Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design r...Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.展开更多
As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simul...As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly systems.By far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly systems.This paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate buffers.Two objectives are considered simultaneously：minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly system.The integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are presented.Since the above problems are non-deterministic polynomial-hard（NP-hard）,a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals＇ fitness.The feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing algorithm.The sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.展开更多
There are numerous riveting points on the large-sized aircraft panel, irregular row of riveting points on delta wing. It is essential to plan the riveting sequence reasonably to improve the efficiency and accuracy of ...There are numerous riveting points on the large-sized aircraft panel, irregular row of riveting points on delta wing. It is essential to plan the riveting sequence reasonably to improve the efficiency and accuracy of automatic drilling and riveting. Therefore, this article presents a new multi-objective optimization method based on ant colony optimization （ACO）. Multi-objective optimization model of automatic drilling and riveting sequence planning is built by expressing the efficiency and accuracy of riveting as functions of the points＇ coordinates. In order to search the sequences efficiently and improve the quality of the sequences, a new local pheromone updating rule is applied when the ants search sequences. Pareto dominance is incorporated into the proposed ACO to find out the non-dominated sequences. This method is tested on a hyperbolicity panel model of ARJ21 and the result shows its feasibility and superiority compared with particle swarm optimization （PSO） and genetic algorithm （GA）.展开更多
In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture （MoCap） has been widely used in the field of large amount of motion capture data...In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture （MoCap） has been widely used in the field of large amount of motion capture databases are available and this is significant for the reuse of motion data. But due to the high degree of freedoms and high capture frequency, the dimension of the mo- tion capture data is usually very high and this will lead to a low efficiency in data processing. So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can＇t ignore. In this paper, first we lay out some problems about the key techniques in motion capture data processing. Then the existing approaches are analyzed and sum- marized. At last, some future work is proposed.展开更多
To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The se...To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.展开更多
The four-circuit parallel line on the same tower effectively solves the problems faced by the line reconstruction and construction under the condition of the increasing shortage of transmission corridors.Optimizing th...The four-circuit parallel line on the same tower effectively solves the problems faced by the line reconstruction and construction under the condition of the increasing shortage of transmission corridors.Optimizing the conductor and phase sequence arrangement of multiple transmission lines is conducive to improving electromagnetic and electrostatic coupling caused by electromagnetic problems.This paper uses the ATP-EMTP simulation software to build a 500 kV multi-circuit transmission line on the same tower.It stimulates the induced voltage and current values of different line lengths,tower spacing,vertical and horizontal spacing between different circuits,phase sequence arrangement,and nominal tower height.Moreover,use the BP neural network optimized by a genetic algorithm to predict the induced voltage and current under the unknown conductor and phase sequence arrangement.Finally,based on multi-objective particle swarm algorithm to construct the optimization model of conductor arrangement scheme of overhead transmission line,combined with electromagnetic environment control index,determine the optimal conductor arrangement scheme by the size of particle fitness function,a significant reduction in induced voltages and currents between transmission lines and the four-circuit conductor layout scheme meeting the requirements of the electromagnetic environment is obtained,which provides a reference for the tower design of the transmission station project.展开更多
The dependence of the ratio R1 for transfer ionization to single capture for Cq+, Nq+, Oq+, Neq+ ions on Ne target upon the electronic structure of the projectile is studied. For Aq+-Ne collisions the ratio R1 decreas...The dependence of the ratio R1 for transfer ionization to single capture for Cq+, Nq+, Oq+, Neq+ ions on Ne target upon the electronic structure of the projectile is studied. For Aq+-Ne collisions the ratio R1 decreases as the atomic number Z of the projectile increases for q=4,5,6,7 sequences which provides strong evidence for the increase of the binding energy of the target valence electron after single electron capture. The increase in binding energy depends both upon the atomic number of the projectile and the target atom.展开更多
This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation...This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.展开更多
Here we present an adaptation of NimbleGen 2.1M-probe array sequence capture for whole exome sequencing using the Illumina Genome Analyzer (GA) platform.The protocol involves two-stage library construction.The specifi...Here we present an adaptation of NimbleGen 2.1M-probe array sequence capture for whole exome sequencing using the Illumina Genome Analyzer (GA) platform.The protocol involves two-stage library construction.The specificity of exome enrichment was approximately 80% with 95.6% even coverage of the 34 Mb target region at an average sequencing depth of 33-fold.Comparison of our results with whole genome shot-gun resequencing results showed that the exome SNP calls gave only 0.97% false positive and 6.27% false negative variants.Our protocol is also well suited for use with whole genome amplified DNA.The results presented here indicate that there is a promising future for large-scale population genomics and medical studies using a whole exome sequencing approach.展开更多
针对红嘴蓝鹊优化算法(Red-billed Blue Magpie Optimization Algorithm,RBMO)存在多样性迅速退化、寻优精度差、易陷入局部最优的问题,提出了一种基于混合策略的自适应红嘴蓝鹊优化算法(Adaptive Red-Billed Blue Magpie Optimization ...针对红嘴蓝鹊优化算法(Red-billed Blue Magpie Optimization Algorithm,RBMO)存在多样性迅速退化、寻优精度差、易陷入局部最优的问题,提出了一种基于混合策略的自适应红嘴蓝鹊优化算法(Adaptive Red-Billed Blue Magpie Optimization Algorithm Based on Mixed Strategy,JRBMO)。首先,引入Hammersley序列初始化种群,使初始解分布更均匀,为寻优提供基础;其次,在勘探阶段,提出自适应螺旋围捕策略,通过动态控制个体的勘探范围与方向,提高RBMO的搜索能力。在开发阶段,引入莱维飞行策略,对当前最优解进行局部扰动,增强算法局部开发能力;最后,提出自适应维度变异策略,根据种群适应度分布的变化,对个体进行维度变异,避免算法陷入局部最优。在CEC2017与CEC2019测试集上对算法性能进行评估,结果显示JRBMO均值胜率分别达到88.9%和70%,验证了JRBMO的有效性。此外,将JRBMO应用于拉(压)弹簧设计问题和三维无线传感器网络(WSN)节点覆盖问题上,JRBMO均取得了最优的结果,其中WSN节点均值覆盖率高出原算法6.3%,体现了JRBMO在实际应用中的普适性。展开更多
目的比较TruSeq^(®)Exome与NimbleGen SeqCap EZ Human Exome两种外显子捕获试剂在脑瘫患儿中的捕获性能差异,为临床遗传学研究和诊断提供技术选择依据。方法纳入48例散发脑瘫患儿外周血样本,分别采用TruSeq(DNA探针)和NimbleGen(...目的比较TruSeq^(®)Exome与NimbleGen SeqCap EZ Human Exome两种外显子捕获试剂在脑瘫患儿中的捕获性能差异,为临床遗传学研究和诊断提供技术选择依据。方法纳入48例散发脑瘫患儿外周血样本,分别采用TruSeq(DNA探针)和NimbleGen(RNA探针)构建外显子组文库,经Illumina HiSeq 2000平台测序。通过生物信息学分析评估比对率、目标区域覆盖度、变异检出一致性等指标,并基于脑瘫相关基因集(2293个基因)分析捕获性能的临床相关性,采用配对t检验进行统计学分析(显著性阈值α=0.05)。结果NimbleGen和TruSeq两种外显子组捕获试剂盒在基础数据质量(比对率、插入片段长度)和GC含量上差异无统计学意义。然而,在关键性能指标上呈现互补特征,NimbleGen在特定深度覆盖上表现更优(1×覆盖率,P=1.84×10^(-5);20×覆盖率,P=1.49×10^(-20));而TruSeq则展现出更高的Indel检测灵敏度(TruSeq vs.NimbleGen:11371±1689 vs.11274±1670,P=3.24×10^(-7))和罕见变异捕获能力(TruSeq vs.NimbleGen:3164±766 vs.3072±774,P=1.20×10^(-4)),并成功检出所有11个阳性致病变异(包括NimbleGen漏检的2例)。结论TruSeq凭借更优的变异检出率更适合临床诊断场景,而NimbleGen的覆盖稳定性可能有利于研究性项目。展开更多
基金financially sponsored by National Natural Science Foundation of China(No.50975121)Changchun Science and Technology Plan Projects(No.10KZ03)the Plan for Scientific and Technology Development of Jilin Province(No.20150520106JH)
文摘Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.
基金supported by National Natural Science Foundation of China (Grant No.50875101)National Hi-tech Research and Development Program of China (863 Program,Grant No.2007AA04Z186)
文摘As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly systems.By far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly systems.This paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate buffers.Two objectives are considered simultaneously：minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly system.The integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are presented.Since the above problems are non-deterministic polynomial-hard（NP-hard）,a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals＇ fitness.The feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing algorithm.The sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.
基金National Natural Science Foundation of China (50805119)Aeronautical Science Foundation of China (2009ZE53)
文摘There are numerous riveting points on the large-sized aircraft panel, irregular row of riveting points on delta wing. It is essential to plan the riveting sequence reasonably to improve the efficiency and accuracy of automatic drilling and riveting. Therefore, this article presents a new multi-objective optimization method based on ant colony optimization （ACO）. Multi-objective optimization model of automatic drilling and riveting sequence planning is built by expressing the efficiency and accuracy of riveting as functions of the points＇ coordinates. In order to search the sequences efficiently and improve the quality of the sequences, a new local pheromone updating rule is applied when the ants search sequences. Pareto dominance is incorporated into the proposed ACO to find out the non-dominated sequences. This method is tested on a hyperbolicity panel model of ARJ21 and the result shows its feasibility and superiority compared with particle swarm optimization （PSO） and genetic algorithm （GA）.
基金Supported by the National Natural Science Foundation of China(No.60875046)by Program for Changjiang Scholars and Innovative Research Team in University(No.IRT1109)+5 种基金the Key Project of Chinese Ministry of Education(No.209029)the Program for Liaoning Excellent Talents in University(No.LR201003)the Program for Liaoning Science and Technology Research in University(No.LS2010008,2009S008,2009S009,LS2010179)the Program for Liaoning Innovative Research Team in University(Nos.2009T005,LT2010005,LT2011018)Natural Science Foundation of Liaoning Province(201102008)by"Liaoning BaiQianWan Talents Program(2010921010,2011921009)"
文摘In order to high reality and efficiency, the technique computer animation. With the development of motion capture, a of motion capture （MoCap） has been widely used in the field of large amount of motion capture databases are available and this is significant for the reuse of motion data. But due to the high degree of freedoms and high capture frequency, the dimension of the mo- tion capture data is usually very high and this will lead to a low efficiency in data processing. So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can＇t ignore. In this paper, first we lay out some problems about the key techniques in motion capture data processing. Then the existing approaches are analyzed and sum- marized. At last, some future work is proposed.
文摘To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.
文摘The four-circuit parallel line on the same tower effectively solves the problems faced by the line reconstruction and construction under the condition of the increasing shortage of transmission corridors.Optimizing the conductor and phase sequence arrangement of multiple transmission lines is conducive to improving electromagnetic and electrostatic coupling caused by electromagnetic problems.This paper uses the ATP-EMTP simulation software to build a 500 kV multi-circuit transmission line on the same tower.It stimulates the induced voltage and current values of different line lengths,tower spacing,vertical and horizontal spacing between different circuits,phase sequence arrangement,and nominal tower height.Moreover,use the BP neural network optimized by a genetic algorithm to predict the induced voltage and current under the unknown conductor and phase sequence arrangement.Finally,based on multi-objective particle swarm algorithm to construct the optimization model of conductor arrangement scheme of overhead transmission line,combined with electromagnetic environment control index,determine the optimal conductor arrangement scheme by the size of particle fitness function,a significant reduction in induced voltages and currents between transmission lines and the four-circuit conductor layout scheme meeting the requirements of the electromagnetic environment is obtained,which provides a reference for the tower design of the transmission station project.
文摘The dependence of the ratio R1 for transfer ionization to single capture for Cq+, Nq+, Oq+, Neq+ ions on Ne target upon the electronic structure of the projectile is studied. For Aq+-Ne collisions the ratio R1 decreases as the atomic number Z of the projectile increases for q=4,5,6,7 sequences which provides strong evidence for the increase of the binding energy of the target valence electron after single electron capture. The increase in binding energy depends both upon the atomic number of the projectile and the target atom.
文摘This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.
基金supported by the Chinese Academy of Sciences (Grant Nos.GJHZ0701-6 and KSCX-YWN-023)the National Natural Science Foundation of China (Grant Nos.30725008,90403130,90608010,30221004,90612019 and 30392130)the National Basic Research Program of China (Grant Nos.2007CB815701,2007CB815703 and 2007CB815705)
文摘Here we present an adaptation of NimbleGen 2.1M-probe array sequence capture for whole exome sequencing using the Illumina Genome Analyzer (GA) platform.The protocol involves two-stage library construction.The specificity of exome enrichment was approximately 80% with 95.6% even coverage of the 34 Mb target region at an average sequencing depth of 33-fold.Comparison of our results with whole genome shot-gun resequencing results showed that the exome SNP calls gave only 0.97% false positive and 6.27% false negative variants.Our protocol is also well suited for use with whole genome amplified DNA.The results presented here indicate that there is a promising future for large-scale population genomics and medical studies using a whole exome sequencing approach.
文摘针对红嘴蓝鹊优化算法(Red-billed Blue Magpie Optimization Algorithm,RBMO)存在多样性迅速退化、寻优精度差、易陷入局部最优的问题,提出了一种基于混合策略的自适应红嘴蓝鹊优化算法(Adaptive Red-Billed Blue Magpie Optimization Algorithm Based on Mixed Strategy,JRBMO)。首先,引入Hammersley序列初始化种群,使初始解分布更均匀,为寻优提供基础;其次,在勘探阶段,提出自适应螺旋围捕策略,通过动态控制个体的勘探范围与方向,提高RBMO的搜索能力。在开发阶段,引入莱维飞行策略,对当前最优解进行局部扰动,增强算法局部开发能力;最后,提出自适应维度变异策略,根据种群适应度分布的变化,对个体进行维度变异,避免算法陷入局部最优。在CEC2017与CEC2019测试集上对算法性能进行评估,结果显示JRBMO均值胜率分别达到88.9%和70%,验证了JRBMO的有效性。此外,将JRBMO应用于拉(压)弹簧设计问题和三维无线传感器网络(WSN)节点覆盖问题上,JRBMO均取得了最优的结果,其中WSN节点均值覆盖率高出原算法6.3%,体现了JRBMO在实际应用中的普适性。
