The Influence Factors of Collective Intelligence Emergence in Knowledge Communities Based on Social Network Analysis

The advent of the age of Information shifts the environment we live in from off-line to on-line. The prospect of Collective Intelligence (CI) is promising. Based on this background, the aim of this paper is to discover the emergence mechanism and influence factors of CI in knowledge communities using the method of quantitative and qualitative analysis. On the basis of the previous research work, our model theorizes that the two dimensions of social network (i.e., interactive network structure and participant’s characteristics) affect two references of effectiveness (i.e. group knowledge production and participation of group decision). And this hypothetical model is validated with simulation data from “Zhihu” community. Our model has been useful since it offers some inspirations and directions to promote the level of CI in knowledge communities.

Intelligent monitoring method of cow ruminant behavior based on video analysis technology

To overcome the limitations of traditional dairy cow's rumination detection methods,a video-based analysis on the intelligent monitoring method of cow ruminant behavior was proposed in this study.The Mean Shift algorithm was used to track the jaw motion of dairy cows accurately.The centroid trajectory curve of the cow mouth motion was subsequently extracted from the video.In this way,the monitoring of the ruminant behavior of dairy cows was realized.To verify the accuracy of the method,six videos,a total of 99'00",24000 frames were selected.The test results demonstrated that the success rate of this method was 92.03%,despite the interference of behaviors,such as raising or turning of the cow’s head.The results demonstrate that this method,which monitors the ruminant behavior of dairy cows,is effective and feasible.

Advanced Data Collection and Analysis in Data‑Driven Manufacturing Process

The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control,rather than using simplified physical models and human expertise.In the era of data-driven manufacturing,the explosion of data amount revolutionized how data is collected and analyzed.This paper overviews the advance of technologies developed for in-process manufacturing data collection and analysis.It can be concluded that groundbreaking sensoring technology to facilitate direct measurement is one important leading trend for advanced data collection,due to the complexity and uncertainty during indirect measurement.On the other hand,physical model-based data analysis contains inevitable simplifications and sometimes ill-posed solutions due to the limited capacity of describing complex manufacturing process.Machine learning,especially deep learning approach has great potential for making better decisions to automate the process when fed with abundant data,while trending data-driven manufacturing approaches succeeded by using limited data to achieve similar or even better decisions.And these trends can demonstrated be by analyzing some typical applications of manufacturing process.

Development of community health service-oriented computer-assisted information system for diagnosis and treatment of respiratory diseases

Objective:Community health services are an emerging trend.We have found in practice that diagnosis and treatment of respiratory diseases in the community are distinct.The respiratory department’s daily work involves a number of outpatient registration items and a vast workload.The routine manual operation is inefficient and it is not convenient to make effective statistical analysis of the outpatient data to identify the risk factors closely related to diseases.Therefore,it is imperative to process the outpatient information of patients with respiratory diseases effectively and efficiently in a unified manner by means of computer technology.Methods:The design and realization of the Community Health Service-oriented computerassisted Information System for Diagnosis and Treatment of Respiratory Diseases(CHS-DTRD)was completed as part of the community intervention study on bronchial asthma that was carried out jointly by the Nanjing First Hospital Affiliated to Nanjing Medical University and the Hospital of Nanjing University of Science&Technology,and based on 2 years of experience and the needs of an overall analysis.Results:The computer-assisted information system for diagnosis and treatment was developed using Java Server Page(JSP)technology and introducing the advanced Asynchronous JavaScript XML(AJAX)technique and MS-SQL Server was used in the background database.CHS-DTRD was composed of eight functional modules(outpatient data maintenance,outpatient appointment,intelligent analysis for disease risk factors,query and statistics,data dictionary maintenance,database manipulation,access control,and system configuration).CHS-DTRD featured a friendly interface,convenient operation,and stability and reliability.Conclusion:Community health-oriented diagnosis and treatment of respiratory diseases is simple,programmable,and intuitive,thus the workload of physicians is significantly reduced and the work efficiency is improved.This system facilitates an intelligent analysis of disease risk factors using data mining technology,and provides physicians with suggestions on intelligent analysis for diagnosis of disease and conclusion of disease causes.

Vulnerabilities and integrity of precise point positioning for intelligent transport systems:overview and analysis

The implementation of Intelligent Transport System (ITS) technology is expected to significantly improve road safety and traffic efficiency. One of the key components of ITS is precise vehicle positioning. Positioning with decimetre to sub-metre accuracy is a fundamental capability for self-driving, and other automated applications. Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) is an attractive positioning approach for ITS due to its relatively low-cost and flexibility. However, GNSS PPP is vulnerable to several effects, especially those caused by the challenging urban environments, where the ITS technology is most likely needed. To meet the high integrity requirements of ITS applications, it is necessary to carefully analyse potential faults and failures of PPP and to study relevant integrity monitoring methods. In this paper an overview of vulnerabilities of GNSS PPP is presented to identify the faults that need to be monitored when developing PPP integrity monitoring methods. These vulnerabilities are categorised into different groups according to their impact and error sources to assist integrity fault analysis, which is demonstrated with Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) methods. The main vulnerabilities are discussed in detail, along with their causes, characteristics, impact on users, and related mitigation methods. In addition, research on integrity monitoring methods used for accounting for the threats and faults in PPP for ITS applications is briefly reviewed. Both system-level (network-end) and user-level (user-end) integrity monitoring approaches for PPP are briefly discussed, focusing on their development and the challenges in urban scenarios. Some open issues, on which further efforts should focus, are also identified.

铁路施工中的智能监测技术及其在高铁项目中的应用

随着现代铁路建设规模的不断扩大和技术要求的提高,智能监测技术在铁路施工中的应用日益广泛。文章探讨了智能监测技术的基本原理及其在高铁项目中的实际应用,通过案例分析展示了该技术在施工过程中的显著优势。通过传感器、图像与视频监控、大数据分析等多种技术手段,智能监测技术不仅提高了施工精度与效率,还显著提升了安全保障水平,降低了工程风险。文章结合多个高铁建设项目,详细分析了这些技术在实际施工中的具体实施及其产生的效果,并提出了未来在铁路建设领域智能监测技术的发展方向。

Application of Al techniques in monitoring and operation of power systems

In recent years,the artificial intelligence(Al)technology is becoming more and more popular in many areas due to its amazing performance.However,the application of Al techniques in power systems is still in its infancy.Therefore,in this paper,the application potentials of Al technologies in power systems will be discussed by mainly focusing on the power system operation and monitoring.For the power system operation,the problems,the demands,and the possible applications of Al techniques in control,optimization,and decision making problems are discussed.Subsequently,the fault detection and stability analysis problems in power system monitoring are studied.At the end of the paper,a case study to use the neural network(NN)for power flow analysis is provided as a simple example to demonstrate the viability of Al techniques in solving power system problems.

矿山生态环境定量遥感监测与智能分析系统设计与实现

矿山生态环境监测与治理是国家生态文明建设和“双碳”目标下的重中之重,其信息化、智能化建设在新一代信息技术革命的助推下成为数字中国建设的重要一环,也是当下时代发展的必然趋势。然而,现有矿山生态环境监测系统仍然停留在单一专题、要素不全、基础量测、本地管理的初级阶段,无法满足现实环境中对矿山生态环境多要素、长时序、高频次监测与分析的需求。基于此,提出一种B/S架构下的矿山生态环境定量遥感监测与智能分析系统——矿山生态天眼,并详细介绍了其研发需求、技术架构、关键技术及核心功能。系统依托卫星遥感技术及其他监测手段,获取并聚合不同来源、信息丰富的矿山生态大数据,形成矿山分布一张图和数据资源服务;进而基于定量遥感反演矿山生态环境各生态参数,形成一套长时序、多要素的矿山生态监测产品,涵盖人类活动、自然地理条件和“植−土−水−气”各生态要素;在此基础上,系统提供GIS时空分析、统计分析及综合定量评价等工具集,分别实现对矿区土地利用、归一化植被指数(NDVI)等参数伴随采矿活动在空间上的变化监测,对土壤含水量、水体悬浮物浓度等生态要素历史统计值在不同时空位置和区域下的查询与可视化,对顾及多项生态因子的矿山生态环境质量综合定量评价,并最终形成矿山生态扰动与治理监测报告。矿山生态天眼的应用将服务于矿山生态环境全过程变化监测、数据管理、智能分析和决策应用,有望提高矿山生态环境监测与治理的效率和智能化水平,为推动生态文明信息化建设提供参考方案。

基于巡检机器人的吊装作业场景DPIM算法

为提高吊车安全作业的高精度检测预警,增强企业安全管理能力,围绕工业安全事件分析与监测预警无人化的需求,定制吊装场景地面和空中飞行相结合的巡检机器人,智能化吊装过程安全规程监控、弹窗图像记录和安全告警。首先,制作包含3120张图片的吊装数据集Cranes-Dataset(CRN-Dataset),提出一种动态视角智能监测(DPIM)算法,以增强人-车-物多尺度目标的快速检测能力;然后,依据多帧图像的角点检测和带噪声基于密度的聚类方法,以及吊车与作业工人空间距离的安全属性,制定安全规则触发告警的流程,实时记录违规操作图像并弹窗预警。结果表明:经过实际部署和验证,DPIM算法相较于其他传统算法,吊装作业目标识别能力有明显提高,且适用嵌入式边缘智能分析节点的实时计算与数据传输,完成危险区域人员拒止的现场部署。

煤矿智能识别监测预警平台研究及应用

针对煤矿智能化建设中对少人化、无人化和标准化目标的诉求,依托先进的5G信息传输渠道,发展全方位远程监测、监控、视频预警技术是提高现场作业规范化和完善作业流程的关键。为了进一步提升煤矿生产的安全规范管理以及远程监测和监控,实现提前预警预报的目标,开发了煤矿智能识别监测预警平台。介绍了该平台的总体架构,详细阐述了该平台业务流程、智能视频模型以及智能视频识别设计。实际应用表明,该平台能够对现场违规行为进行有效监测、监控和预警,有效提高违章行为的精准辨识,能够明显减少因违规带来的损失,为矿井智能化建设提供有力支撑。

智能化矿井监控调度作业的认知作业分析及认知疲劳判定

智能化矿井单调的监控调度作业易造成作业人员认知疲劳,从而降低作业人员工作绩效,影响煤矿安全生产。阐述了监控调度作业任务中所涉及的信息感知、信息加工和信息输出3类主要认知过程,采用形式模型、进程追踪和非结构化访谈等认知作业分析方法对其进行解析。通过实验获取被试者的人体生理指标、主观评定参数和任务绩效参数来评估认知疲劳,采用SPSS 22.0统计软件对测试数据进行处理,探讨了被试者在不同时间段内认知疲劳程度的变化。通过计算机模拟智能矿井监控调度作业,采用双任务作业方式成功诱发了被试者的认知疲劳,KSS和SPC量表,以及任务绩效指标表明,智能化矿井作业人员在进行长时间的单调监控作业过程中会导致疲劳和嗜睡增加,工作绩效下降。反应时间和主观量表参数能够较好反映被试者认知疲劳状态的变化,而心率参数的表征效果并不明显。

城轨交通接触网安全在线监测系统

接触网是城轨交通供电系统的重要组成部分,为降低接触网故障对安全运营的影响,提出城轨交通接触网安全在线监测系统。通过计算机视觉、深度学习技术并研发相关硬件,对接触网设备导高值、拉出值、磨耗、悬挂部件、弓网燃弧及温度等状态实现在线自动检测,为检修运维提供数字化、可视化、智能化的技术手段,全面实现设备智能化巡视和设备状态感知。重点阐述监测系统总体架构、功能、关键技术,以及在天津地铁6号线的实际应用。应用效果表明:系统对缺陷检测精度高、对缺陷位置定位误差小,提升了接触网故障处理效率和应急响应速度,实现了接触网巡视、动态检测、静态检测、磨耗检测作业的少人化和无纸化,并逐渐实现接触网维护工作由计划修向状态修过渡。

现代足球智能监控技术前沿与应用

现代足球智能监控技术利用计算机视觉技术和数据分析方法对赛场上的事件和数据进行监控和分析,以实现球队战术的优化、训练计划的整合以及球员比赛表现的评估等目标。相对于其他监控技术,智能监控技术对球员技术动作的干扰较小,具备更强的环境适应性。在单一事件(如控球和传球)的细粒度识别任务中,智能监控技术表现出较优越的性能。在全局技战术分析和球员表现评估任务中,智能监控技术展现出精确的数据挖掘能力和直观的可视化分析能力。此外,足球智能监控技术中的裁判辅助模块有助于构建更加客观和准确的判罚机制,维护竞技公平、优化足球比赛的观赏体验、促进裁判水平的提高。

CTCS3-300H型列控车载设备状态在线监测与智能分析系统开发与应用

针对CTCS3-300H型列控车载设备(简称:列控车载设备)各单元运行日志数据分散存储、数据转储下载接口多、数据分析工作繁琐耗时的问题,根据列控车载设备结构特点,设计了列控车载设备状态在线监测及智能分析系统。该系统统一了列控车载设备各单元的日志数据采集与传输接口,借助既有的车地无线传输通道,设计了灵活、务实的车地数据传输方案,实现了用于列控车载设备状态在线监测的事件通告报文的实时传输,提供故障日志自动下载、手动按需下载两种日志数据无线下载方式,在满足列控车载设备状态在线监测和故障分析需要的前提下,最大限度地减少车地数据传输量。通过有效利用列控车载设备日志数据进行智能分析,能够快速、准确定位故障原因,实现故障自动报警并给出处置建议,提高了列控车载设备故障应急处置和维护效率,为动车组安全运行提供强有力的技术保障。

专利情报视角下的盾构机智能感知技术现状及发展趋势研究

盾构机是一种隧道掘进的专用工程机械,作为高端装备的代表,也是国之重器。随着大数据、人工智能的快速发展,盾构机智能化越来越成为研发的方向和重点。本文针对盾构机智能化关键技术之智能感知专利技术进行了宏观态势分析,并针对刀具监测这一核心技术进行深入分析,为掌握盾构机智能化发展态势,把握技术热点提供建议,为专利布局和相关研究提供新思路。

基于人工智能物联网的多维度手术设备管控平台的构建与应用评价

目的:构建基于人工智能物联网(AIoT)的多维度手术设备管控平台,助力提升医院手术室医疗设备精细化及智慧化管理水平。方法:通过融合物联网(IoT)、大数据分析、室内定位技术、人工智能(AI)技术等信息技术构建基于AIoT的多维度手术设备管控平台,实时采集腔镜、电刀等手术设备使用过程数据,打通与手术设备相关的信息系统的关系,包括医院信息系统(HIS)、实验室信息系统(LIS)、放射科信息系统(RIS)系统、手术麻醉信息系统(OAMS)等,为手术设备的效率分析、效益分析和资产管理提供技术支持。该平台架构分为数据提取层、数据引擎层和AI数据分析层3层,包括数据自动采集、数据深度融合、数据挖掘与分析和数据可视化展示4个功能模块。结果:平台于2022年6月在上海市中医医院上线应用,实现了手术室设备室内定位、一键盘点等日常管理智能化;建立了一套以IoT为基础、信息系统融合下的重点手术设备使用效率、成本效益自动统计的绩效分析方法,实现智慧化服务、智能化管理和数字化运营。结论:多维度手术设备管控平台的构建与应用提高了手术室医疗设备的工作效率,实现降本增效,助力医院手术设备精细化和智慧化管理,为医院管理者科学决策提供数据支撑。

语音智能控制技术在广播电视监测系统中的应用分析

为探讨语音智能控制技术在广播电视监测系统中的具体应用,首先介绍语音智能控制技术的基本概念及其在广播电视监测中的应用背景,阐述该技术的特性,详细描述广播电视监测系统的技术架构及关键组件。其次,论述语音智能控制技术的集成步骤和具体应用场景,强调该技术在提高监测效率和处理紧急情况能力方面的优势。最后,展望语音智能控制技术在广播电视监测系统中的发展趋势。

大数据背景下计算机信息技术在网络安全中的运用

随着互联网的迅猛发展和信息技术的日新月异,网络安全问题变得越来越突出和复杂化。恶意攻击、数据泄露、网络病毒等威胁不断涌现,对信息安全造成了严重的威胁。在这样的背景下,大数据技术的兴起为网络安全领域带来了新的机遇和挑战。首先介绍了大数据和网络安全的关系,其次详细阐述了大数据在网络安全中的应用领域,最后分析了大数据驱动的网络安全挑战和解决方案,并展望了未来的研究方向。

基于智能视频分析系统的智能监控系统构建

传统的视频监控系统使用效率低且人工查找录像繁琐,在当前海量的视频录像面前,这些问题更加突出,而基于人工智能技术的智能视频分析系统能够对监控画面进行告警分析。本研究通过将视频监控系统与智能视频分析系统结合,构建了一套智能监控系统,该系统可以根据业务部署的告警策略,自动识别人员、空间环境、物体等场景下的异常情况,从而实现视频监控从被动转向主动预警。同时,本研究基于智能监控系统应用实践,提供了一个智能监控体系构建流程和应用场景,帮助企业更好地将传统视频监控与智能视频分析系统相结合,创建智能监控业务和应用场景。

基于SEI的煤矿智能监控视频传输方法

目前煤矿视频监控数据传输存在高延迟问题,而视频传输延迟的主要成因是编码延迟。针对该问题,提出了一种无视频编码的基于媒体补充增强信息(SEI)的煤矿智能监控视频传输方法。该方法在解复用视频流得到视频压缩帧后缓存一份副本,解码视频压缩帧得到视频解码帧,通过SEI存储视频解码帧中AI模型分析结果,根据时间戳对应关系将自定义SEI写入该视频解码帧对应视频压缩帧副本的网络提取层单元,并复用视频压缩帧副本,实现煤矿智能监控视频实时传输。在24核CPU上对该方法进行实验测试,结果表明:对于1280×720分辨率的视频,采用该方法处理视频时CPU整体使用率由采用传统方法时的24.7%~36.6%降至20.3%~23.9%,端到端延迟由1946 ms降至345 ms;对于1920×1080分辨率的视频,采用该方法处理视频时CPU整体使用率由采用传统方法时的29.2%~41.8%降至18.5%~26.3%,端到端延迟由6204 ms降至479 ms。该方法通过规避视频编码环节,降低了煤矿智能监控视频传输延迟,且节省了视频编码所需的CPU或GPU资源,降低了智能视频监控系统硬件成本。