Probabilistic tree-based representation for solving minimum cost integer flow problems with nonlinear non-convex cost functions

The minimum cost flow problem (MCFP) is the most generic variation of the network flow problem which aims to transfer a commodity throughout the network to satisfy demands. The problem size (in terms of the number of nodes and arcs) and the shape of the cost function are the most critical factors when considering MCFPs. Existing mathematical programming techniques often assume the cost functions to be linear or convex. Unfortunately, the linearity and convexity assumptions are too restrictive for modelling many real-world scenarios. In addition, many real-world MCFPs are large-scale, with networks having a large number of nodes and arcs. In this paper, we propose a probabilistic tree-based genetic algorithm (PTbGA) for solving large-scale minimum cost integer flow problems with nonlinear non-convex cost functions. We first compare this probabilistic tree-based representation scheme with the priority-based representation scheme, which is the most commonly-used representation for solving MCFPs. We then compare the performance of PTbGA with that of the priority-based genetic algorithm (PrGA), and two state-of-the-art mathematical solvers on a set of MCFP instances. Our experimental results demonstrate the superiority and efficiency of PTbGA in dealing with large-sized MCFPs, as compared to the PrGA method and the mathematical solvers.     

一种下肢运动意图识别算法性能实时测评系统* .txt

摘要：对腿部运动意图识别算法的实时性能进行综合可靠的评价是实现下肢假肢灵活稳定控制的前提。提出一种逐层分级的基于下肢运动意图识别算法的实时测评方法，对算法的可靠性、稳定性以及运动意图识别速度进行综合评价。利用开发的运动意图识别算法评测系统，对基于肌电信号源和机械信号源的两种运动意图识别算法进行了实时性能测试，发现肌电信号源的算法识别时间大于机械信号源算法，但是其算法稳定性优于机械信号源算法。进一步地，还利用该评测系统有效地区分出正常识别策略与异常识别策略，发现正常策略的动作识别稳定系数比异常策略高25%左右。因此，所提的基于下肢运动意图识别算法的实时测评方法，能够对不同信号源算法以及不同识别策略的实时性能进行有效评价，为智能下肢假肢控制系统开发提供可供参考的测试平台。 .txt     

基于最小包容区域的球度误差评定方法

针对球度误差评定方法存在原理误差或模型误差,提出了一种符合最小包容区域定义的球度误差评定方法,即将几何搜索逼近算法与基于最小包容区域法的球度误差评定的几何结构和定义结合起来的准确评定方法。对仿真数据和其他文献中的数据进行了评定。所提方法与其他方法的评定结果表明,所提方法可以准确地找到最小包容区域球的球心并给出球度误差的精确解。     

改进ILoG算子的故障检测方法

针对强背景噪声干扰下轮对轴承故障特征微弱、难以准确检测的问题,提出了一种自适应改进高斯拉普拉斯(improved Laplacian of Gaussian,简称ILoG)算子的微弱故障检测方法。ILoG算子滤波器具有优良的信号突变特征检测能力,将其用于轮对轴承故障信号的冲击特征检测,同时利用水循环算法(water cycle algorithm,简称WCA)的寻优特性,并行搜寻筛选最佳的ILoG算子影响参数,通过对参数优化后ILoG算子滤波后信号做进一步包络解调分析,提取出轮对轴承微弱的故障特征信息。对实际轮对轴承外圈和内圈故障信号分析的结果表明,该方法可以有效检测出轴承微弱故障特征频率,故障检测效果优于小波阈值和多尺度形态学差值滤波方法。     

基于差分进化算法的手眼标定方法

针对眼在手机器人视觉系统的手眼标定问题，提出了一种基于差分进化算法的手眼标定方法。首先建立了眼在手机器人视觉系统手眼标定的数学模型，通过李群李代数理论，将手眼标定问题转化为误差函数的优化问题，同时保证了优化问题的最优解落在特殊欧氏群ＳＥ（３）上。通过差分进化算法对优化问题进行求解，避免了求解过程中的局部最优问题。最后在实物平台上进行了手眼标定实验，实验结果验证了算法的可行性和有效性。     

Mechanical,surface morphological and multi-objective optimization of tribological properties of V2O5 doped lead calcium titanate borosilicate glass ceramics

In the 55[(Pb_xCa_1−x).OTiO₂]-44[2SiO₂.B₂O₃]-1V₂O₅ system, various experimental studies were performed to obtain the mechanical and tribological properties of synthesized glass ceramics (GCs). The surface morphological study and elemental analysis were carried out using scanning electron microscope (SEM) followed by EDAX. The tribological measurements were also performed; based on Taguchi's L₂₅ orthogonal array, considering different GC compositions, sliding speeds and loads. The signal to noise (S/N) ratio was used to identify the influencing parameters on minimizing wear coefficient (k) and coefficient of friction (COF). To improve the tribological properties, the modeling of output responses was performed using response surface methodology (RSM) that was being used for multi-objective optimization using a Pareto optimality approach i.e. Genetic Algorithm (GA). The experimentally achieved k and COF were compared with the earlier work that carried out by the authors for germanium (Ge) doped GC samples. It is observed that the various mechanical properties (Vickers hardness, 29 GPa, Young's modulus, 1808 MPa, and compressive strength, 221 MPa) improved significantly in comparison to the Ge doped GC samples. The improved mechanical and tribological properties of the synthesized GC can be used as a coating material for the structural alloys and machine tool slides in view of its effective wear and corrosion resistance.     

基于ＰＩＤ控制算法的直流微网容错控制方法研究

构建直流微网容错控制对象模型，调节直流微电网的输出回路参数；以输出功率、直流微网的 参考电压、弱电网下系统惯性响应特征等为约束参量，构建直流微网容错控制目标函数，在不同电网强度下 进行直流微网容错控制的参数自整定性调节，采用无功环比例积分控制方法进行直流微网容错寻优分析， 建立模糊 ＰＩＤ控制模型，采用变结构的模糊 ＰＩＤ控制方法进行直流微网容错控制过程中的自适应加权学习 和误差反馈调节，实现直流微网容错控制改进设计。仿真结果表明，采用该方法进行直流微网控制的容错 性能较好，输出稳定性较强，具有较好的直流微网输出增益。     

基于VMD瞬时能量与GA- RBF的滚动轴承故障诊断

针对滚动轴承早期故障的有效识别,提出了一种基于VMD瞬时能量与GA优化的RBF神经网络的滚动轴承故障诊断方法,可以有效对滚动故障做出诊断。首先,VMD将滚动轴承振动信号进行分解成合适数目的本证模态函数;其次,计算本证模态函数分量的瞬时能量并组成特征向量;最后,将特征向量输入到GA优化的RBF神经网络实现轴承故障识别。通过滚动轴承故障诊断实验对该方法进行验证。结果表明,该方法识别滚动轴承故障的准确率为96.43%,较默认参数的RBF神经网络和EEMD瞬时能量与GA-RBF神经网络有明显的提高,证明了所提方法的可行性。     

Using hybridized ANN-GA prediction method for DOE performed drying experiments

Abstract

Coal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2.     

Optimal filtering and control for wireless networked closed-loop control systems

This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.