A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

基于神经网络算法的井下裂缝诊断与堵漏技术

在钻井过程中，常常钻遇不同宽度的井下地层裂缝。钻遇裂缝时容易发生钻井液漏失现象，甚至发生钻井液失返现象，严重影响了安全、高效钻井。目前裂缝封堵的方法常存在封堵成功率不高、堵漏承压能力低的问题，其中一个重要的原因是对井下地层的裂缝宽度等特征认识不清。基于地层裂缝产生的岩石力学机理，确定影响裂缝宽度关键的6个力学和工程因素，并利用神经网络计算的非线性、大数据特点建立了井下地层裂缝宽度的分析模型，模型包含输入层、输出层和3个隐藏层。通过该模型诊断井下裂缝宽度，提高了计算精度，平均误差仅为2.09%，最大误差为5.88%，解决钻井现场仅凭经验判断裂缝误差较大和依靠成像测井成本较高的问题。同时根据神经网络模型诊断得到的裂缝宽度优化堵漏材料的粒径配比，提高了裂缝内的架桥封堵强度和架桥的稳定性，封堵层的承压能力达到12.8 MPa，反向承压能力达到4.5 MPa。现场堵漏试验最高憋压10 MPa，经过封堵作业后大排量循环不漏，达到了裂缝性地层高效堵漏的目的，堵漏一次成功。      

Cost-minimization analysis of a working vacation queue with N-policy and server breakdowns

This paper investigates the N-policy M/M/1 queueing system with working vacation and server breakdowns. As soon as the system becomes empty, the server begins a working vacation. The server works at a lower service rate rather than completely stopping service during a vacation period. The server may break down with different breakdown rates during the idle, working vacation, and normal busy periods. It is assumed that service times, vacation times, and repair times are all exponentially distributed. We analyze this queueing model as a quasi-birth–death process. Furthermore, the equilibrium condition of the system is derived for the steady state. Using the matrix-geometric method, we find the matrix-form expressions for the stationary probability distribution of the number of customers in the system and system performance measures. The expected cost function per unit time is constructed to determine the optimal values of the system decision variables, including the threshold N and mean service rates. We employ the particle swarm optimization algorithm to solve the optimization problem. Finally, numerical results are provided, and an application example is given to demonstrate the applicability of the queueing model.     

试油井选层压裂新工艺及现场试验

设计新型可洗井封隔器，保障油井有反洗井通道，有效防止封隔器以上砂埋所引发的管柱砂卡事故，并对新型封隔器、控制开关、密封接头、同心大通径压力计托筒等井下工具组合优化。解决了多层油井任意选层压裂、求产、测井温一体化的难题。     

泌阳凹陷复杂断块群油气分布控制因素

泌阳凹陷北部斜坡复杂断块群油田以断鼻、断块及断层+岩性油气藏为主,垂向含油层系多,平面上叠合连片,油气主要富集在鼻状构造主体部位的断鼻断块内。通过对研究区油气控制因素的分析,指出继承性的鼻状构造控制了油气聚集方向和富集程度,油气藏含油范围和油气藏高度则主要受断层封闭性和封闭程度的控制,油气分布规律受伸展构造所伴生的各种断块群构造型式的控制。     

离子注入中污染的产生及防护

本文概述了离子注入过程中污染产生的原因和防止污染的措施，特别强调了对微粒污染和金属污染的防护以满足ＵＬＳＩ加工对离子注入的要求。     

冷媒充填机的PLC控制

介绍了冷媒充填机控制系统的组成和工作原理，着重叙述了西门子S7－200可编程控制器与触摸屏结合的控制方式，使系统的性能得到较大的提高。     

Effect of vibration condition and inter-particle frictions on the packing of uniform spheres

We present a numerical study of the packing of uniform spheres under three-dimensional vibration using the discrete element method (DEM), focusing on the effects of vibration condition (amplitude and frequency) and inter-particle frictions (sliding and rolling frictions). The results are analysed in terms of packing density, coordination number (CN), radial distribution function (RDF) and pore structure. It is shown that increasing either the vibration amplitude or frequency causes packing density to increase initially to a maximum and then decrease. Both vibration frequency and amplitude should be considered to characterize the effect of vibration process on packing structure. The sliding and rolling frictions between particles can decrease packing density since they dissipate energy, although the effect of rolling friction is less significant. In line with the change of packing density, microstructural properties such as CN, RDF and pore distribution also change: a looser packing often corresponds to smaller CN, less peaked RDF and larger but more widely distributed pores.