Battlefield target intelligence system architecture modeling and system optimization

To address the current problems of poor generality,low real-time,and imperfect information transmission of the battlefield target intelligence system,this paper studies the battlefield target intelligence system from the top-level perspective of multi-service joint warfare.First,an overall planning and analysis method of architecture modeling is proposed with the idea of a bionic analogy for battlefield target intelligence system architecture modeling,which reduces the difficulty of the planning and design process.The method introduces the Department of Defense architecture framework(DoDAF)modeling method,the multi-living agent(MLA)theory modeling method,and other combinations for planning and modeling.A set of rapid planning methods that can be applied to model the architecture of various types of complex systems is formed.Further,the liveness analysis of the battlefield target intelligence system is carried out,and the problems of the existing system are presented from several aspects.And the technical prediction of the development and construction is given,which provides directional ideas for the subsequent research and development of the battlefield target intelligence system.In the end,the proposed architecture model of the battlefield target intelligence system is simulated and verified by applying the colored Petri nets(CPN)simulation software.The analysis demonstrates the reasonable integrity of its logic.

Research on the Application of Intelligent Optimization Algorithm in Mechanical Design

Intelligent optimization algorithm belongs to a kind of emerging technology,show good characteristics,such as high performance,applicability,its algorithm includes many contents,including genetic,particle swarm and artificial neural network algorithm,compared with the traditional optimization way,these algorithms can be applied to a variety of situations,meet the demand of solution,in the mechanical design industry has wide application prospects.This paper analyzes the application of the algorithm in mechanical design and the comparison of the results to verify the significance of the intelligent optimization algorithm in mechanical design.

Swarm intelligence for mixed-variable design optimization

Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence ap-proach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.

Optimal seismic design of reinforced concrete structures under timehistory earthquake loads using an intelligent hybrid algorithm

A reliable seismic-resistant design of structures is achieved in accordance with the seismic design codes by designing structures under seven or more pairs of earthquake records. Based on the recommendations of seismic design codes, the average time-history responses （ATHR） of structure is required. This paper focuses on the optimal seismic design of reinforced concrete （RC） structures against ten earthquake records using a hybrid of particle swarm optimization algorithm and an intelligent regression model （IRM）. In order to reduce the computational time of optimization procedure due to the computational efforts of time-history analyses, IRM is proposed to accurately predict ATHR of structures. The proposed IRM consists of the combination of the subtractive algorithm （SA）, K-means clustering approach and wavelet weighted least squares support vector machine （WWLS-SVM）. To predict ATHR of structures, first, the input-output samples of structures are classified by SA and K-means clustering approach. Then, WWLS-SVM is trained with few samples and high accuracy for each cluster. 9- and 18-storey RC frames are designed optimally to illustrate the effectiveness and practicality of the proposed IRM. The numerical results demonstrate the efficiency and computational advantages of IRM for optimal design of structures subjected to time-history earthquake loads.

Engineering Design Optimization Based on Intelligent Response Surface Methodology

An intelligent response surface methodology (IRSM) was proposed to achieve the most competitive metal forming products, in which artificial intelligence technologies are introduced into the optimization process. It is used as simple and inexpensive replacement for computationally expensive simulation model. In IRSM, the optimal design space can be reduced greatly without any prior information about function distribution. Also, by identifying the approximation error region, new design points can be supplemented correspondingly to improve the response surface model effectively. The procedure is iterated until the accuracy reaches the desired threshold value. Thus, the global optimization can be performed based on this substitute model. Finally, we present an optimization design example about roll forming of a "U" channel product.

An Intelligent Approach in Design Optimzation Based on Determining Constraints Compatibility

Based on monotonicity analysis and computer symbolic manipulating technique,a procedure for determining constraints compatibility in design optimization hasbeen proposed in this paper. By using the proposed method relationshipsbetween constrains can be determined and the optimization is greatly simplifid.The method is code with intelligent production systems.

Integrated Building Envelope Design Process Combining Parametric Modelling and Multi-Objective Optimization

As an important element in sustainable building design, the building envelope has been witnessing a constant shift in the design approach. Integrating multi-objective optimization (MOO) into the building envelope design process is very promising, but not easy to realize in an actual project due to several factors, including the complexity of optimization model construction, lack of a dynamic-visualization capacity in the simulation tools and consideration of how to match the optimization with the actual design process. To overcome these difficulties, this study constructed an integrated building envelope design process (IBEDP) based on parametric modelling, which was implemented using Grasshopper platform and interfaces to control the simulation software and optimization algorithm. A railway station was selected as a case study for applying the proposed IBEDP, which also utilized a grid-based variable design approach to achieve flexible optimum fenestrations. To facilitate the stepwise design process, a novel strategy was proposed with a two-step optimization, which optimized various categories of variables separately. Compared with a one-step optimization, though the proposed strategy performed poorly in the diversity of solutions, the quantitative assessment of the qualities of Pareto-optimum solution sets illustrates that it is superior. © 2016, Tianjin University and Springer-Verlag Berlin Heidelberg.

Optimal placement of piezoelectric active bars in vibration control by topological optimization

A continuous variable optimization method and a topological optimization method are proposed for the vibration control of piezoelectric truss structures by means of the optimal placements of active bars. In this optimization model, a zero-one discrete variable is defined in order to solve the optimal placement of piezoelectric active bars. At the same time, the feedback gains are also optimized as continuous design variables. A two-phase procedure is proposed to solve the optimization problem. The sequential linear programming algorithm is used to solve optimization problem and the sensitivity analysis is carried out for objective and constraint functions to make linear approximations. On the basis of the Newmark time integration of structural transient dynamic responses, a new sensitivity analysis method is developed in this paper for the vibration control problem of piezoelectric truss structures with respect to various kinds of design variables. Numerical examples are given in the paper to demonstrate the effectiveness of the methods.

AN EFFICIENT ASSESSMENT METHOD FOR INTELLIGENT DESIGN RESULTS OF SHEAR WALL STRUCTURE BASED ON MECHANICAL PERFORMANCE,MATERIAL CONSUMPTION,AND EMPIRICAL RULES

Efficient methods for incorporating engineering experience into the intelligent generation and optimization of shear wall structures are lacking,hindering intelligent design performance assessment and enhancement.This study introduces an assessment method used in the intelligent design and optimization of shear wall structures that effectively combines mechanical analysis and formulaic encoding of empirical rules.First,the critical information about the structure was extracted through data structuring.Second,an empirical rule assessment method was developed based on the engineer's experience and design standards to complete a preliminary assessment and screening of the structure.Subsequently,an assessment method based on mechanical performance and material consumption was used to compare different structural schemes comprehensively.Finally,the assessment effectiveness was demonstrated using a typical case.Compared to traditional assessment methods,the proposed method is more comprehensive and significantly more efficient,promoting the intelligent transformation of structural design.

Optimization of the Design of Structures in Coverage Using the Genetic Algorithm Method

In this research work,an optimization of the design of steel structures is developed through Genetic Algorithms(Gas)with the objective of reducing the amount of material and with them the cost,for which the programming of the GAs tool in the Fortran programming language with the SAP2000 Application Programming Interface(API)v.19 using Visual Studio Professional 2015-Intel?Visual Fortran with Parallel Studio XE.The GAs method consists of randomly searching the Objective Function(OF)of the optimal variables with restrictions.The OF defined by the weight of the structure,the variables in relation to the cross sections of the structural elements and the constraints are the design of resistance using the method of Load and Resistance Factor Design(LRFD),restriction of displacements and restriction of the ratio of slenderness considered in the National Building Regulations(RNE),whose GAs search parameters are given by probability of crossing 70%by simple crossing strategy,probability of mutation 20%,elitism of 5%and maximum number of generations according to the profile.The examined metal structure is a cover with five double-body warren arches with 32 m light connected with 24 joists both with welded connections,that is,rigid.The metal structure was evaluated on demand for loads(dead load,live load,snow load,wind load and earthquake loads).The structural design was carried out with the following profiles:circular,tubular,square,angular and Normal Profile with I-shaped section(IPN)of which the most optimal was the tubular profile.The outer diameters of the cross section are:diagonal with a diameter of 1 in,lower flange with a diameter of 23/8 in,upper flange with a diameter of 11/2 in,inner belt with a diameter of 1 in and belt.outside with a diameter of 11/2 in.

智能精密播种机的优化设计

智能精密播种机是一种自动化的农业设备,可大幅提高种植效率、减少种植成本。但是,目前智能精密播种机依赖高精度的传感器、控制系统和算法来实现准确的种子投放和深度控制,需要较高的技术要求和技术支持,且对于一些不规则形状的种子或大粒小种的种子,适应性较差。为此,提出了一种智能精密播种机优化方案,并使用高精度传感器和控制系统实现多功能播种,控制种子的投放量和调整播种深度,使用节能设备模块化设计和使用易于更换的部件、数据分析功能和简单易用的用户界面。最后,通过田间试验进行功能性试验,结果表明:经过优化后的智能精密播种机播种变异系数≤5%,满足播种农艺要求,可以实现准确的种子投放和深度控制,避免种植密度不均匀和深度不一致等问题,从而提高种植效率和作物品质。

Optimization method for diagnostic sequence based on improved particle swarm optimization algorithm

To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of QPSO algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.

The Realization of the Most Economical and optimized Control System

In order to plow an access to low cost automation, the method to set up the most economical and optimized control system is studied. Such a system is achieved by adopting the field bus technologies based on net connection to form the hierarchical architecture and employing genetic algorithm to intelligently optimize the parameters of the topology structure at the field execution level and the parameters of a local controller. Praxis has proved that this realization can shorten the system development cycle, improve the system's reliability, and achieve conspicuous social economic benefits.

双目标优化与生成对抗网络结合的框架结构阻尼器布置方案智能设计方法

为实现框架结构的阻尼器智能化布置,结合减震设计原理和智能算法,采用双目标优化算法和生成对抗网络算法分别进行阻尼器竖向和水平智能布置研究,并将该方法应用到两个框架结构减震设计工程案例中。在框架结构减震设计中,采用双目标优化算法进行阻尼器竖向布置,并与逐层逼近法、工程师设计和非减震设计进行对比,结果表明,采用该优化算法得到的阻尼器竖向布置方案能有效降低层间位移角和楼层加速度,提高结构的抗震性能。在确定各楼层的阻尼器数量后,利用训练好的生成对抗网络生成模型,可快速、自动地选择和确定各楼层阻尼器的平面安装位置,生成的平面布置与工程师设计的平面布置在相似性差异度综合评价指标上小于临界值0.1,说明两者相似度较高,且有利于提高原结构的抗扭能力。将双目标优化算法与生成对抗网络相结合,不仅能满足框架结构的减震性能目标,而且可实现阻尼器布置方案的智能设计,提升减震工程设计效率。

可重构智能表面辅助通信系统的非线性优化及实验设计

针对可重构智能表面辅助的通信系统,提出了一种智能反射表面开关选择和功率分配的联合设计方法,以改变电磁波传播环境、提高信号传输效率、减少功率消耗。首先,在保证接收端数据传输速率达到一定门限值等多个约束条件下,建立以系统能量效率最大化为目标的多维变量联合非线性优化问题,其中涉及的优化变量包括:智能反射表面开关选择、相位调整和功率分配。在开关选择方面,采用贪婪算法选择开启起主要作用的元器件,并关掉起次要作用的智能反射表面。在功率分配方面,通过求解凸优化问题得到功率分配的闭式解。基于此,设计出一种高传输速率、低功率消耗的迭代优化算法。最后,设计仿真教学实验,通过MATLAB软件,验证了所提出的联合优化算法通过改变电磁波传播环境,有效地提高了信号传输的速率和能量效率;并分析了实验中导致算法性能发生变化的机理,这也有利于帮助学生提高科研创新能力和工程实践能力。

面向综合评估的船舶工装设计方法

针对船舶建造工装设计标准化程度低、设计不规范以及设计管理落后等问题,提出一种面向综合评估的工装设计方法。从工装库构建、智能设计、仿真评估与优化等角度开展工装数字化设计研究。基于工装模型库实现设计模型的有效管理和调用,并通过集成寻优算法实现工装最优布局,在此基础上基于有限元仿真和工艺仿真技术,结合具体指标,实现工装结构力学性能和工艺仿真评估,同时利用结构优化技术对设计结果进行优化,在满足使用需求的基础上降低了加工成本。基于达索三维体验平台开发数字化工装设计系统,固化设计流程,并通过分段吊马设计的实际应用,实现设计效率提升200%,同时重量减轻12.8%。

工程结构超多维变量可微分智能优化方法研究

参数优化问题在工程结构设计、建造、运维等阶段都极为常见。受限于有限元方法的单向分析信息流,传统优化方法主要采用启发式算法,优化效率与可优化变量数目较低,无法满足日趋复杂的工程应用需求。提出了一种基于深度学习的高效可微分结构优化方法,引入了结构体系的高保真图数据表征方法,利用智能代理模型的可微分特性,实现超多维变量的结构高效优化。不规则多层混凝土结构设计参数优化案例表明,可微分结构优化方法可在分钟级别优化数千个变量并达到令人满意的指标,材料用量比人工设计方案降低了13%;相较于优化时间长达数星期的传统优化方法,可微分结构优化方法效率可提高10 000倍以上。可微分结构优化方法可内嵌任意工程目标函数与基于深度学习的结构智能计算模型,能够拓展应用于参数反演等多种任务,具有广泛的工程场景适应性。

启发式优化算法及其在汽车零部件优化设计中的应用对比研究

在汽车结构和零部件设计过程中会产生一系列的优化问题,以实现最佳的性能、最轻的质量和最高的效益。由于优化问题的复杂性,通常利用启发式智能优化算法进行求解。针对启发式优化算法的机理不清晰和其在汽车零部件优化设计过程中效果不明确的问题,对具有代表性的算法进行了统一的推导和表示,利用52组数学测试函数和5个汽车零部件优化案例进行测试分析。结果表明,两类混合改进的优化算法在汽车零部件优化设计问题上的效果较好,同时还给出了工程应用建议和算法研究方向。

基于多目标函数优化的恒应力加速试验设计

对恒应力加速寿命试验进行了优化设计。首先,选取正常应力下可靠性指标估计的准确程度作为评价加速试验方案优劣的准则,推导出正常应力下对数寿命的极大似然估计(MLE)渐近方差的估计值作为单一优化目标函数;然后,在单一优化目标函数的基础上,考虑到试验代价的因素,推导出恒应力加速寿命试验优化设计的多目标函数;最后,将智能优化算法应用到单一目标优化函数和多目标优化函数的求解,通过实例验证解决了加速试验多目标函数优化的求解问题。

恒应力加速试验优化设计的工程实现

对加速寿命试验进行优化设计,以提高试验结果的准确性,同时节省试验成本。对加速寿命试验统计分析进行了深入研究,以恒应力加速寿命试验设计优化为例,通过分析方案要素和约束条件等建立了其优化目标函数,为采用智能算法优化恒应力加速寿命试验设计提供了理论基础。首先,确定研究对象为Weibull寿命分布产品恒应力加速试验的优化设计,根据试验目的选取优化目标函数,对优化目标函数进行推导;然后,针对基于解析优化的加速寿命试验设计方法存在的不足,从工程应用角度出发寻求新的优化方法,提出基于模拟退火算法的试验优化设计方法,并给出相应的应用验证实例;最后,通过实例验证,证明了所提出的方法能够在保证优化结果准确的基础上,解决加速寿命试验优化设计的工程应用问题。