面向CPU、GPU多目标机的混合求解器设计与实现

传统常微分方程的并行求解方法主要包括面向任务的并行和面向方法的并行,但是这两种求解算法,只能利用CPU,或者只能面向同质形式的ODE(ordinary differential equations)簇,存在严重不足。以RIDC(revisionist integral deferred correction)算法为基础,设计了一种面向CPU、GPU多目标机的混合求解器,基于流水线形式求解微分方程组,实现了单个方程组的内部和不同方程组之间的并行计算,进而能够充分发挥GPU的多核优势,有利于计算节点内部的负载均衡。仿真实验验证了框架的效率、准确率和精准度。

TCAS系统多目标机冲突决策处理的研究

本文主要介绍空中交通多目标机冲突的Pairwise决策方式。以空中交通警戒和防撞系统（TCAS）对多目标冲突处理为例，介绍了Pairwise处理方式在实际案例中的应用。详细分析了TCAS系统对多目标机冲突处理的原理、处理流程以及主要处理功能模块。介绍了TCAS对多目标机冲突的分类以及分类后的具体Pairwise处理方式。最后对比了Pairwise与Global两种多目标机冲突的处理方式，分析了两种处理方式的优缺点。

面向多目标机的交叉调试器的研究与设计

为了缩短嵌入式软件开发的时间和提高嵌入式软件的质量,需要研究和开发新的交叉调试器技术。该文研究了交叉调试器的原理和主要技术难点,针对嵌入式软件开发环境与运行环境不同,以及嵌入式芯片多样性的特点,采用构件组装的技术,提出了一种全新结构的交叉调试器的设计方案。此设计方案可以支持多种嵌入式目标机,并且具有良好的目标机重定向性、功能可扩充性和平台可移植性。

应用Lanchester方程解决多机多目标攻击火力分配问题

运用 Lanchester方程 ,将决定攻击优势的火力指数与火力分配原则有机地联系起来。为达到在多机多目标攻击情况下获得最大优势的目的 。

基于遗传算法的多目标最小二乘支持向量机在近红外多组分定量分析中的应用

近红外(NIR)定量分析通常涉及多个组分,采用遗传算法和自适应建模策略,建立了能够对多组分同时定量的多目标最小二乘支持向量机(LS-SVM),并将其应用于玉米中四个组分和连翘中两个活性成分的NIR分析。结果表明多目标遗传算法配合自适应建模策略可保证优化收敛于全局最优解。所建玉米多目标LS-SVM模型明显优于PLS1和PLS2模型;连翘多目标LS-SVM模型与PLS模型均可取得较好的校正和预测效果。两组数据中,径向基神经网络(RBFNN)模型均出现过拟合现象。多目标LS-SVM和单目标LS-SVM性能相近,但多目标LS-SVM建模运行一次即可得到结果,在NIR多组分定量分析中具有潜在应用优势。

超视距多机协同多目标攻击战术决策技术研究

超视距多机协同多目标攻击是现代空战的主要形式,有多项关键技术有待研究,本文针对多机协同多目标攻击的战术决策方法进行了探讨,研究和分析了国内外常用的方法,分析了它们的优缺点,以期为多目标攻击决策的进一步系统性研究提供参考。

基于改进AHP的多机协同多目标攻击威胁评估算法分析

现代空战需要先进的火控计算,目标威胁评估是其重要的组成部分。文章通过建立威胁评估体系模型,再提出威胁因子及权值计算方法,从而构建了一套完整的目标威胁评估算法。最后针对一个多机协同多目标攻击态势,利用建立的算法进行威胁矩阵的计算,计算结果表明该方法是有效可行的。

Optimal design of modular dual-field modulated permanent magnet linear motors with thrust characteristics

To reduce thrust ripple and cost and improve the average thrust of permanent magnet linear motors,a modular dual-field modulation permanent magnet linear motor was studied,and the parameters were optimized.First,sensitive parameters were selected using the Taguchi method,and then the optimal variables were sampled using the optimal Latin hypercube experimental design method and an ensemble of surrogates model of optimization objectives,and its accuracy was verified.Next,a multi-objective particle swarm optimization algorithm was used to optimize the purpose of“maximum average thrust and minimum thrust ripple”,and the Pareto front of average thrust and thrust ripple was obtained.Finite element analysis showed that the optimized modular dual flux-modulation permanent magnet linear motor(MDFMPMLM)had a 29.5%reduction in thrust ripple and a 5%increase in average thrust compared to the original motor.This study provided an effective method for improving the performance of permanent magnet linear motors.

Improved YOLOv8-Based Target Detection Algorithm for UAV Aerial Image

In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.

Improved AVOA based on LSSVM for wind power prediction

Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.

MULTI-FIGHTER COORDINATED MULTI-TARGET ATTACK SYSTEM

A definition of self-determined priority is used in airfight decision firstly. A scheme of grouping the whole fighters is introduced, and the principle of target assignment and fire control is designed. Based on the neutral network, the decision algorithm is derived and the whole coordinated decision system is simulated. Secondly an algorithm for missile-attacking area is described and its calculational result is obtained under initial conditions. Then the attacking of missile is realized by the proportion guidance. Finally, a multi-target attack system. The system includes airfight decision, estimation of missile attack area and calculation of missile attack procedure. A digital simulation demonstrates that the airfight decision algorithm is correct. The methods have important reference values for the study of fire control system of the fourth generation fighter.

MFCMTA空战态势评估研究

针对多机协同多目标攻击(MFCMTA)空战特点,文中提出了基于模糊D-S证据理论的MFCMTA空战态势评估方法。首先,根据模糊集理论和D-S证据理论可以相互取长补短的特点,分析了模糊集理论和D-S证据理论融合的方法,并对mass函数进行了分析计算。其次,对MFCMTA空战的态势评估事件进行分类和提取,对发生事件的状态进行量化。针对态势评估中的不确定性问题,用模糊逻辑对目标事件进行检测,建立了事件检测的模糊逻辑模型,然后引入D-S证据理论,将模糊D-S证据理论应用于MFCMTA态势评估,找出事件与态势假设之间的潜在关系,并根据目标的行为序列来逐步推断其意图,建立了基于模糊D-S证据理论的MFCMTA态势评估模型。最后,给出了算例分析。算例结果表明推理算法和知识表达方式合理,所建立的模型合理有效,是一种有效解决态势评估问题的应用模型。

An Effective Multiple Model Least Squares Method in Tracking of a Maneuvering Target

A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.

Target Tracking Using the Interactive Multiple Model Method

Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.

Simulation-based multi-objective optimization for roll shifting strategy in hot strip mill

A simulation-based multi-objective optimization approach for roll shifting strategy in hot strip mills was presented. Firstly, the effect of roll shifting strategy on wear contour was investigated by mtmerical simulation, and two evaluation indexes including edge smoothness and body smoothness of wear contours were introduced. Secondly, the edge smoothness average and body smoothness average of all the strips in a rolling campaign were selected as objective functions, and shifting control parameters as decision variables, the multi-objective method of MODE/D as the optimizer, and then a simulation-based multi-objective optimization model for roll shifting strategy was built. The experimental result shows that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to roll shifting strategy. Moreover, the conflicting relationship between two objectives can also be found, which indicates another advantage of multi-objective optimization. Finally, industrial test confirms the feasibility of the multi-objective approach for roll shifting strategy, and it can improve strip profile and extend same width rolling miles of a rolling campaign from 35 km to 70 km.

Analytical modeling and multi-objective optimization(MOO) of slippage for wheeled mobile robot(WMR) in rough terrain

Good understanding of relationship between parameters of vehicle, terrain and interaction at the interface is required to develop effective navigation and motion control algorithms for autonomous wheeled mobile robots （AWMR） in rough terrain. A model and analysis of relationship among wheel slippage （S）, rotation angle （0）, sinkage （z） and wheel radius （r） are presented. It is found that wheel rotation angle, sinkage and radius have some influence on wheel slippage. A multi-objective optimization problem with slippage as utility function was formulated and solved in MATLAB. The results reveal the optimal values of wheel-terrain parameters required to achieve optimum slippage on dry sandy terrain. A method of slippage estimation for a five-wheeled mobile robot was presented through comparing the odometric measurements of the powered wheels with those of the fifth non-powered wheel. The experimental result shows that this method is feasible and can be used for online slippage estimation in a sandy terrain.

A combination algorithm of Chaos optimization and genetic algorithm and its application in maneuvering multiple targets data association

The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.

Multi-objective optimization for draft scheduling of hot strip mill

A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective differential evolution algorithm based on decomposition （MODE/D）. The two-objective and three-objective optimization experiments were performed respectively to demonstrate the optimal solutions of trade-off. The simulation results show that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to draft scheduling. The extreme Pareto solutions are found feasible and the centres of the Pareto fronts give a good compromise. The conflict exists between each two ones of three objectives. The final optimal solution is selected from the Pareto-optimal front by the importance of objectives, and it can achieve a better performance in all objective dimensions than the empirical solutions. Finally, the practical application cases confirm the feasibility of the multi-objective approach, and the optimal solutions can gain a better rolling stability than the empirical solutions, and strip flatness decreases from （0± 63） IU to （0±45） IU in industrial production.

Optimized Design for the Plow of a Submarine Plowing Trencher

The plow of the submarine plowing trencher is one of the main functional mechanisms, and its optimization is very important. The design parameters play a very significant role in determining the requirements of the towing force of a vessel. A multi-objective genetic algorithm based on analytical models of the plow surface has been examined and applied in efforts to obtain optimal design of the plow. For a specific soil condition, the draft force and moldboard surface area which are the key parameters in the working process of the plow are optimized by finding the corresponding optimal values of the plow blade penetration angle and two surface angles of the main cutting blade of the plow. Parameters such as the moldboard side angle of deviation, moldboard lift angle, angular variation of the tangent line, and the spanning length are also analyzed with respect to the force of the moldboard surface along soil flow direction. Results show that the optimized plow has an improved plow performance. The draft forces of the main cutting blade and the moldboard are 10.6% and 7%, respectively, less than the original design. The standard deviation of Gaussian curvature of moldboard is lowered by 64.5%, which implies that the smoothness of the optimized moldboard surface is much greater than the original.

Optimization of Multi-Project Multi-Site Location Based on MOPSOs

Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to its useful char- acteristics such as easy implantation, simple parameter settings and fast convergence. First these problems are trans- formed into ones with continuous variables by defining an equivalent probability matrix in this paper, then multi-objective particle swarm optimization based on the minimal particle angle is used to solve them. Methods such as continuation of discrete variables, update of particles for matrix variables, normalization of particle position and evalua- tion of particle fitness are presented. Finally the efficiency of the proposed method is validated by comparing it with other methods on an eight-project-ten-site location problem.