低位放顶煤液压支架的改造

对ZFSB4 0 0 0 / 18.5 / 2 8、ZFG4 4 0 0 / 18.5 / 2 9型液压支架在新集一矿使用中存在的问题进行了分析 ,提出了改进措施。通过改造 ,解决了支架在顶板支护、后溜拉移中存在的问题 ,消除安全隐患 ,为设备检修创造条件。     

Simulation of a PCB assembly line: A modified JIT approach

In this paper, a simulation model of an existing printed circuit board (PCB) assembly line is developed in an attempt to identify strategies which tend to improve the system performance. The model is based on the concept of the JIT (Just-In-Time) production approach. Parameters investigated includes sequencing rules, lot sizes, number of Kanbans used, and number of PCB types.     

振兴三明地方水泥工业之我见

三明水泥行业经济效益连续两年大滑坡,造成滑坡的主要原因是价格太低,提出振兴的八点建议和三点教训。     

Automatically modeling, simulating and explaining physical systems

This paper presents an interactive framework for constructing models from high-level specifications of real physical systems, deriving low-level simulations from the constructed models and then generating explanations of the simulated behavior to help engineers interpret and control the real physical systems. To demonstrate our method, we show an example of modeling river networks, which are eco-environmental engineering systems, to provide a computer-based solution to problems of simulating and controlling concentrations of water quality constituents in river networks[1].     

基于灰度共生矩阵的图像纹理特征提取分析

图像纹理作为一种重要的视觉手段,是图像中普遍存在而又难以描述的特征。目前常用的纹理特征提取的方法主要有统计方法、模型方法、信号处理方法和结构方法。灰度共生矩阵即为灰度级的空间相关矩阵,以其为基础的统计方法通过对矩阵统计量的求取较好地提取到了纹理特征,通过选取关键参数编程并进行仿真实现,分别求取了四个方向的灰度共生矩阵及其特征量来分析图像的纹理特征。     

数据挖掘技术在学校管理中的应用

数据挖掘技术是一门多学科相互交叉融合而形成的新兴学科。目前,该技术已在商业、金融业、农业、互联网、医药业等多个领域中得到广泛应用。而将数据挖掘技术与学校管理相结合,可以从大量事务管理数据中提取出了隐藏在其中的有用信息,因此可以帮助教学人员合理安排教学工作,协助辅导员对学生的管理,从而促进教育体制的进一步完善与发展。本文由数据挖掘技术概述入手,论述了数据挖掘技术在学校管理中的作用,最后,将数据挖掘技术应用在学生成绩管理中,可以实现透过现象看本质,提炼有价值的信息。     

LDAP认证技术在油田生产系统中的设计与实现

根据采用单一用户登录应用系统的要求,提出一种基于LDAP的双重认证方式。以用户认证为背景,首先对LDAP基本原理及体系结构进行简单描述,然后设计并实现基于LDAP验证与本地验证相结合的双重认证方式。本文对认证方法的特点进行阐述,并成功应用于青海油田油气水井远程计量传输发布系统。     

SIMUWORKS仿真软件的应用研究

SIMUWORKS涵盖了系统优化设计与调试、过程仿真培训、故障诊断、复杂系统动态特性建模研究、专家系统等各个方向的应用,提供了一整套完备的全过程支撑、一体化、通用的应用环境,其根本是以微机环境为基础的应用平台。软件部分采用了面向对象的图形化建模方法、分布式实时数据库技术和动态内存机器码生成技术,在国内外仿真领域处于领先地位。     

Review of digital twin about concepts,technologies, and industrial applications

Various kinds of engineering software and digitalized equipment are widely applied through the lifecycle of industrial products. As a result, massive data of different types are being produced. However, these data are hysteretic and isolated from each other, leading to low efficiency and low utilization of these valuable data. Simulation based on theoretical and static model has been a conventional and powerful tool for the verification, validation, and optimization of a system in its early planning stage, but no attention is paid to the simulation application during system run-time. With the development of new-generation information and digitalization technologies, more data can be collected, and it is time to find a way for the deep application of all these data. As a result, the concept of digital twin has aroused much concern and is developing rapidly. Dispute and discussions around concepts, paradigms, frameworks, applications, and technologies of digital twin are on the rise both in academic and industrial communities. After a complete search of several databases and careful selection according to the proposed criteria, 240 academic publications about digital twin are identified and classified. This paper conducts a comprehensive and in-depth review of these literatures to analyze digital twin from the perspective of concepts, technologies, and industrial applications. Research status, evolution of the concept, key enabling technologies of three aspects, and fifteen kinds of industrial applications in respective lifecycle phase are demonstrated in detail. Based on this, observations and future work recommendations for digital twin research are presented in the form of different lifecycle phases.     

Automated manufacturing system discovery and digital twin generation

The latest developments in industry involved the deployment of digital twins for both long and short term decision making, such as supply chain management, production planning and control. Modern production environments are frequently subject to disruptions and consequent modifications. As a result, the development of digital twins of manufacturing systems cannot rely solely on manual operations. Recent contributions proposed approaches to exploit data for the automated generation of the models. However, the resulting representations can be excessively accurate and may also describe activities that are not significant for estimating the system performance. Generating models with an appropriate level of detail can avoid useless efforts and long computation times, while allowing for easier understanding and re-usability. This paper proposes a method to automatically discover manufacturing systems and generate adequate digital twins. The relevant characteristics of a production system are automatically retrieved from data logs. The proposed method has been applied on two test cases and a real manufacturing line. The experimental results prove its effectiveness in generating digital models that can correctly estimate the system performance.