Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassem...Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.展开更多
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decom...A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.展开更多
Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting hea...Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.展开更多
First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorit...First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorithm for neural networks based on genetic algorithm and BP algorithm is developed. The difference between the new training algorithm and BP algorithm in the ability of nonlinear approaching is expressed through an example, and the application foreground is illustrated by an example.展开更多
A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings.The difference from GA is that...A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings.The difference from GA is that AGA takes objective function as adaptability function directly,so it cuts down some unnecessary time expense because of float-point calculation of function conversion.The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented.It can be applied to a wide class of problems.The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA.The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.展开更多
The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balan...The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balancing its model weight and multi-parametric distributions to the required accuracy. A novel measuring instrument of space manipulator is designed to orbital simulative motion and locational accuracy test. The camera system of space manipulator, calibrated by MOGA algorithm, is used to locational accuracy test in this measuring instrument. The experimental result shows that the absolute errors are [0.07, 1.75] mm for MOGA calibrating model, [2.88, 5.95] mm for MN method, and [1.19, 4.83] mm for LM method. Besides, the composite errors both of LM method and MN method are approximately seven times higher that of MOGA calibrating model. It is suggested that the MOGA calibrating model is superior both to LM method and MN method.展开更多
针对目前黄连(Coptis chinensis)人工收获劳动强度大、效率低的问题,该研究在分析黄连种植农艺特性的基础上,提出了一种交替式黄连挖掘装置。首先,在对挖掘装置关键部件曲柄摇杆机构运动学建模的基础上,利用遗传算法,将黄连根土复合体...针对目前黄连(Coptis chinensis)人工收获劳动强度大、效率低的问题,该研究在分析黄连种植农艺特性的基础上,提出了一种交替式黄连挖掘装置。首先,在对挖掘装置关键部件曲柄摇杆机构运动学建模的基础上,利用遗传算法,将黄连根土复合体被抛出时的最大速度作为目标函数,以杆长、最小传动角、挖掘深度、最小切土厚度、入土角、切土轨迹、抛出位置为约束条件对曲柄摇杆机构各杆杆长进行了优化求解。进一步,采用图像重建法建立了黄连根茎的三维模型,运用离散元法(discrete element method,DEM)构建了挖掘装置-黄连根茎-土壤的离散元复合模型,并联合多体动力学软件RecurDyn进行了挖掘过程的耦合仿真分析,黄连根土复合体的运动过程及状态变化表明,挖掘装置挖掘性能符合设计要求。最后,制作试验样机进行挖掘性能试验,得到了挖掘装置在前进速度为0.5m/s、曲柄转速为300r/min时挖掘性能最优,其抛送合格率、挖松率、损伤率分别为93.15%、99.28%、2.95%。试验结果表明,该挖掘装置的挖掘性能满足黄连根茎的挖掘要求,可为黄连机械化收获装备的开发提供参考。展开更多
基金supported by the National High Technology Research and Development Program of China(2006AA04Z427).
文摘Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.
文摘A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.
基金Project(50875265) supported by the National Natural Science Foundation of ChinaProject(20080440992) supported by the Postdoctoral Science Foundation of ChinaProject(2009SK3159) supported by the Technology Support Plan of Hunan Province,China
文摘Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.
基金This project was supported by Guangdong Natural Science Foundation.
文摘First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorithm for neural networks based on genetic algorithm and BP algorithm is developed. The difference between the new training algorithm and BP algorithm in the ability of nonlinear approaching is expressed through an example, and the application foreground is illustrated by an example.
文摘A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings.The difference from GA is that AGA takes objective function as adaptability function directly,so it cuts down some unnecessary time expense because of float-point calculation of function conversion.The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented.It can be applied to a wide class of problems.The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA.The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.
基金Project(J132012C001)supported by Technological Foundation of ChinaProject(2011YQ04013606)supported by National Major Scientific Instrument & Equipment Developing Projects,China
文摘The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balancing its model weight and multi-parametric distributions to the required accuracy. A novel measuring instrument of space manipulator is designed to orbital simulative motion and locational accuracy test. The camera system of space manipulator, calibrated by MOGA algorithm, is used to locational accuracy test in this measuring instrument. The experimental result shows that the absolute errors are [0.07, 1.75] mm for MOGA calibrating model, [2.88, 5.95] mm for MN method, and [1.19, 4.83] mm for LM method. Besides, the composite errors both of LM method and MN method are approximately seven times higher that of MOGA calibrating model. It is suggested that the MOGA calibrating model is superior both to LM method and MN method.
文摘针对目前黄连(Coptis chinensis)人工收获劳动强度大、效率低的问题,该研究在分析黄连种植农艺特性的基础上,提出了一种交替式黄连挖掘装置。首先,在对挖掘装置关键部件曲柄摇杆机构运动学建模的基础上,利用遗传算法,将黄连根土复合体被抛出时的最大速度作为目标函数,以杆长、最小传动角、挖掘深度、最小切土厚度、入土角、切土轨迹、抛出位置为约束条件对曲柄摇杆机构各杆杆长进行了优化求解。进一步,采用图像重建法建立了黄连根茎的三维模型,运用离散元法(discrete element method,DEM)构建了挖掘装置-黄连根茎-土壤的离散元复合模型,并联合多体动力学软件RecurDyn进行了挖掘过程的耦合仿真分析,黄连根土复合体的运动过程及状态变化表明,挖掘装置挖掘性能符合设计要求。最后,制作试验样机进行挖掘性能试验,得到了挖掘装置在前进速度为0.5m/s、曲柄转速为300r/min时挖掘性能最优,其抛送合格率、挖松率、损伤率分别为93.15%、99.28%、2.95%。试验结果表明,该挖掘装置的挖掘性能满足黄连根茎的挖掘要求,可为黄连机械化收获装备的开发提供参考。
