基于交流比例积分控制器的单相逆变器

提出一种电感电流、输出电压瞬时值反馈控制和负载电流前馈控制相结合的复合控制策略。在此基础上,研制了一台800W的单相逆变器。实验结果表明,提出的控制策略改善了逆变器输出电压的波形,提高了系统的暂态特性。     

连续体结构的参数化水平集统一优化

针对传统分步式结构优化设计的不足,提出一种同时进行结构拓扑、形状和尺寸统一优化的设计方法.首先采用水平集函数描述统一的结构优化模型和几何尺寸边界,通过引入紧支径向插值基函数将结构拓扑优化变量、形状优化变量和尺寸优化变量变换为基函数的扩展系数;然后取该扩展系数为设计变量,借助一种参数的变化表达3种优化要素对结构性能的影响,将复杂的多变量优化问题变换为相对简单的参数优化问题,有利于与相对成熟的优化算法相结合提高求解效率;进一步用R函数将其融合为一个整体,构造出统一优化模型,并用最优化准则法进行求解.最后通过数值案例证明了该方法的有效性和精确性.     

基于计算的工业喷涂机器人基本参数设计方法

设计与确定机器人参数需要在各项性能之间进行折衷,常常是一个耗时的迭代过程。本文提出了一种工业喷涂机器人基本参数确定的一般方法。通过喷涂机器人的设计与开发过程,基于机器人建模方法,归纳出机器人基本参数对各项性能尤其是工作空间特性的影响因素与规律,并在此过程中上分析了其结构参数对机器人工作空间和性能的影响,从而得出了机器人工作空间特性与结构参数之间的内在联系,为工业机器人的设计提供依据。     

基于BS的人才管理系统的设计与实现

目前计算机、网络技术的发展和应用日益普及,大力发展人才开发事业是当前任何企事业单位的迫切需求,使得目前人才库建设的任务日益繁重,借助于计算机网络实现人才库系统建设是一个重要的发展趋势。本文根据地方政府管理部门对人才资源的管理需求,分析了建设人才资源数据库系统的功能要求与需求特点,并根据系统的特点,分析了人才资源数据库的实体一关系,对于重要功能模块进行了设计与测试。     

基于呼叫中心的客户关系管理系统服务流程优化研究

针对钢铁企业物流公司客户关系管理现状,及其客户服务请求处理流程中的不足,提出了基于呼叫中心的钢铁企业最优化客服请求处理流程及其技术实现,给出了平台功能设计,探讨了平台实现的关键技术。该系统充分满足客户满意度以及服务请求处理的及时性,快速性,确切性,可操作性等方面要求。     

MICP技术改性膨胀土试验研究

为了探究膨胀土新型改良技术,引进微生物诱导碳酸钙沉淀(MICP)新型环保的土体加固技术,利用巴氏芽孢八叠球菌开展MICP压力灌浆改性膨胀土室内试验,研究了胶结液浓度对膨胀土力学性能、微观结构及水理特性的影响,揭示了MICP技术改性膨胀土的作用机理,评价了MICP压力灌浆改性膨胀土试验的效果。试验结果表明:灌浆处理后试样的无侧限抗压强度最大增加了379.4%,土体刚度也有所提高;试样的膨胀性、渗透性均有明显改善,膨胀力最高可减少25.3 kPa,渗透系数最大减少3个数量级;试验改性效果随着胶结液浓度的增大而提高,但当胶结液浓度增大到一定程度后,改性效果不再显著。研究成果验证了MICP技术用于改性膨胀土的有效性和可行性。     

A multi-objective Markov Chain Monte Carlo cellular automata model: Simulating multi-density urban expansion in NYC

Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models. In this article, we introduce a novel multi-chain multi-objective MCMC (mc-MO-MCMC) CA model to simulate LUCC. Unlike the classical MCMC, the proposed mc-MO-MCMC is a multiple chains method that imports crossover operation from classical evolutionary optimization algorithms. In each new chain, after the initial one, the crossover operator generates the initial solution. The selection of solutions to be crossed over are made according to their fitness score. In this paper, we chose the example of New York City (USA) to apply our model to simulate three conflicting objectives of changes from non-urban to low-, medium- or high-density urban between 2001 and 2016 using USA National Land Cover Database (NLCD). Elevation, slope, Euclidean distance to highways and local roads, population volume and average household income are used as LUCC causative factors. Furthermore, to demonstrate the efficiency of our proposed model, we compare it with the multi-objective genetic algorithm (MO-GA) and standard single-chain multi-objective MCMC (sc-MO-MCMC). Our results demonstrate that mc-MO-MCMC produces accurate simulations of land use dynamics featured by faster convergence to the Pareto frontier comparing to MO-GA and sc-MO-MCMC. The proposed multi-objective cellular automata model should efficiently help to simulate a trade-off among multiple and, possibly, conflicting land use change dynamics at once.     

A two-stage adaptive multi-fidelity surrogate model-assisted multi-objective genetic algorithm for computationally expensive problems

Surrogate model-assisted multi-objective genetic algorithms (MOGA) show great potential in solving engineering design problems since they can save computational cost by reducing the calls of expensive simulations. In this paper, a two-stage adaptive multi-fidelity surrogate (MFS) model-assisted MOGA (AMFS-MOGA) is developed to further relieve their computational burden. In the warm-up stage, a preliminary Pareto frontier is obtained relying only on the data from the low-fidelity (LF) model. In the second stage, an initial MFS model is constructed based on the data from both LF and high-fidelity (HF) models at the samples, which are selected from the preliminary Pareto set according to the crowding distance in the objective space. Then the fitness values of individuals are evaluated using the MFS model, which is adaptively updated according to two developed strategies, an individual-based updating strategy and a generation-based updating strategy. The former considers the prediction uncertainty from the MFS model, while the latter takes the discrete degree of the population into consideration. The effectiveness and merits of the proposed AMFS-MOGA approach are illustrated using three benchmark tests and the design optimization of a stiffened cylindrical shell. The comparisons between the proposed AMFS-MOGA approach and some existing approaches considering the quality of the obtained Pareto frontiers and computational efficiency are made. The results show that the proposed AMFS-MOGA method can obtain Pareto frontiers comparable to that obtained by the MOGA with HF model, while significantly reducing the number of evaluations of the expensive HF model.

     

融合显著性与运动信息的相关滤波跟踪算法

主流的目标跟踪算法以矩形模板的形式建立被跟踪物体的视觉表征, 无法有效区分目标与背景像素, 在背景复杂、目标非刚体形变、复杂运动等挑战性因素影响下容易出现模型偏移的问题, 导致跟踪失败. 与此同时, 像素级的显著性信息与运动先验信息作为人类视觉系统有效区分目标与背景、识别运动物体的重要信号, 并没有在主流目标跟踪算法中得到有效的集成利用. 针对上述问题, 提出目标的像素级概率性表征模型, 并且建立与之对应的像素级目标概率推断方法, 能够有效利用像素级的显著性与运动观测信息, 实现与主流的相关滤波跟踪算法的融合; 提出基于显著性的观测模型, 通过背景先验与提出的背景距离模型, 能够在背景复杂的情况下得到高辨识度的像素级图像观测; 利用目标与相机运动的连续性来计算目标和背景的运动模式, 并以此为基础建立基于运动估计的图像观测模型. 实验结果表明, 提出的目标表征模型与融合方法能够有效集成上述像素级图像观测信息, 提出的跟踪方法总体跟踪精度优于多种当下最先进的跟踪器, 对跟踪场景中的背景复杂、目标形变、平面内旋转等挑战性因素具有更好的鲁棒性.     

A robot hand-eye calibration method of line laser sensor based on 3D reconstruction

In the robotic eye-in-hand measurement system, a hand-eye calibration method is essential. From the perspective of 3D reconstruction, this paper first analyzes the influence of the line laser sensor hand-eye calibration error on the 3D reconstructed point clouds error. Based on this, considering the influence of line laser sensor measurement errors and the need for high efficiency and convenience in robotic manufacturing systems, this paper proposes a 3D reconstruction-based robot line laser hand-eye calibration method. In this method, combined with the point cloud registration technique, the newly defined error-index more intuitively reflects the calibration result than traditional methods. To raise the performance of the calibration algorithm, a Particle Swarm Optimization - Gaussian Process (PSO-GP) method is adopted to improve the efficiency of the calibration. The experiments show that the Root Mean Square Error (RMSE) of the reconstructed point cloud can reach 0.1256 mm when using the proposed method, and the reprojection error is superior to those using traditional hand-eye calibration methods.