The multi-modes feature, the measure of the manipulating flexibility, andself-reconfiguration control method of the underactuated redundant manipulators are investigatedbased on the optimizing technology. The relation...The multi-modes feature, the measure of the manipulating flexibility, andself-reconfiguration control method of the underactuated redundant manipulators are investigatedbased on the optimizing technology. The relationship between the configuration of the joint spaceand the manipulating flexibility of the underactuated redundant manipulator is analyzed, a newmeasure of manipulating flexibility ellipsoid for the underactuated redundant manipulator withpassive joints in locked mode is proposed, which can be used to get the optimal configuration forthe realization of the self-reconfiguration control. Furthermore, a time-varying nonlinear controlmethod based on harmonic inputs is suggested for fulfilling the self-reconfiguration. A simulationexample of a three-DOFs underactuated manipulator with one passive joint features some aspects ofthe investigations.展开更多
Firstly, in view of the respective defects of existing self-centering devices for vehicle suspension height, the design scheme of the proposed mechanical self-centering device for suspension height is described. Takin...Firstly, in view of the respective defects of existing self-centering devices for vehicle suspension height, the design scheme of the proposed mechanical self-centering device for suspension height is described. Taking the rear suspension of a certain light bus as a research example, the structures and parameters of the novel device are designed and ascertained. Then, the road excitation models, the performance evaluation indexes and the half-vehicle model are built, the simulation outputs of time and frequency domain are obtained with the road excitations of random and pulse by using MATLAB/Simulink software. So the main characteristics of the self-centering suspension are presented preliminarily. Finally, a multi-objective parameter design optimization model for the self-centering device is built by weighted sum approach, and optimal solution is obtained by adopting complex approach. The relevant choosing-type parameters for self-centering device components are deduced by using discrete variable optimal method, and the optimal results are verified and analyzed. So the performance potentials of the self-centering device are exerted fully in condition of ensuring overall suspension performances.展开更多
A new one knob self optimizing fuzzy control system of CO 2 arc welding is established based on the synthetic performance evaluation of droplet transfer process. It includes two kinds of self optimizing fuzzy controll...A new one knob self optimizing fuzzy control system of CO 2 arc welding is established based on the synthetic performance evaluation of droplet transfer process. It includes two kinds of self optimizing fuzzy controllers: the arc voltage controller and the current waveform controller. The fuzzy control principle and the key points of the control patterns are presented. Through on line detecting, computing of characteristic parameters and one knob self optimizing adjusting, the characteristic parameters and welding variables can be adjusted to suitable ranges under the control of the arc voltage controller. Meanwhile the current waveform controller is active in the rear time stage of the short circuiting and the instant of re triggering arc. The experiment results show that the control and its algorithm can improve the synthetic performance of arc welding process apparently.展开更多
针对燃煤电厂参与调峰负荷波动较大,出口SO_(2)浓度控制效果不佳的问题,建立了一种基于捕鱼优化算法(catch fish optimization algorithm,CFOA)优化融合神经网络的出口SO_(2)浓度预测模型。首先使用互信息算法筛选由机理分析得到的特征...针对燃煤电厂参与调峰负荷波动较大,出口SO_(2)浓度控制效果不佳的问题,建立了一种基于捕鱼优化算法(catch fish optimization algorithm,CFOA)优化融合神经网络的出口SO_(2)浓度预测模型。首先使用互信息算法筛选由机理分析得到的特征变量,并通过逐次变分模态分解对筛选后的辅助变量进行分解重构,保留相关性较大的重构分量作为输入变量。随后采用双向时间卷积网络、双向门控循环单元与多头自注意力机制构建融合神经网络模型,通过CFOA对模型超参数寻优以进一步提高精度。最后使用某660 MW燃煤电厂历史运行数据进行对比实验,实验结果表明,该模型在出口SO_(2)浓度剧烈波动的工况下仍能实现较好的预测效果。同多种模型对比,该模型具有更小的误差和更高的预测精度,体现出其在复杂变化环境中的鲁棒性和可靠性。展开更多
In order to improve the extracellular endo-1,4-β-mannosidase(MAN) activity of recombinant Pichia pastoris, optimization of signal peptides was investigated. At first, five potential signal peptides(W1, MF4 I, INU1 A,...In order to improve the extracellular endo-1,4-β-mannosidase(MAN) activity of recombinant Pichia pastoris, optimization of signal peptides was investigated. At first, five potential signal peptides(W1, MF4 I, INU1 A, αpre, HFBI) were chosen to be analyzed by Signal P 4.0, among which W1 was designed. Then, the widely used signal peptide α-factor in expression vector p GAPZαA was replaced by those five signal peptides to reconstruct five new expression vectors. MAN activity was assayed after expression vectors were transformed into Pichia pastoris. The data show that the relative efficiencies of W1, MF4 I, INU1 A, αpre, and HFBI signal peptides are 23.5%, 203.5%, 0, 79.7%, and 120.3% compared with α-factor, respectively. The further gene copy number determination by the quantitative real-time PCR reveals that the MAN activities mediated by α-factor from 1 to 6 gene copy number levels are 12.95, 43.33, 126.63, 173.53, 103.23 and 88.63 U/m L, while those mediated by MF4 I are 79.22, 133.89, 260.14, 347.5, 206.15 and 181.89 U/m L, respectively. The maximum MAN activity reached 347.5 U/m L with 4 gene copies mediated by MF4 I. These results indicate that replacing the signal peptide α-factor with MF4 I and increasing MAN gene copies to a proper number can greatly improve the secretory expression of MAN.展开更多
The real-valued self set in immunity-based network intrusion detection system （INIDS） has some defects： multi-area and overlapping, which are ignored before. The detectors generated by this kind of self set may hav...The real-valued self set in immunity-based network intrusion detection system （INIDS） has some defects： multi-area and overlapping, which are ignored before. The detectors generated by this kind of self set may have the problem of boundary holes between self and nonself regions, and the generation efficiency is low, so that, the self set needs to be optimized before generation stage. This paper proposes a self set optimization algorithm which uses the modified clustering algorithm and Gaussian distribution theory. The clustering deals with multi-area and the Gaussian distribution deals with the overlapping. The algorithm was tested by Iris data and real network data, and the results show that the optimized self set can solve the problem of boundary holes, increase the efficiency of detector generation effectively, and improve the system＇s detection rate.展开更多
The success in synthesizing high purity SnO2 nanobelts via self-propagating high tem- perature synthesis was achieved. According to the experimental results, a relationship between the residual heat of the reaction sy...The success in synthesizing high purity SnO2 nanobelts via self-propagating high tem- perature synthesis was achieved. According to the experimental results, a relationship between the residual heat of the reaction system at 2848 K and SnO2 nanobelts yield was identified. The most appropriate formula for this self-propagating high tempera- ture synthesis method is turned out to be composed of Sn powder and thermite with weight ratio of 1：1.展开更多
Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufactur...Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.展开更多
There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced se...There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.展开更多
Based on the newly-developed element energy projection （EEP） method with optimal super-convergence order for computation of super-convergent results, an improved self-adaptive strategy for one-dimensional finite ele...Based on the newly-developed element energy projection （EEP） method with optimal super-convergence order for computation of super-convergent results, an improved self-adaptive strategy for one-dimensional finite element method （FEM） is proposed. In the strategy, a posteriori errors are estimated by comparing FEM solutions to EEP super-convergent solutions with optimal order of super-convergence, meshes are refined by using the error-averaging method. Quasi-FEM solutions are used to replace the true FEM solutions in the adaptive process. This strategy has been found to be simple, clear, efficient and reliable. For most problems, only one adaptive step is needed to produce the required FEM solutions which pointwise satisfy the user specified error tolerances in the max-norm. Taking the elliptical ordinary differential equation of the second order as the model problem, this paper describes the fundamental idea, implementation strategy and computational algorithm and representative numerical examples are given to show the effectiveness and reliability of the proposed approach.展开更多
Earthquake may cause severe damage to all kinds of bridge such as the falling down of the girder; therefore,effective measures should be employed to control the seismic displacement. In this paper,the method of compre...Earthquake may cause severe damage to all kinds of bridge such as the falling down of the girder; therefore,effective measures should be employed to control the seismic displacement. In this paper,the method of comprehensive optimal control,com-bined with analytic hierarchy process,is employed to investigate the seismic response control of the Nanjing Jiangxinzhou Bridge,which is a single-tower self-anchored suspension bridge （SSSB）. Also,3-dimensional nonlinear seismic response analyses are con-ducted. Three types of practical connection measures for seismic response control of SSSB are investigated,and the optimal pa-rameters of the connection devices are achieved by this method. Results show that both the elastic connection devices and the damp-ers with rational parameters can reduce the seismic displacement of the bridge effectively,but the elastic connection devices will in-crease the seismic force of the tower. When all factors are consid-ered,the optimal measure is by using the elastic connection devices and the dampers together. These results can provide references for seismic response control of SSSBs.展开更多
Since digital circuits have been widely and thoroughly applied in various fields, electronic systems are increasingly more complicated and require greater reliability. Faults may occur in electronic systems in complic...Since digital circuits have been widely and thoroughly applied in various fields, electronic systems are increasingly more complicated and require greater reliability. Faults may occur in electronic systems in complicated environments. If immediate field repairs are not made on the faults, electronic systems will not run normally, and this will lead to serious losses. The traditional method for improving system reliability based on redundant fault-tolerant technique has been unable to meet the requirements. Therefore, on the basis of(evolvable hardware)-based and(reparation balance technology)-based electronic circuit fault self-repair strategy proposed in our preliminary work, the optimal design of rectification circuits(RTCs) in electronic circuit fault self-repair based on global signal optimization is deeply researched in this paper. First of all, the basic theory of RTC optimal design based on global signal optimization is proposed. Secondly, relevant considerations and suitable ranges are analyzed. Then, the basic flow of RTC optimal design is researched. Eventually, a typical circuit is selected for simulation verification, and detailed simulated analysis is made on five circumstances that occur during RTC evolution. The simulation results prove that compared with the conventional design method based RTC, the global signal optimization design method based RTC is lower in hardware cost, faster in circuit evolution, higher in convergent precision, and higher in circuit evolution success rate. Therefore, the global signal optimization based RTC optimal design method applied in the electronic circuit fault self-repair technology is proven to be feasible, effective, and advantageous.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 federated self-supervised framework is a distributed machine learning method that combines federated learning and self-supervised learning, which can effectively solve the problem of traditional federated learning...The federated self-supervised framework is a distributed machine learning method that combines federated learning and self-supervised learning, which can effectively solve the problem of traditional federated learning being difficult to process large-scale unlabeled data. The existing federated self-supervision framework has problems with low communication efficiency and high communication delay between clients and central servers. Therefore, we added edge servers to the federated self-supervision framework to reduce the pressure on the central server caused by frequent communication between both ends. A communication compression scheme using gradient quantization and sparsification was proposed to optimize the communication of the entire framework, and the algorithm of the sparse communication compression module was improved. Experiments have proved that the learning rate changes of the improved sparse communication compression module are smoother and more stable. Our communication compression scheme effectively reduced the overall communication overhead.展开更多
Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the m...Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the monitoring range and the communication range, and proposes an optimal deployment with self-healing movement algorithm for closed or semi-closed area with irregular shape, which can not only satisfy both coverage and connectivity by using as few nodes as possible, but also compensate the failure of nodes by mobility in WSNs. We compute the maximum efficient range of several neighbor sensors based on the dif-ferent relationships between monitoring range and communication range with consideration of the complex boundary or obstacles in the region, and combine it with the Euclidean Minimum Spanning Tree (EMST) algorithm to ensure the coverage and communication of Region of Interest (ROI). Besides, we calculate the location of dead nodes by Geometry Algorithm, and move the higher priority nodes to replace them by an-other Improved Virtual Force Algorithm (IVFA). Eventually, simulation results based-on MATLAB are presented, which do show that this optimal deployment with self-healing movement algorithm can ensure the coverage and communication of an entire region by requiring the least number of nodes and effectively compensate the loss of the networks.展开更多
基金This project is supported by National Natural Science Foundation of China (No.50375007,No.50475177).
文摘The multi-modes feature, the measure of the manipulating flexibility, andself-reconfiguration control method of the underactuated redundant manipulators are investigatedbased on the optimizing technology. The relationship between the configuration of the joint spaceand the manipulating flexibility of the underactuated redundant manipulator is analyzed, a newmeasure of manipulating flexibility ellipsoid for the underactuated redundant manipulator withpassive joints in locked mode is proposed, which can be used to get the optimal configuration forthe realization of the self-reconfiguration control. Furthermore, a time-varying nonlinear controlmethod based on harmonic inputs is suggested for fulfilling the self-reconfiguration. A simulationexample of a three-DOFs underactuated manipulator with one passive joint features some aspects ofthe investigations.
基金supported by Youth Technological Phosphor Project of Shanghai City (No.04QMX1474).
文摘Firstly, in view of the respective defects of existing self-centering devices for vehicle suspension height, the design scheme of the proposed mechanical self-centering device for suspension height is described. Taking the rear suspension of a certain light bus as a research example, the structures and parameters of the novel device are designed and ascertained. Then, the road excitation models, the performance evaluation indexes and the half-vehicle model are built, the simulation outputs of time and frequency domain are obtained with the road excitations of random and pulse by using MATLAB/Simulink software. So the main characteristics of the self-centering suspension are presented preliminarily. Finally, a multi-objective parameter design optimization model for the self-centering device is built by weighted sum approach, and optimal solution is obtained by adopting complex approach. The relevant choosing-type parameters for self-centering device components are deduced by using discrete variable optimal method, and the optimal results are verified and analyzed. So the performance potentials of the self-centering device are exerted fully in condition of ensuring overall suspension performances.
文摘A new one knob self optimizing fuzzy control system of CO 2 arc welding is established based on the synthetic performance evaluation of droplet transfer process. It includes two kinds of self optimizing fuzzy controllers: the arc voltage controller and the current waveform controller. The fuzzy control principle and the key points of the control patterns are presented. Through on line detecting, computing of characteristic parameters and one knob self optimizing adjusting, the characteristic parameters and welding variables can be adjusted to suitable ranges under the control of the arc voltage controller. Meanwhile the current waveform controller is active in the rear time stage of the short circuiting and the instant of re triggering arc. The experiment results show that the control and its algorithm can improve the synthetic performance of arc welding process apparently.
文摘针对燃煤电厂参与调峰负荷波动较大,出口SO_(2)浓度控制效果不佳的问题,建立了一种基于捕鱼优化算法(catch fish optimization algorithm,CFOA)优化融合神经网络的出口SO_(2)浓度预测模型。首先使用互信息算法筛选由机理分析得到的特征变量,并通过逐次变分模态分解对筛选后的辅助变量进行分解重构,保留相关性较大的重构分量作为输入变量。随后采用双向时间卷积网络、双向门控循环单元与多头自注意力机制构建融合神经网络模型,通过CFOA对模型超参数寻优以进一步提高精度。最后使用某660 MW燃煤电厂历史运行数据进行对比实验,实验结果表明,该模型在出口SO_(2)浓度剧烈波动的工况下仍能实现较好的预测效果。同多种模型对比,该模型具有更小的误差和更高的预测精度,体现出其在复杂变化环境中的鲁棒性和可靠性。
基金Project(13JJ9002)supported by Hunan Provincial Natural Science Foundation of ChinaProject(2012XK4081)supported by the Key Science Technology Plan Project of Hunan Provincial Science&Technology Department,ChinaProject(CX2012B124)supported by the Graduate Degree Thesis Innovation Program of Hunan Province,China
文摘In order to improve the extracellular endo-1,4-β-mannosidase(MAN) activity of recombinant Pichia pastoris, optimization of signal peptides was investigated. At first, five potential signal peptides(W1, MF4 I, INU1 A, αpre, HFBI) were chosen to be analyzed by Signal P 4.0, among which W1 was designed. Then, the widely used signal peptide α-factor in expression vector p GAPZαA was replaced by those five signal peptides to reconstruct five new expression vectors. MAN activity was assayed after expression vectors were transformed into Pichia pastoris. The data show that the relative efficiencies of W1, MF4 I, INU1 A, αpre, and HFBI signal peptides are 23.5%, 203.5%, 0, 79.7%, and 120.3% compared with α-factor, respectively. The further gene copy number determination by the quantitative real-time PCR reveals that the MAN activities mediated by α-factor from 1 to 6 gene copy number levels are 12.95, 43.33, 126.63, 173.53, 103.23 and 88.63 U/m L, while those mediated by MF4 I are 79.22, 133.89, 260.14, 347.5, 206.15 and 181.89 U/m L, respectively. The maximum MAN activity reached 347.5 U/m L with 4 gene copies mediated by MF4 I. These results indicate that replacing the signal peptide α-factor with MF4 I and increasing MAN gene copies to a proper number can greatly improve the secretory expression of MAN.
基金Supported by the National Natural Science Foundation of China （No. 60671049, 61172168）and Graduate Innovation Project of Heilongjiang （No. YJSCX2011-034HLI）
文摘The real-valued self set in immunity-based network intrusion detection system （INIDS） has some defects： multi-area and overlapping, which are ignored before. The detectors generated by this kind of self set may have the problem of boundary holes between self and nonself regions, and the generation efficiency is low, so that, the self set needs to be optimized before generation stage. This paper proposes a self set optimization algorithm which uses the modified clustering algorithm and Gaussian distribution theory. The clustering deals with multi-area and the Gaussian distribution deals with the overlapping. The algorithm was tested by Iris data and real network data, and the results show that the optimized self set can solve the problem of boundary holes, increase the efficiency of detector generation effectively, and improve the system＇s detection rate.
基金supported by the Natural Science Foundation of Hubei Province (No.2009CDB300)the Fundamental Research Funds for the Central Universities of China (No.5082001)
文摘The success in synthesizing high purity SnO2 nanobelts via self-propagating high tem- perature synthesis was achieved. According to the experimental results, a relationship between the residual heat of the reaction system at 2848 K and SnO2 nanobelts yield was identified. The most appropriate formula for this self-propagating high tempera- ture synthesis method is turned out to be composed of Sn powder and thermite with weight ratio of 1：1.
基金Supported by National Natural Science Foundation of China(Grant No.61272428)PhD Programs Foundation of Ministry of Education of China(Grant No.20120002110067)
文摘Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.
基金supported by the Aviation Science Funds of China(2010ZC13012)the Fund of Jiangsu Innovation Program for Graduate Education (CXLX11 0203)
文摘There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.
基金the National Natural Science Foundation of China(No.50678093)Program for Changjiang Scholars and Innovative Research Team in University(No.IRT00736)
文摘Based on the newly-developed element energy projection （EEP） method with optimal super-convergence order for computation of super-convergent results, an improved self-adaptive strategy for one-dimensional finite element method （FEM） is proposed. In the strategy, a posteriori errors are estimated by comparing FEM solutions to EEP super-convergent solutions with optimal order of super-convergence, meshes are refined by using the error-averaging method. Quasi-FEM solutions are used to replace the true FEM solutions in the adaptive process. This strategy has been found to be simple, clear, efficient and reliable. For most problems, only one adaptive step is needed to produce the required FEM solutions which pointwise satisfy the user specified error tolerances in the max-norm. Taking the elliptical ordinary differential equation of the second order as the model problem, this paper describes the fundamental idea, implementation strategy and computational algorithm and representative numerical examples are given to show the effectiveness and reliability of the proposed approach.
基金Supported by the National Natural Science Foundation of China for Distinguished Young Scientists (50725828)the National Natural Science Foundation of China (50908046)the Specialized Research Fund for the Doctoral Program of Higher Education (200802861012)
文摘Earthquake may cause severe damage to all kinds of bridge such as the falling down of the girder; therefore,effective measures should be employed to control the seismic displacement. In this paper,the method of comprehensive optimal control,com-bined with analytic hierarchy process,is employed to investigate the seismic response control of the Nanjing Jiangxinzhou Bridge,which is a single-tower self-anchored suspension bridge （SSSB）. Also,3-dimensional nonlinear seismic response analyses are con-ducted. Three types of practical connection measures for seismic response control of SSSB are investigated,and the optimal pa-rameters of the connection devices are achieved by this method. Results show that both the elastic connection devices and the damp-ers with rational parameters can reduce the seismic displacement of the bridge effectively,but the elastic connection devices will in-crease the seismic force of the tower. When all factors are consid-ered,the optimal measure is by using the elastic connection devices and the dampers together. These results can provide references for seismic response control of SSSBs.
基金supported by the National Natural Science Foundation of China (Nos. 61271153, 61372039)
文摘Since digital circuits have been widely and thoroughly applied in various fields, electronic systems are increasingly more complicated and require greater reliability. Faults may occur in electronic systems in complicated environments. If immediate field repairs are not made on the faults, electronic systems will not run normally, and this will lead to serious losses. The traditional method for improving system reliability based on redundant fault-tolerant technique has been unable to meet the requirements. Therefore, on the basis of(evolvable hardware)-based and(reparation balance technology)-based electronic circuit fault self-repair strategy proposed in our preliminary work, the optimal design of rectification circuits(RTCs) in electronic circuit fault self-repair based on global signal optimization is deeply researched in this paper. First of all, the basic theory of RTC optimal design based on global signal optimization is proposed. Secondly, relevant considerations and suitable ranges are analyzed. Then, the basic flow of RTC optimal design is researched. Eventually, a typical circuit is selected for simulation verification, and detailed simulated analysis is made on five circumstances that occur during RTC evolution. The simulation results prove that compared with the conventional design method based RTC, the global signal optimization design method based RTC is lower in hardware cost, faster in circuit evolution, higher in convergent precision, and higher in circuit evolution success rate. Therefore, the global signal optimization based RTC optimal design method applied in the electronic circuit fault self-repair technology is proven to be feasible, effective, and advantageous.
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 federated self-supervised framework is a distributed machine learning method that combines federated learning and self-supervised learning, which can effectively solve the problem of traditional federated learning being difficult to process large-scale unlabeled data. The existing federated self-supervision framework has problems with low communication efficiency and high communication delay between clients and central servers. Therefore, we added edge servers to the federated self-supervision framework to reduce the pressure on the central server caused by frequent communication between both ends. A communication compression scheme using gradient quantization and sparsification was proposed to optimize the communication of the entire framework, and the algorithm of the sparse communication compression module was improved. Experiments have proved that the learning rate changes of the improved sparse communication compression module are smoother and more stable. Our communication compression scheme effectively reduced the overall communication overhead.
文摘Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the monitoring range and the communication range, and proposes an optimal deployment with self-healing movement algorithm for closed or semi-closed area with irregular shape, which can not only satisfy both coverage and connectivity by using as few nodes as possible, but also compensate the failure of nodes by mobility in WSNs. We compute the maximum efficient range of several neighbor sensors based on the dif-ferent relationships between monitoring range and communication range with consideration of the complex boundary or obstacles in the region, and combine it with the Euclidean Minimum Spanning Tree (EMST) algorithm to ensure the coverage and communication of Region of Interest (ROI). Besides, we calculate the location of dead nodes by Geometry Algorithm, and move the higher priority nodes to replace them by an-other Improved Virtual Force Algorithm (IVFA). Eventually, simulation results based-on MATLAB are presented, which do show that this optimal deployment with self-healing movement algorithm can ensure the coverage and communication of an entire region by requiring the least number of nodes and effectively compensate the loss of the networks.
