Speed Regulation Method Using Genetic Algorithm for Dual Three-phase Permanent Magnet Synchronous Motors

Dual three-phase Permanent Magnet Synchronous Motor(DTP-PMSM)is a nonlinear,strongly coupled,high-order multivariable system.In today’s application scenarios,it is difficult for traditional PI controllers to meet the requirements of fast response,high accuracy and good robustness.In order to improve the performance of DTP-PMSM speed regulation system,a control strategy of PI controller based on genetic algorithm is proposed.Firstly,the basic mathematical model of DTP-PMSM is established,and the PI parameters of DTP-PMSM speed regulation system are optimized by genetic algorithm,and the modeling and simulation experiments of DTP-PMSM control system are carried out by MATLAB/SIMULINK.The simulation results show that,compared with the traditional PI control,the proposed algorithm significantly improves the performance of the control system,and the speed output overshoot of the GA-PI speed control system is smaller.The anti-interference ability is stronger,and the torque and double three-phase current output fluctuations are smaller.

Research on Flexible Job Shop Scheduling Based on Improved Two-Layer Optimization Algorithm

To improve the productivity,the resource utilization and reduce the production cost of flexible job shops,this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching.Firstly,a mathematical model is established to minimize the maximum completion time.Secondly,an improved two-layer optimization algorithm is designed:the outer layer algorithm uses an improved PSO(Particle Swarm Optimization)to solve the workpiece batching problem,and the inner layer algorithm uses an improved GA(Genetic Algorithm)to solve the dual-resource scheduling problem.Then,a rescheduling method is designed to solve the task disturbance problem,represented by machine failures,occurring in the workshop production process.Finally,the superiority and effectiveness of the improved two-layer optimization algorithm are verified by two typical cases.The case results show that the improved two-layer optimization algorithm increases the average productivity by 7.44% compared to the ordinary two-layer optimization algorithm.By setting the different numbers of AGVs(Automated Guided Vehicles)and analyzing the impact on the production cycle of the whole order,this paper uses two indicators,the maximum completion time decreasing rate and the average AGV load time,to obtain the optimal number of AGVs,which saves the cost of production while ensuring the production efficiency.This research combines the solved problem with the real production process,which improves the productivity and reduces the production cost of the flexible job shop,and provides new ideas for the subsequent research.

Research on Grid Planning of Dual Power Distribution Network Based on Parallel Ant Colony Optimization Algorithm

A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement.

A slope-based decoupling algorithm to simultaneously control dual deformable mirrors in a woofer–tweeter adaptive optics system

We propose a slope-based decoupling algorithm to simultaneously control the dual deformable mirrors （DMs） in a woofer-tweeter adaptive optics system. This algorithm can directly use the woofer＇s response matrix measured from a Shack-Hartmann wave-front sensor to construct a slope-based orthogonal basis, and then selectively distribute the large- amplitude low-order aberration to woofer DM and the remaining aberration to tweeter DM through the slope-based orthogonal basis. At the same moment, in order to avoid the two DMs generating opposite compensation, a constraint matrix used to reset tweeter control vector is convenient to be calculated with the slope-based orthogonal basis. Numeral simulation demonstrates that this algorithm has a good performance to control the adaptive optics system with dual DMs simultaneously. Compared with the typical decoupling algorithm, this algorithm can take full use of the compensation ability of woofer DM and release the stroke of tweeter DM to compensate high-order aberration. More importantly, it does not need to measure the accurate shape of tweeter＇s influence function and keeps better performance of restraining the coupling error with the continuous-dynamic aberration.

Improved Dual Algorithm for Constrained Optimization Problems

One class of effective methods for the optimization problem with inequality constraints are to transform the problem to a unconstrained optimization problem by constructing a smooth potential function. In this paper, we modifies a dual algorithm for constrained optimization problems and establishes a corresponding improved dual algorithm; It is proved that the improved dual algorithm has the local Q-superlinear convergence; Finally, we performed numerical experimentation using the improved dual algorithm for many constrained optimization problems, the numerical results are reported to show that it is valid in practical computation.

DESIGN AND IMPLEMENTATION OF DUAL-FIELD MODULAR INVERSION ALGORITHM

Modular inverse arithmetic plays an important role in elliptic curve cryptography. Based on the analysis of Montgomery modular inversion algorithm, this paper presents a new dual-field modular inversion algorithm, and a novel scalable and unified architecture for Montgomery inverse hardware in finite fields GF(p) and GF(2n) is proposed. Furthermore, this architecture based on the new modular inversion algorithm has been verified by modeling it in Verilog-HDL, and accomplished it under 0.18 μm CMOS technology. The result indicates that our work has better performance and flexibility than other works.

Scheduling algorithm of dual-armed cluster tools with residency time and reentrant constraints

To solve the scheduling problem of dual-armed cluster tools for wafer fabrications with residency time and reentrant constraints,a heuristic scheduling algorithm was developed.Firstly,on the basis of formulating scheduling problems domain of dual-armed cluster tools,a non-integer programming model was set up with a minimizing objective function of the makespan.Combining characteristics of residency time and reentrant constraints,a scheduling algorithm of searching the optimal operation path of dual-armed transport module was presented under many kinds of robotic scheduling paths for dual-armed cluster tools.Finally,the experiments were designed to evaluate the proposed algorithm.The results show that the proposed algorithm is feasible and efficient for obtaining an optimal scheduling solution of dual-armed cluster tools with residency time and reentrant constraints.

A Primal-Dual Infeasible-Interior-Point Algorithm for Multiple Objective Linear Programming Problems

A primal-dual infeasible interior point algorithm for multiple objective linear programming (MOLP) problems was presented. In contrast to the current MOLP algorithm. moving through the interior of polytope but not confining the iterates within the feasible region in our proposed algorithm result in a solution approach that is quite different and less sensitive to problem size, so providing the potential to dramatically improve the practical computation effectiveness.

Identification and Removal of Non-meteorological Echoes in Dual-polarization Radar Data Based on a Fuzzy Logic Algorithm

A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek.For selected precipitation and non-meteorological events,the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values.The membership functions and weights are then determined by these density functions.Finally,the nonmeteorological echoes are identified by combining the membership functions and weights.The performance is qualitatively evaluated by long-term rain accumulation.The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection（POD）,false alarm rate（FAR）,and clutter–signal ratio（CSR）.In addition,the issues in using filtered dual-polarization data are alleviated.

A Dual-Satellite GNSS Positioning Algorithm of High Accuracy in Incomplete Condition

Global navigation satellite system could provide accurate positioning results in signal complete condition. However, the performance is severe when signal denied, especially for the single-mode Bei Dou receiver. This paper proposes a dual-satellite positioning algorithm to promote the positioning performance in the satellite signal gap. The new algorithm utilizes the previous positioning data stored in complete condition to simplify the positioning equations. As the clock bias persists for a short period, this proposed method could work out accurate positioning results by only two visible satellites, without the need of computing the clock bias. Also, the Kalman filtering algorithm is used to smooth the trajectories, and improve the positioning results. During the incomplete period, only two satellites for 30 seconds and three satellites for 60 seconds, the preliminary experiment result shows that, the presented method could provide almost the same positioning results as in complete condition.

Generalized Yule-walker and two-stage identification algorithms for dual-rate systems

In this paper, two approaches are developed for directly identifying single-rate models of dual-rate stochastic systems in which the input updating frequency is an integer multiple of the output sampling frequency. The first is the generalized Yule-Walker algorithm and the second is a two-stage algorithm based on the correlation technique. The basic idea is to directly identify the parameters of underlying single-rate models instead of the lifted models of dual-rate systems from the dual-rate input-output data, assuming that the measurement data are stationary and ergodic. An example is given.

Building Bayesian Network(BN)-Based System Reliability Model by Dual Genetic Algorithm(DGA)

A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.

FURTHER STUDY ON A DUAL ALGORITHM

The dual algorithm for minimax problems is further studied in this paper.The resulting theoretical analysis shows that the condition number of the corresponding Hessian of the smooth modified Lagrange function with changing parameter in the dual algorithm is proportional to the reciprocal of the parameter,which is very important for the efficiency of the dual algorithm.At last,the numerical experiments are reported to validate the analysis results.

A Primal-Dual Simplex Algorithm for Solving Linear Programming Problems with Symmetric Trapezoidal Fuzzy Numbers

Two existing methods for solving a class of fuzzy linear programming (FLP) problems involving symmetric trapezoidal fuzzy numbers without converting them to crisp linear programming problems are the fuzzy primal simplex method proposed by Ganesan and Veeramani [1] and the fuzzy dual simplex method proposed by Ebrahimnejad and Nasseri [2]. The former method is not applicable when a primal basic feasible solution is not easily at hand and the later method needs to an initial dual basic feasible solution. In this paper, we develop a novel approach namely the primal-dual simplex algorithm to overcome mentioned shortcomings. A numerical example is given to illustrate the proposed approach.

Research on Optimization of Dual-Resource Batch Scheduling in Flexible Job Shop

With the rapid development of intelligent manufacturing and the changes in market demand,the current manufacturing industry presents the characteristics of multi-varieties,small batches,customization,and a short production cycle,with the whole production process having certain flexibility.In this paper,a mathematical model is established with the minimum production cycle as the optimization objective for the dual-resource batch scheduling of the flexible job shop,and an improved nested optimization algorithm is designed to solve the problem.The outer layer batch optimization problem is solved by the improved simulated annealing algorithm.The inner double resource scheduling problem is solved by the improved adaptive genetic algorithm,the double coding scheme,and the decoding scheme of Automated Guided Vehicle(AGV)scheduling based on the scheduling rules.The time consumption of collision-free paths is solved with the path planning algorithm which uses the Dijkstra algorithm based on a time window.Finally,the effectiveness of the algorithm is verified by actual cases,and the influence of AGV with different configurations on workshop production efficiency is analyzed.

基于源荷匹配的梯级水风光蓄系统优化运行研究

在“双碳”目标下,我国亟需进行能源结构转型,提高可再生能源在电力系统中的比例。调节性水电站和抽蓄电站具有良好的调蓄能力,可以作为调节电源与风光电源联合运行,从前端减缓风光不稳定出力对电力系统的冲击,促进新能源集中上网消纳。因此,充分利用梯级水电站及抽水蓄能电站的调节性能,制定合理的一体化调度策略,有利于在发电侧实现水风光蓄打捆送出和协同消纳,助力“双碳”目标实现。基于对梯级水风光蓄互补发电系统在短期调度中电网侧负荷需求和梯级水电水资源利用率的考虑,构建以源荷差异最小、梯级水电蓄能增量最大为目标,以常规水电站、风光电站、抽水蓄能电站安全运行条件为约束的短期互补调度模型,并采用粒子群-动态规划内外层嵌套算法对模型进行求解。选取西南某流域常规梯级水电站、抽水蓄能电站及周边风光电站组成的水风光蓄互补发电系统为研究对象进行模拟计算,结果表明:(1)抽水蓄能电站倾向于在梯级电站水头较低时选择泄水发电,而在梯级电站水头较高时选择抽水或维持当前水位不变;(2)所构建模型有效提高了系统总出力曲线与电网负荷曲线的匹配度,实现了梯级水电站之间负荷与水量的合理分配,一定程度上减少了梯级水电站效益损耗,提高了水能利用率,增加了梯级水电蓄能增量,有利于源网协同运行和梯级电站之间的稳定运行。

基于电气自动化的灌溉水电双控系统设计

针对我国灌溉系统水资源利用率较低的问题,基于电气自动化对灌溉水电双控系统进行了设计。灌溉水电双控系统主要组成包括控制模块、信息采集模块、收费管理模块、执行模块和通信模块。为了实现节水灌溉,提升水利用率,在管理方面对控制系统软件进行设计,在技术方面对控制系统算法进行设计。为了验证灌溉系统的性能,对系统进行了数据计量准确性试验和节水灌溉运行试验。试验结果表明:系统可以准确地测量用电量和用水量,且可自动启动节水灌溉。

基于全变分展开的低剂量CT重建网络

针对CT迭代展开重建网络仅对数据保真项进行神经网络展开降低了重建网络计算性能的问题,通过对基于全变分的CT迭代重建算法进行神经网络展开,提出一种对数据保真项和全变分正则项全部进行神经网络展开的重建网络,从而改善了CT重建图像的视觉质量。首先,采用原始–对偶算法求解基于全变分的CT重建问题,得到易于神经网络展开的迭代重建算法。然后,对该迭代重建算法进行神经网络展开,尤其是对正则项部分的算法进行神经网络展开,得到迭代展开CT重建网络。在模拟的低剂量CT数据集上验证了该算法的有效性。实验结果表明,与6种低剂量CT重建算法相比,该算法在抑制低剂量CT图像噪声的同时,很好地保留了图像中的结构和细节纹理。重建图像的定量评价分析显示,该算法取得了良好的峰值信噪比和归一化均方误差指标值,验证了提出的低剂量CT重建算法具有较好的噪声抑制能力和较强的鲁棒性。

双重信息引导的蚁群算法求解绿色多舱车辆路径问题

针对当前实际运输中广泛存在的绿色多舱车辆路径问题(GMCVRP),文章提出一种双重信息引导的蚁群优化算法(DIACO)进行求解.首先,在DIACO的全局搜索阶段,重新构建传统蚁群优化算法(TACO)中的信息素浓度矩阵(PCM),使其同时包含客户块信息和客户序列信息,即建立具有双重信息的PCM(DIPCM),从而更全面学习和累积优质解的信息;采用3种启发式方法生成较高质量个体,用于初始化DIPCM,可快速引导算法朝向解空间中优质区域进行搜索.其次,在DIACO的局部搜索阶段,设计结合自适应策略的多种变邻域操作,用于对解空间的优质区域执行深入搜索.再次,提出信息素浓度平衡机制,以防止搜索陷入停滞.最后,使用不同规模的算例进行仿真测试和算法对比,结果验证了DIACO是求解GMCVRP的有效算法.

基于X射线双光谱的物质识别方法研究

针对工业生产、材料科学和安检等领域对于物质的检测需求,开展了基于X射线双光谱的物质识别方法研究。提出了针对不同物质的双光谱识别算法模型,模型引入边界物质界定与伪彩色算法,实现了对物质种类的识别。搭建X射线双光谱透射实验系统,实测了复杂背景、多种物体的X射线双光谱图像,研究了物质界定算法与伪彩色算法对于物质种类识别的影响。基于此优化了算法模型,实现了灵活、快速地对物质种类的识别,对于工业生产物质分类和安检快速筛查具有重要意义。