直流系统后台监控组态软件的设计与实现

本文详细介绍后台监控组态软件的设计与实现 ,着重阐述了软件的组成、功能以及软件的两个重要数据结构的设计 ,即实时数据的类库和组件类库的设计。最后 ,介绍了用图形界面丰富的C++Builder3.0在 Win95平台上实现该软件的编程。

监控组态软件在夹层玻璃生产中的应用

通过监控组态软件在夹层玻璃生产中的运用,可直观监控生产关键环节,调阅监控保存数据,对残次品产生机理进行分析,从而指导生产,保持产品一致性、提高产品品质。

基于物联网技术的焊接电源群组化监控系统

船舶焊接生产过程中存在焊接质量监控困难、设备利用率低且能源浪费严重的问题,为了方便焊接质量监控与追溯,提高设备利用率以及节约能源,使用ZigBee物联网无线通讯技术以及Labview软件开发了焊接电源群组化监控系统.重点介绍了整个系统的设计,包括下位机部分电路、节点程序开发、ZigBee网络结构以及上位机界面设计.考虑到车间环境复杂性,采用了网状网络的ZigBee网络结构,系统测试表明,整个系统运行稳定,能够对焊接电源进行实时监控.

天然气压缩机组参数监控系统的改造研究

针对天然气压缩机组参数监控系统落后、自动化程度低,重要参数的监控分散在整个机组的各个部位并且是指针式显示,机组的参数异常时无报警功能的现象,开发出了能够实现机组参数的集中数字化显示,并对异常参数进行报警和故障处理监控的系统。该系统的应用可以有效避免现场机组停机、站内憋压及放空等恶性事件。该系统体积小、成本低,可以应用到具有压缩机组的所有站点,推广性强。

地铁自动化集成系统车站群组监控模式探讨

借鉴深圳地铁4号线自动化集成系统车站群组监控的运营经验,对比传统的车站监控模式,介绍了车站群组监控模式的硬件架构和软件架构。车站群组监控模式把线路车站划分成若干个群组,每个群组由1个轴心站和若干卫星站组成。在实际的应用过程中,车站群组监控模式因其所具有的节省设备投资和降低维护成本的优点,成为目前国内地铁新建项目的选择方案之一。

分布式组培室环境监控系统的设计与实现

该文设计和实现了一种基于CAN和RS485总线的分布式组培室环境监控系统,给出了CAN和RS485总线的电路原理图和通信协议设计方案。在STM32F103RBT6微处理器上移植了μC/OS-II,并规划了相关任务和中断服务。该系统具有高可靠性和易于扩展的特点,能够满足组培室的日常实验和生产需求。

基于IP组播与MPEG-4的数字视频监控系统在电力系统中的应用

本文介绍了图像视频监控系统的发展过程,分析了基于IP组播与MPEG-4视频编码的网络视频监控系统的特点和优势,并给出了应用于电力系统安全生产监控的一个实例。实际应用表明,基于MPEG-4标准和IP组播技术的系统具有更高的压缩率、更高的网络带宽利用率等特点。

深圳地铁三号线综合监控系统的特点

简单介绍深圳地铁三号线综合监控项目的内容,综合监控系统的意义,系统中群组监控、后备线控车站、集中维护的管理平台、分布式软件架构、模块化软件设计、联动等多种控制方式和多系统信息共享等特点,并根据项目特点及运营信息化需求,简述综合监控系统的发展趋势。

Sea water temperature auto-monitoring system based on general packet radio service (GPRS)

This paper introduces the design and implementation of sea-water temperature auto-monitoring system based on General Packet Radio Service （GPRS）. This system integrates modern wireless communication technology, the signal gathering technology and computer network technology. MSC1210 microcontroller is used in data collection device in order to make system accurate and fast. In addition, wireless and Internet technologies are used for transferring and displaying collected field data. A prototype system has been completed and tested in field trials. The results proved the feasibility and usefulness of this system for monitoring the temperature. By using this system, a lot of resources and money can be saved.

力控科技:着眼用户需求 迎接发展机遇

北京力控元通科技有限公司(以下简称力控科技),基于自身多年来的技术积累,坚持以用户需求为着眼点,以市场需求为导向,始终有预见性地开发具有潜在应用价值的新产品,不断地创新和丰富产品线,为用户提供全方位的行业解决方案和服务,推动中国自动化软件行业整体水平不断提高。

PMI项目管理知识应用

简要分析PMI项目管理知识体系及国内项目管理存在的不足,讨论如何提供国内公司的项目管理水平。希望通过文章的分析和探讨,能够为相关人士提供一定的参考和借鉴。

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

Seismic signal recognition using improved BP neural network and combined feature extraction method

Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.

High-Speed EMU TCMS Design and LCC Technology Research

This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS） software development platform, the TCMS testing and verification bench, the EMU driving simulation platform, and the EMU remote data transmittal and maintenance platform. All these platforms and benches combined together make up the EMU life cycle cost （LCC） system. Each platform facilitates EMU LCC management and is an important part of the system.

Effective Learner-Lecturer Interaction Working With a Virtual Learning Environment

Using the Internet to learn a language creates wide opportunities to enhance learning （Association of teachers of English in Catalonia （APAC）, 2010）. The Internet activities promote learners＇ self-monitoring ability, encourage the use of multimedia and network technology, and develop students＇ cooperation and participation. During the latest years, there have been many changes in education as these new technologies, including VLEs （Virtual Learning Environments）, which have become an important part in the teaching/learning process. According to Tech Terms Computer Dictionary （2012）, VLE is a virtual classroom where teachers and students communicate. VLEs have evolved as at an early stage, they were only ways of transmitting information： Teachers uploaded the multimedia resources and students read this information. At a higher stage, VLEs have become interactive. This means that students become active. We have designed a virtual environment where students, weekly, must contribute their opinions and comments in response to a required activity uploaded by the teacher. In this paper, we describe this weekly task and analyze students＇ opinion about this planned activity. The students become an active subject in this field. In this paper, we show how VLEs are no longer a means of transmitting information but a means of interaction as well as a way of motivating our students to be involved in their learning process

大规模组播网络故障定位的模型及算法

为了解决在大规模组播网络上进行有效的故障定位中手工故障定位效率低的问题,根据组播网络拓扑特点提出了基于图论的网络可达性故障定位问题的数学模型。在此基础上,提出两种故障定位算法,即基于经验的路径加权法和基于图论的连通图算法。算法可以有效地在大规模组播网络上进行自动故障定位,提高了故障定位的效率。基于中国教育和科研计算机网(Ch ina education and researchnetw ork,CERNET)组播网络拓扑结构的数据模拟验证了算法的有效性。

MONITORING OF METACOGNITIVE STRATEGY TRAINING

The goal of education is to cultivate learner autonomy. Studies in metacognition suggest that the ideal way to accomplish this is through metacognitive strategy training. Research also indicates that effective training programs depend on monitoring. To cultivate self-regulation or learner autonomy, it is necessary to exert external monitoring at the beginning, and gradually reduce external supervision as the students become more autonomous. The present study argues for cooperative group learning and regular evaluation as the main monitoring strategies. A semester long study indicates that cooperative group learning, besides being valuable in cultivating social relationship, and fostering cooperation and learner autonomy, can also serve as a good monitoring strategy in metacognitive strategy training. Regular evaluation, when it is progress- oriented and learner-centered, and conducted at proper times, will also provide valuable information for students to check, evaluate and adjust their learning. Self-questioning sheets and learner portfolios are also useful techniques in monitoring students' learning.