in VANET.The presented EMCMHR-LM model involves the procedure of arranging clusters,cluster head(CH)selection,and route selection appropriate for VANETs.The presentedEMCMHR-LMmodel uses slime mold optimization based clustering(SMO-C)technique to group the vehicles into clusters.Besides,an enhanced wild horse optimization based multihop routing(EWHO-MHR)protocol by the optimization of network parameters.The presented EMCMHR-LMmodel is simulated usingNetwork Simulator(NS3)tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models. 展开更多
关键词 SCALABILITY VANET CLUSTERING multihop routing metaheuristics route selection fitness function
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Efficient Routing Protocol with Localization Based Priority&Congestion Control for UWSN
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作者 S.Sandhiyaa C.Gomathy 《Computers, Materials & Continua》 SCIE EI 2023年第3期4747-4768,共22页
The nodes in the sensor network have a wide range of uses,particularly on under-sea links that are skilled for detecting,handling as well as management.The underwater wireless sensor networks support collecting pollut... The nodes in the sensor network have a wide range of uses,particularly on under-sea links that are skilled for detecting,handling as well as management.The underwater wireless sensor networks support collecting pollution data,mine survey,oceanographic information collection,aided navigation,strategic surveillance,and collection of ocean samples using detectors that are submerged inwater.Localization,congestion routing,and prioritizing the traffic is the major issue in an underwater sensor network.Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource.Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper.We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio,dispatching of packets,power,and distance among the nodes.This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic.The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet,delay of latency,and power utilization than the existing algorithms. 展开更多
关键词 Congestion aware routing angle-based forwarding scheme fitness function hard stage soft stage scheme
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Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems
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作者 J.Jean Justus U.Sakthi +4 位作者 K.Priyadarshini B.Thiyaneswaran Masoud Alajmi Marwa Obayya Manar Ahmed Hamza 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期205-219,共15页
The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,... The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so on.The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling.At the same time,the task scheduling process is yet to be explored in the multi-core systems.This paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation.The GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks proficiently.The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset.The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy consumption.The overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS. 展开更多
关键词 Task scheduling energy efficiency multi-core systems fitness function MAKESPAN
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Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks
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作者 VDinesh SSrinivasan +1 位作者 Gyanendra Prasad Joshi Woong Cho 《Computer Systems Science & Engineering》 SCIE EI 2023年第7期687-699,共13页
In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time durations.At the same time,vehicles exhibit high velocity,leading to more vehicle disconnections... In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time durations.At the same time,vehicles exhibit high velocity,leading to more vehicle disconnections.Both of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability.Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs.But one such difficulty was reducing the cluster number under increasing transmitting nodes.This article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for VANET.The major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping vehicles.In addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given space.The DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean distance.The experimental assessment of the DHOGO-EAC technique was carried out under distinct aspects.The comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches. 展开更多
关键词 Vehicular networks CLUSTERING evolutionary algorithm fitness function distance metric
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Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol
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作者 Manar Ahmed Hamza Haya Mesfer Alshahrani +5 位作者 Sami Dhahbi Mohamed K Nour Mesfer Al Duhayyim ElSayed M.Tag El Din Ishfaq Yaseen Abdelwahed Motwakel 《Computer Systems Science & Engineering》 SCIE EI 2023年第5期1759-1773,共15页
Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications.In spite of this,it is chall... Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications.In spite of this,it is challenging to design energy-efficient WSN.The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network.In order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network development.In this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for WSN.The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN.To accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution quality.Besides,the inclusion of DE in traditional AOA helps in overcoming local optima problems.In addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance.In order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches. 展开更多
关键词 Wireless sensor network ROUTING multihop communication arithmetic optimization algorithm fitness function
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Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks
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作者 Sami Saeed Binyamin Mahmoud Ragab 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期105-119,共15页
Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a prom... Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches. 展开更多
关键词 Cognitive radio wireless sensor networks CLUSTERING dwarf mongoose optimization algorithm fitness function
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Cooperative extended rough attribute reduction algorithm based on improved PSO 被引量:10
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作者 Weiping Ding Jiandong Wang Zhijin Guan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第1期160-166,共7页
Particle swarm optimization (PSO) is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been ... Particle swarm optimization (PSO) is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduc- tion of decision tables is a non-derterministic polynomial (NP)-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness func- tions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine (UCI) data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction. 展开更多
关键词 rough set extended attribute reduction particle swarm optimization (PSO) cooperative evolutionary strategy fitness function.
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Variations of Snow Temperature and their Influence on Snow Cover Physical Parameters in the Western Tianshan Mountains,China 被引量:3
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作者 CHEN Xia WEI WenShou +1 位作者 LIU MingZhe GU GuangQin 《Journal of Mountain Science》 SCIE CSCD 2011年第6期827-837,共11页
This article discussed about snow temperature variations and their impact on snow cover parameters. Automatic temperature recorders were used to sample at lo-minute intervals at the Tianshan Station for Snow-cover and... This article discussed about snow temperature variations and their impact on snow cover parameters. Automatic temperature recorders were used to sample at lo-minute intervals at the Tianshan Station for Snow-cover and Avalanche Research, Chinese snow temperature Academy of Sciences. lo-layer and the snow cover parameters were measured by the snow property analyzer (Snow Fork) in its Stable period, Interim period and Snow melting period. Results indicate that the amplitude of the diurnal fluctuation in the temperature during Snow melting period is 1.62 times greater than that during Stable period. Time up to the peak temperature at the snow surface lags behind the peak solar radiation by more than 2.5 hours, and lags behind the peak atmospheric temperature by more than 0.2 hours during all three periods. The optimal fitted function of snow temperature profile becomes more complicated from Stable period to Snow melting period. 22 h temperature profiles in Stable period are the optimal fitted by cubic polynomial equation. In Interim period and Snow melting period, temperature profiles are optimal fitted by exponential equation between sunset and sunrise, and by Fourier function when solar radiation is strong. The vertical gradient in the snow temperature reaches its maximum value at the snow surface for three periods. The peak of this maximum value occurs during Stableperiod, and is 4.46 times greater than during Interim period. The absolute value of temperature gradient is lower than 0.1℃ cm-1 for 30 cm beneath snow surface. Snow temperature and temperature gradient in Stable period-Interim period indirectly cause increase (decrease) of snow density mainly by increasing (decreasing) permittivity. While it dramatically increases its water content to change its permittivity and snow density in Snow melting period. 展开更多
关键词 Snow temperature Vertical temperaturegradient Optimal fitting function Snow density Western Tianshan Mountains China
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Neural network and genetic algorithm based global path planning in a static environment 被引量:2
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作者 杜歆 陈华华 顾伟康 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2005年第6期549-554,共6页
Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network m... Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective. 展开更多
关键词 Mobile robot Neural network Genetic algorithm Global path planning Fitness function
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Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems 被引量:1
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作者 罗娜 钱锋 +1 位作者 赵亮 钟伟民 《Journal of Central South University》 SCIE EI CAS 2012年第2期443-452,共10页
In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in paral... In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm. 展开更多
关键词 estimation of distribution algorithm fitness function modeling Gaussian process surrogate approach
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Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm 被引量:1
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作者 崔平远 Yang Guojun 《High Technology Letters》 EI CAS 2001年第1期63-66,共4页
The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update th... The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update the weights of neural networks. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the proposed method improves considerably the precision of the inverse kinematics solutions for robot manipulators and guarantees a rapid global convergence and overcomes the drawbacks of SGA and the BP algorithm. 展开更多
关键词 Inverse kinematics Neural networks Fuzzy control Genetic algorithm Fitness function
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A self-adaptive linear evolutionary algorithm for solving constrained optimization problems 被引量:1
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作者 Kezong TANG Jingyu YANG +1 位作者 Shang GAO Tingkai SUN 《控制理论与应用(英文版)》 EI 2010年第4期533-539,共7页
In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce ... In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual's relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify 'good' individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions. 展开更多
关键词 Multiobjective optimization Evolutionary algorithms Pareto optimal solution Linear fitness function
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Composite multiobjective optimization beamforming based on genetic algorithms 被引量:1
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作者 史兢 Meng Weixiao Zhang Naitong Wang Zheng 《High Technology Letters》 EI CAS 2006年第3期283-287,共5页
All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms (GAs). Firstly, this pap... All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms (GAs). Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe. 展开更多
关键词 genetic algorithms composite beamforming fitness function
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A Highly Effective DPA Attack Method Based on Genetic Algorithm
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作者 Shuaiwei Zhang Xiaoyuan Yang +1 位作者 Weidong Zhong Yujuan Sun 《Computers, Materials & Continua》 SCIE EI 2018年第8期325-338,共14页
As one of the typical method for side channel attack,DPA has become a serious trouble for the security of encryption algorithm implementation.The potential capability of DPA attack induces researchers making a lot of ... As one of the typical method for side channel attack,DPA has become a serious trouble for the security of encryption algorithm implementation.The potential capability of DPA attack induces researchers making a lot of efforts in this area,which significantly improved the attack efficiency of DPA.However,most of these efforts were made based on the hypothesis that the gathered power consumption data from the target device were stable and low noise.If large deviation happens in part of the power consumption data sample,the efficiency of DPA attack will be reduced rapidly.In this work,a highly efficient method for DPA attack is proposed with the inspiration of genetic algorithm.Based on the designed fitness function,power consumption data that is stable and less noisy will be selected and the noisy ones will be eliminated.In this way,not only improves the robustness and efficiency of DPA attack,but also reduces the number of samples needed.With experiments on block cipher algorithms of DES and SM4,10%and 12.5%of the number of power consumption curves have been reduced in average with the proposed DPAG algorithm compared to original DPA attack respectively.The high efficiency and correctness of the proposed algorithm and novel model are proved by experiments. 展开更多
关键词 DPA EFFICIENCY noise genetic algorithm fitness function novel model
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Generalized Self-Adaptive Genetic Algorithms
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作者 Bin Wu Xuyan Tu +1 位作者 Jian Wu Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China Department of Information and Control Engineering, Southwest Institute of Technology, Mianyang 621002, China 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2000年第1期72-75,共4页
In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed init... In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved. 展开更多
关键词 generalized self-adaptive genetic algorithm initial population IMMIGRATION fitness function
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Analysis on velocity distribution and displacement along the profile of a slope using both empirical and analytical methods
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作者 CHEN Tie-lin ZHOU Cheng +2 位作者 LIU En-long DAI Feng LIU Jiao 《Journal of Mountain Science》 SCIE CSCD 2017年第12期2589-2602,共14页
Assessing the slope deformation is significant for landslide prediction. Many researchers have studied the slope displacement based on field data from the inclinometer in combination with complicated numerical analysi... Assessing the slope deformation is significant for landslide prediction. Many researchers have studied the slope displacement based on field data from the inclinometer in combination with complicated numerical analysis. They found that there was a shear zone above the slip surface, and they usually focused on the distribution of velocity and displacement within the shear zone. In this paper,two simple methods are proposed to analyze the distribution of displacement and velocity along the whole profile of a slope from the slip surface to the slope surface during slow movement. In the empirical method, the slope soil above the shear zone is assumed as a rigid body. Dual or triple piecewise fitting functions are empirically proposed for the distribution of velocity along the profile of a slope. In the analytical method, the slope soil is not assumed as a rigid body but as a deformable material. Continuous functions of the velocity and displacement along the profile of a slope are directly obtained by solving the Newton's equation of motion associated with the Bingham model. Using the two proposed methods respectively, the displacement and velocity along the slope profiles of three slopes are determined. A reasonable agreement between the measured data and the calculated results of the two proposed methods has been reached. In comparison with the empirical method, the analytical method would be more beneficial for slope deformation analysis in slope engineering, because the parameters are material constants in the analytical solution independent of time t, and the nonlinear viscosity of the soil can be considered. 展开更多
关键词 Slope deformation Progressive deformation Fitting function Analytical solution Bingham model Nonlinear viscosity
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Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment
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作者 Nawaf Alhebaishi Abdulrhman M.Alshareef +4 位作者 Tawfiq Hasanin Raed Alsini Gyanendra Prasad Joshi Seongsoo Cho Doo Ill Chul 《Computers, Materials & Continua》 SCIE EI 2022年第9期5233-5250,共18页
In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computi... In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices.Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge.One of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved performance.With this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing environment.The SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource allocation.The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy consumption.The problem is formulated based on the availability of VMs,task characteristics,and queue dynamics.The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center.For experimental validation,a comprehensive set of simulations were performed using the CloudSim tool.The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures. 展开更多
关键词 Resource scheduling edge computing soft computing fitness function virtual machines
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