一个新的线光源照明模型

提出了一个有效的线光源照明模型。该模型同时考虑了线光源在被照明射表面引起的漫反射分量和镜面反射分量。通过将线光源看作一个具有无限细小宽度的有向长方形光源，我们得到了一个计算漫反射分量的简洁公式。而镜面反射发量被表示成一个Ｐｈｏｎｇ镜面模型为积分核的积分，高斯积分技术则用来求解该积分。考虑到镜面反射分量的求解比较复杂，我们通过一个简单的测试算法来快速决定当前被照射点是否位于高光区域内。最后，结合ｓｈ     

基于遗传算法的车灯线光源优化设计

采用遗传算法建立了遗传算法曲面反射点搜索模型,将其用于特殊反射点的寻找。经过多次试验确定遗传算法中所涉及参数的值,最终找出抛物面上的特殊反射点,建立线光源发光功率(P)和线光源长度(L)的函数关系,求出最低功率时线光源的长度。实验表明,该模型算法准确,速度快,收敛性好,适应性强,适用于非规则的,任意曲面点的搜索。     

雷达装备测试性设计优化技术

针对雷达装备测试性优化设计的实际技术需求,对雷达测试性优化设计过程进行了分析。以雷达装备各阶段数据为基础,综合考虑测试效能、测试代价、可靠性约束等要素,研究了基于测试代价和测试效能的测试性优化方法,并给出了基于最小测试代价的雷达系统测试性优化模型。针对模型多目标优化求解问题,给出了一种基于改进的粒子群优化算法。该算法引入混沌理论,使初始种群呈现多样性,避免了传统粒子群算法的早熟现象,同时提高了搜索的精度和速度。通过对案例的仿真与验证表明,利用这种改进的粒子群算法对基于最小测试代价的测试性优化模型进行求解时,能够在满足模型目标函数的约束条件下,获得全局最优解。     

基于多信号流图模型的典型无人机测控系统测试性优化设计方法研究

针对无人机测控系统维修保障和故障诊断定位的实际需求,引入基于多信号流图模型,建立了无人机测控系统测试性优化设计方法。首先,介绍了多信号流图建模基本思路,给出了多信号流图的建模步骤;然后,结合典型无人机测控系统组成及工作原理,构建了无人机测控系统的多层次多信号流图模型,在此基础上开展了测试性预测和测试性优化设计,总结后给出了工程设计中的关键注意事项。研究结果表明：该方法可以有效提高无人机测控系统的故障检测率和故障隔离率,减小模糊组,优化后系统的测试性设计水平得到了提高,说明了该方法的有效性。     

基于Hough变换的输送带纵向撕裂检测方法

针对现有输送带纵向撕裂检测方法存在敏感度低、操作不便、非可视化等问题,提出了一种基于Hough变换的输送带纵向撕裂检测方法。该方法使用线光源辅助照射,利用CCD实时采集输送带下表面图像,通过Hough变换提取图像斜率差特征来判断输送带是否发生纵向撕裂。测试结果验证了该方法的有效性。     

传输线定向耦合器的设计与实现

该文介绍了传输线定向耦合器的基本原理。通过计算得出了初始设计参数,运用仿真软件ADS,建立了耦合器的仿真模型。通过仿真得出了器件的初始曲线。在对模型进行了优化设计后,得出了符合设计要求的的最优耦合器模型,并对此模型进行了性能分析、产品加工及性能测试。测试结果表明,采用该方法研制的产品具有性能良好、结构简单、便于集成等特点。     

一种微带功分器的设计分析

该文介绍了微带wilkinson型等分功分器的基本原理,通过计算得出了初始设计参数,运用电磁仿真软件HFSS,建立了仿真模型.在对模型进行了优化设计后,得出了符合设计要求的功分器的最优模型,并对此模型进行了性能分析、产品加工及性能测试.测试结果表明,采用该方法研制的产品具有性能良好,结构简单、便于集成等特点.     

变电站照明智能控制系统节能优化设计

变电站照明系统的工作状态直接影响整体变电站工作效率,但传统设计的照明系统控制能力不佳,导致系统出现过度浪费的现象,因此,基于智能控制技术,以节能为目标,优化设计变电站照明系统。在硬件设计上,用双向DC-DC变换器替换传统系统中的单向DC-DC变换器,同时优化设计串口通信模块。在软件设计上,计算变电站照明用电效用,构建节能照明模型,通过智能控制操作,实现对系统软件的节能优化设计。试验共设置了3个测试组,每组的负载分别为50 W、100 W和150 W。测试结果表明,系统在不同升压需求下,其占空比测试结果符合预期,实现了节能优化的设计要求。     

钢板表面缺陷检测光学系统的设计

为满足钢板表面缺陷在线检测系统宽幅面、高速、高分辨率的检测要求,优化设计了钢板表面缺陷视觉检测系统的光学部分.采用了一种新型LED线光源获得高强度均匀照明,多线阵CCD拼接成像完成幅面分割.明、暗域相结合的成像模式确保了大部分缺陷的有效检出.综合考虑光源、镜头与线阵CCD的影响,计算并优化选取了光学镜头的焦距、f数和视场角等参数以满足检测需要,整个光学系统设计满足在线检测需要并在样机中得以应用.     

基于逆向工程技术的汽车壳体三维扫描系统优化设计

提出基于逆向工程技术的汽车壳体三维扫描系统优化设计,以解决传统三维扫描系统存在的数据捕捉效率低、扫描速率低、壳体扫描精度低等问题。依据汽车壳体三维扫描系统硬件整体结构,对硬件部分的激光线光源、摄像机、数据捕捉卡等设备进行改进设计;优化软件部分的壳体扫描点自校准过程和汽车壳体曲面三维扫描过程,实现基于逆向工程技术的汽车壳体三维扫描系统优化设计。实验结果表明,该系统数据捕捉效率高、扫描速度快、壳体曲面扫描精度高。     

Exact cost minimization of a series-parallel reliable system with multiple component choices using an algebraic method

The redundancy allocation problem is formulated minimizing the design cost for a series-parallel system with multiple component choices while ensuring a given system reliability level. The obtained model is a nonlinear integer programming problem with a nonlinear, nonseparable constraint. We propose a method based on the construction of a test set of an integer linear problem, which allows us to obtain an exact solution of the problem. It is compared to other approaches in the literature and standard nonlinear solvers.     

Convex output feedback model predictive control for mitigation of COVID-19 pandemic

In this paper, a model predictive control approach is proposed for epidemic mitigation. The disease spreading dynamics is described by an 8-compartment smooth nonlinear model of the COVID-19 pandemic in Hungary known from the literature, where the manipulable control input is the stringency of the introduced non-pharmaceutical measures. It is assumed that only the number of hospitalized people is measured on-line, and the other state variables are computed using a state observer which is based on the dynamic inversion of a linear sub-system of the model. The objective function contains a measure of the direct harmful consequences of the restrictions, and the constraints refer to input bounds and to the capacity of the healthcare system. By exploiting the special properties of the model, the nonlinear optimization problem required by the control design is reformulated to convex tasks, allowing a computationally efficient solution. Two approaches are proposed: the first finds a suboptimal solution by geometric programming, while the second one further simplifies the problem and transforms it to a linear programming task. Simulations show that both suboptimal solutions fulfill the design specifications even in the presence of parameter uncertainties.     

Matching swimmers to events in a championship swimming meet

This paper presents a zero-one mathematical programming model for determining which swimmers to assign to different events in a championship swimming meet. Since the model includes nonlinear constraints, it is demonstrated how some ad hoc linear constraints may be created as surrogates for the nonlinear constraints so that a zero-one linear programming code may be used to solve the problem.     

Real-time design of an adaptive nonlinear predictive controller

Based on real-time identification and using the concept of NARX (Nonlinear AutoRegressive with exogenous inputs) models, a new adaptive nonlinear predictive controller (ANPC) design is proposed. NARX models represent a natural way to describe the input-output relationship of severely nonlinear systems. From an initial batch of input-output data, a parsimonious NARX model is obtained using the Modified Gram-Schmidt (MGS) orthogonalization algorithm. Following this initial off-line identification and model reduction procedure, the control loop is closed. The ANPC directly uses the obtained structure and initial parameter estimates, which are updated each time step using recursive identification. The controller is designed similar to a typical linear predictive controller based on solving a nonlinear programming (NLP) problem. This paper shows how to solve this NLP problem on-line without the knowledge of the NARX model structure. The design is given for the multi-input multi-output (MIMO) case.     

Hierarchical stochastic production planning for the highest business benefit

This paper addresses the hierarchical stochastic production planning (HSPP) problem of flexible automated workshops (FAWs), each with a number of flexible manufacturing systems (FMSs) the part-transfer between which is a delay of a time period. The problem not only includes uncertainties in the demand, capacities, material supply, processing times, necessity for rework, and scrap, but also considers multiple products and multiple time periods. The objective is to develop a production plan which tells each FMS how many parts to produce and when to produce them so as to obtain the highest business benefit. Herein, the HSPP problem is formulated by a stochastic nonlinear programming model whose constraints are linear but whose objective function is piecewise linear. For the convenience of solving the stochastic nonlinear programming model above, it is approximately transformed into a deterministic nonlinear programming model and further into a linear programming model. Because the scale of the model for a general workshop is too large to be solved by the simplex method on a personal computer within acceptable time, Karmarkar's algorithm and an interaction/prediction algorithm, respectively, are used to solve the model, the former for the medium or small scale problems and the latter for the large scale problems. By the implementation of the above-mentioned algorithms and through many HSPP examples, Karmarkar's algorithm, the interaction/prediction algorithm and the linear programming method in Matlab 5.0 are compared, the result of which shows that the proposed approaches are very effective and suitable for not only “push” production but also “pull” production.     

分片线性规划及应用

提出通过分片线性逼近和分片线性规划,将非线性优化问题转化为一系列的线性规划进行求解的方法。讨论了分片线性规划的性质,证明了分片线性规划问题可以通过有限次线性规划得到求解,同时,给出了分片线性规划问题局部最优解的充要条件,并基于此构造了求解分片线性规划问题的下降算法。该算法与自适应链接超平面模型相结合,成功地对离心式冷水机组的工作点进行了优化。通过优化,机组的能耗比之当前工作点有了明显的下降,表明通过分片线性规划求解非线性优化问题的有效性。     

Optimization of nonlinear trusses using a displacement-based approach

A displacement-based optimization strategy is extended to the design of truss structures with geometric and material nonlinear responses. Unlike the traditional optimization approach that uses iterative finite element analyses to determine the structural response as the sizing variables are varied by the optimizer, the proposed method searches for an optimal solution by using the displacement degrees of freedom as design variables. Hence, the method is composed of two levels: an outer level problem where the optimal displacement field is searched using general nonlinear programming algorithms, and an inner problem where a set of optimal cross-sectional dimensions are computed for a given displacement field. For truss structures, the inner problem is a linear programming problem in terms of the sizing variables regardless of the nature of the governing equilibrium equations, which can be linear or nonlinear in displacements. The method has been applied to three test examples, which include material and geometric nonlinearities, for which it appears to be efficient and robust. Received December 4, 2000     

Nonlinear MPC for a Sensorless Multi‐Vectored Propeller Airship Based on Sliding Mode Observer with Saturation

A nonlinear fault tolerant station keeping controller for a multi‐vectored propeller airship without velocity and angular velocity sensors is developed, which is composed of three modules: nonlinear model predictive controller (NMPC), sliding mode observer (SMO), and linear programming (LP) based control allocation. The kinematics and dynamics model of the airship are introduced. Based on the nonlinear model, with the assumption that the velocity and angular velocity sensors are damaged, a sliding mode observer is designed to estimate the velocity and angular velocity of the airship. To achieve good performance in the station keeping mission, an explicit nonlinear model predictive control is derived. A linear programming base control allocation method is proposed to solve both amplitude and rate constraint of the propulsion forces and deflection angles. Stability analysis is carried out to prove that the system can be stabilized in finite time. Simulation results for the station keeping control are illustrated to prove the effectiveness of the proposed method.     

柔性自动化车间生产计划的最优分解

研究了柔性自动化车间生产计划的最优分解问题,首先根据实际需要建立车间生产计划的非线性规划模型,然后为求解方便将其转化成线性规划模型,由于这种模型对于一般车间来讲其规模已经很大,很难在微机上用单纯形法求争,为此,提出分别用卡马卡算法和一种新的基于卡马卡算法的关联预测法来求解车间生产计划的最优分解问题,并编制了相应软件,最后通过算例研究,比较了卡马卡算法、基于卡马卡算法的关联预测法和Matlab中的线性规划法,结果表明所提方法是非常有效的。