基于Zigbee的连采工作面机械设备数据采集系统

针对现有连采工作面机械设备监测数据采用有线方式传输而存在的铺线困难、数据丢包率大等问题,提出了一种基于Zigbee的连采工作面机械设备数据采集系统;以连采机油泵电动机数据采集系统为例,介绍了该系统的组成结构及工作原理,并对该系统采集的油泵电动机运行电流进行了分析,从而推断出油泵电动机的运行状况。运行结果表明,该系统数据传输可靠,为分析连采工作面机械设备的运行状况提供了很好的技术手段。     

基于层次分析法的矿井“三机”设备选型研究

介绍了结构模型的一般原理和层次分析法在综采工作面"三机"配套选型中的应用,提出了综采工作面设备配套方案的决策模型,该模型将功耗最小作为准则层的最优先级。将层次分析法引入综采工作面设备的配套选型中,不但从根本上解决了"三机"设备耗电量大、浪费大、搭配不合理等问题,而且还提高了决策的合理性和科学性,达到了节约电能的目的。     

基于一体化智能型多媒体教室设备系统的选型

随着多媒体技术的不断发展,越来越多的新设备和新技术融入到多媒体教室的建设和管理中,因此,一体化智能型多媒体教室设备系统的选型在多媒体建设中起着关键的作用。多媒体教室不仅是现代教学的基础设施,更是评估各高职院校办学硬条件的必要指标。文章通过对我校一体化智能型多媒体教室设备系统选型的分析,探讨符合我校教学、管理需求的,实用、高效的一体化智能型多媒体教室设备系统的选型。     

露天煤矿拉铲设备选型的研究

我国露天煤矿的地质赋存条件比较适合采用拉铲倒堆开采工艺。文章对露天煤矿拉铲设备选型进行了研究,分析了露天煤矿拉铲设备选型的主要开采参数的确定原则,介绍了拉铲设备选型的基本步骤以及主要参数的计算方法,最后采用计算机模拟技术建立了拉铲设备选型的数学模型。     

基于辅助决策支持系统的凿井设备选型与布置系统的设计与应用

深立井施工中凿井设备的选型与布置是一项复杂的工作,设计时需要考虑的因素多,且计算量大。因此设计一套凿井工程设备选型与布置设计系统,具有较大的理论与实用价值。基于决策支持系统思想,对千米以上的深井凿井设备的选型与布置系统的理论设计与实际的应用研究。系统由对话子系统、数据子系统和模型子系统三部分组成,通过设计多层菜单方法控制各个部分,实现人机对话,同时采用插件式管理,并尽可能使用动态化设计,以提高系统的灵活性。系统可以实现凿井设备的选型、凿井设备的布置与优化、计算任务书和设备布置的平、剖面图的自动生成等功能,为凿井设备的选型与布置提供新的服务载体。最后结合千米深井的凿井设备选型与布置问题,对该凿井设备选型与布置系统的应用进行了说明。     

利用国产装备实现综采自动化的研究与实践

文章分析了自动化综采设备的发展现状,提出了成套自动化综采设备的适应性研究、国产成套自动化综采设备选型及配套技术研究及实施方案。文章重点研究了自动化综采工作面控制系统集成中的工作面集中控制系统和通信、控制一体化系统。该系统已在平煤集团六矿成功投入运行,运行状况良好,有效提高了综采工作面的机械化、自动化和安全管理水平,降低了职工劳动强度,提高了生产效率,实现了综采工作面无人跟机作业。     

液压支架选型设计系统

为了方便煤矿设计人员对液压支架进行选型设计,采用C#语言、ASP.NET技术、Visual Studio2013系统开发工具及SQL Server 2008数据库,开发了基于B/S模式的液压支架选型设计系统,介绍了系统总体设计及功能实现方案。采用该系统,用户输入设计参数即可得出选型结果,并能够对选型结果进行择优。以某煤矿液压支架选型设计实例验证了该系统的可行性。     

自动控制系统与设备选型

1前言对于每一个自动控制系统的课题开发者来说，设备选型是系统开发工作的重要内容之一，对课题开发的成败起着举足轻重的作用。我们结合几年来的课题开发工作经验，对自动控制系统及其设备选型进行分析和讨论。2计算机控制系统的种类自动控制系统已由传统的电气控制、常规仪器仪表控制进入到计算机控制。当然，一个完整的控制系统是由常规仪表和微机控制共同构成的。本文主要涉及计算机控制系统。对干不同的课题，其设备选型也各不一样。针对不同的控制对象及目标，可供我们选择的计算机控制系统大致有以下几种。2．1按系统资源和特点分类…     

电磁阀原理和设计选型原则及依据

电磁阀门是管道流体输送系统中应用最广泛最重要的执行机构和控制元件,控制系统中不可缺少的设备。本文介绍了电磁阀的工作原理,以及电磁阀的设计选型依据和选型原则。     

局域网选型

本文讨论了局域网系统组建中的选型问题。文中提出了局域网选型的原则、过程,并对选型的方法进行了探讨。     

Estimation of sample selection models with two selection mechanisms

This paper focuses on estimating sample selection models with two incidentally truncated outcomes and two corresponding selection mechanisms. The method of estimation is an extension of the Markov chain Monte Carlo (MCMC) sampling algorithm from Chib (2007) and Chib et al. (2009). Contrary to conventional data augmentation strategies when dealing with missing data, the proposed algorithm augments the posterior with only a small subset of the total missing data caused by sample selection. This results in improved convergence of the MCMC chain and decreased storage costs, while maintaining tractability in the sampling densities. The methods are applied to estimate the effects of residential density on vehicle miles traveled and vehicle holdings in California.     

Prototype selection for dynamic classifier and ensemble selection

In dynamic ensemble selection (DES) techniques, only the most competent classifiers, for the classification of a specific test sample, are selected to predict the sample’s class labels. The key in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers’ competence is usually estimated according to a given criterion, which is computed over the neighborhood of the test sample defined on the validation data, called the region of competence. A problem arises when there is a high degree of noise in the validation data, causing the samples belonging to the region of competence to not represent the query sample. In such cases, the dynamic selection technique might select the base classifier that overfitted the local region rather than the one with the best generalization performance. In this paper, we propose two modifications in order to improve the generalization performance of any DES technique. First, a prototype selection technique is applied over the validation data to reduce the amount of overlap between the classes, producing smoother decision borders. During generalization, a local adaptive K-Nearest Neighbor algorithm is used to minimize the influence of noisy samples in the region of competence. Thus, DES techniques can better estimate the classifiers’ competence. Experiments are conducted using 10 state-of-the-art DES techniques over 30 classification problems. The results demonstrate that the proposed scheme significantly improves the classification accuracy of dynamic selection techniques.

     

From dynamic classifier selection to dynamic ensemble selection

In handwritten pattern recognition, the multiple classifier system has been shown to be useful for improving recognition rates. One of the most important tasks in optimizing a multiple classifier system is to select a group of adequate classifiers, known as an Ensemble of Classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection performs no better than static selection. We propose four new dynamic selection schemes which explore the properties of the oracle concept. Our results suggest that the proposed schemes, using the majority voting rule for combining classifiers, perform better than the static selection method.     

QoS-aware web service selection with negative selection algorithm

Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.     

Fast orthogonal forward selection algorithm for feature subset selection

Feature selection is an important issue in pattern classification. In the presented study, we develop a fast orthogonal forward selection (FOFS) algorithm for feature subset selection. The FOFS algorithm employs an orthogonal transform to decompose correlations among candidate features, but it performs the orthogonal decomposition in an implicit way. Consequently, the fast algorithm demands less computational effort as compared with conventional orthogonal forward selection (OFS).     

Bayesian copula selection

In recent years, the use of copulas has grown extremely fast and with it, the need for a simple and reliable method to choose the right copula family. Existing methods pose numerous difficulties and none is entirely satisfactory. We propose a Bayesian method to select the most probable copula family among a given set. The copula parameters are treated as nuisance variables, and hence do not have to be estimated. Furthermore, by a parameterization of the copula density in terms of Kendall's τ, the prior on the parameter is replaced by a prior on τ, conceptually more meaningful. The prior on τ, common to all families in the set of tested copulas, serves as a basis for their comparison. Using simulated data sets, we study the reliability of the method and observe the following: (1) the frequency of successful identification approaches 100% as the sample size increases, (2) for weakly correlated variables, larger samples are necessary for reliable identification.     

Fuzzy feature selection

In fuzzy classifier systems the classification is obtained by a number of fuzzy If–Then rules including linguistic terms such as Low and High that fuzzify each feature. This paper presents a method by which a reduced linguistic (fuzzy) set of a labeled multi-dimensional data set can be identified automatically. After the projection of the original data set onto a fuzzy space, the optimal subset of fuzzy features is determined using conventional search techniques. The applicability of this method has been demonstrated by reducing the number of features used for the classification of four real-world data sets. This method can also be used to generate an initial rule set for a fuzzy neural network.     

Safeguards selection principles

The selection of effective safeguards is an important aspect of computer security that can be facilitated by the application of a set of 20 principles derived from computer security research and experience. These 20 principles contribute to a growing theory of computer security as the subject matures and agreement is reached on security concepts by practitioners in the field.     

Feature selection toolbox

A software package developed for the purpose of feature selection in statistical pattern recognition is presented. The software tool includes both several classical and new methods suitable for dimensionality reduction, classification and data representation. Examples of solved problems are given, as well as observations regarding the behavior of criterion functions.