Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

AIOps在IT设备全生命周期管理中的应用研究

为提升云资源池IT设备运维效率和降低运维成本,本文对IT设备全生命周期引入AI能力进行研究。本文通过梳理IT设备全生命周期管理中的各个阶段,分析引入AI能力的可能以及产生的效果,给出IT设备智能化运维的建议,为通过AI和大数据技术实现IT设备智能化运维提供参考。

Review on artificial intelligence techniques for improving representative air traffic management capability

The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

Probabilistic Rationale of Actions for Artificial Intelligence Systems Operating in Uncertainty Conditions

The approach for probabilistic rationale of artificial intelligence systems actions is proposed.It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling.The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.The approach includes description of the proposed probabilistic models,optimization methods for rationale actions and incremental algorithms for solving the problems of supporting decision-making on the base of monitored data and rationale robot actions in uncertainty conditions.The approach means practically a proactive commitment to excellence in uncertainty conditions.A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems.

Decision support model for effects estimation and proportionality assessment for targeting in cyber operations

Cyber operations are relatively a new phenomenon of the last two decades.During that period,they have increased in number,complexity,and agility,while their design and development have been processes well kept under secrecy.As a consequence,limited data(sets)regarding these incidents are available.Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations(such as attack,target identification and selection,and collateral damage),still methodologies and models are needed in order to plan,execute,and assess them in a responsibly and legally compliant way.Based on these facts,it is the aim of this article to propose a model that i))estimates and classifies the effects of cyber operations,and ii)assesses proportionality in order to support targeting decisions in cyber operations.In order to do that,a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical e military experts.The proposed model was evaluated on two cyber operations use cases in a focus group with four technical e military experts.Both the design and the results of the evaluation are revealed in this article.

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model

AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.

Capacity Openness Boosts Digitization Transformation of Telecom Operators

With the emergence of digital transformation success stories,more and more enterprises are launching digital solutions to improve business efficiency or achieve optimal revenue growth.The traditional communication market tends to be saturated,and the digital transformation of telecom operators is imminent.This article combining the development trend of telecom operators’product business and the application situation of the pilot summarizes the innovative application categories that telecom operators continue to launch based on their own advantages and in response to the needs of the industry.This article focuses on the transformation of security capabilities in communication services,big data,Internet of Things,artificial intelligence,unified authentication and network information security,and deeply analyzes the business characteristics and application modes from the aspects of resource integration,open platform construction,new connection construction,and innovative business development.This article puts forward suggestions for the security development of telecom operators,and provides basic research content for the subsequent research on the innovative application of telecom operators’capability openness.

基于人工智能的水电站IT系统自动化运维应用研究

当前大多数水电站均存在人力及物力投入量大、工作精细化程度低等弊端,对科学、高效的运维模式进行创新探索尤为迫切。针对在日益复杂的业务环境和多样化的用户需求,运用新一代的基于人工智能的自动化运维技术,综合标准化、结构化、流程化、自动化和智慧化等手段,构建基于人工智能的水电站IT系统自动化运维方法及应用支持系统。应用人工智能技术,建立一个与实际水电站相匹配的3D数字化水电站,并以3D数字化模型为载体统筹管理水电站数据,实现水电站信息系统自动化运维,降低IT服务成本和提升IT服务效率。实验结果表明:该方法传输带宽利用率可达到97.5%,运维知识的答案匹配度在97%~99%范围内变化,且并发调用响应时间低于1.2 s,满足信息化运维实时性要求。

论人工智能技术在我国档案工作中的应用

从档案收管用三个业务环节出发,探讨人工智能技术应用于我国档案工作面临的问题,提出人工智能技术应用于我国档案工作中的建议,以提高我国档案工作的水平。通过文献研究和案例分析,总结人工智能技术应用于我国档案工作中存在的问题,并在此基础上提出优化建议。人工智能技术在我国档案工作中的应用前景广阔,目前已有的实践案例对促进人工智能技术在我国档案工作中的应用起到了积极作用。

基于AIOps智能运维的云原生多云管理的研究与改进

文章对基于AIOps智能运维的云原生多云管理技术展开了研究,旨在帮助企业提高多云环境中应用程序的管理质量和效率。其中,首先分析了多云环境下应用程序管理面临的挑战,并介绍了AIOps智能运维技术的概念和原理;其次,设计并实现了一个基于AIOps的云原生多云管理系统,其中包括需求分析、系统架构设计、功能模块实现和测试等环节;最后,通过实验和结果分析验证了该系统在多云环境中对应用程序管理的有效性和可行性。研究表明,基于AIOps智能运维的云原生多云管理技术可有效解决多云环境下应用程序的管理问题,能提高管理效率和质量,具有一定的实用价值。

论人工智能时代大学生精准思政运行机制的构建

人工智能时代的到来为大学生精准思政的有效实施带来了重要机遇。但是有效推进人工智能时代大学生精准思政,需要厘清人工智能时代大学生精准思政系统的构成要素,在此基础上,再积极构建精准思政的运行机制,即问题导向机制、数据采集机制、决策分析机制、协同育人机制、评价反馈机制、正向激励机制、系统优化机制等。这有利于为大学生精准思政提供规范的制度保障,不断生成强大的内在驱动力,最终促进人工智能时代大学生精准思政提质增效。

关于医院建筑机电工程创新的思考

现今医院建筑机电工程模式与当今快速发展的时代背景不协调,妨碍了机电工程实现功能需求和“双碳”目标的要求,需要创新。分析了现今医院建筑机电工程模式的弊病;提出了在完成机电系统综合的基础上,与建筑工程剥离,作为完整独立的工程项目单独实施,在整个过程中以运维作引导,充分发挥设计龙头作用;基于医院建筑电气化、电力需求响应,探讨了医院建筑机电工程实现功能目标且经济低碳的运维策略;最后指出医院建筑机电工程创新的直接受益者和主导者是医院。

Koopman原理内嵌MLP神经网络模型驱动的电力系统非线性振荡特征分析方法

针对电力系统非线性动态特性表征与物理机理融合不清晰、精度低的问题,提出了一种Koopman原理内嵌多层感知机(MLP)神经网络模型驱动的电力系统非线性特性表征与分析方法。阐明了Koopman算子的基本原理,分析了Koopman算子在非线性系统时序演化中的作用。采用MLP神经网络构建编码、解码映射,进而形成Koopman原理内嵌的神经网络深度学习模型,通过深度学习实现非线性系统“编码映射-线性演化-解码映射”3种结构的演化逼近。分析了将所提方法应用于电力系统动态特性分析的物理机理,建立了所提方法的求解与应用流程。通过单机与4机系统算例对所提方法进行对比验证,结果表明所提方法可以精确表征平衡点稳定域内的系统动态过程,可用于电力系统非线性振荡动态特性解析。

居住功能体检器助力好房子建设

随着城市化的推进,房地产市场从增量转向存量,房屋运维管理变得至关重要。本文探讨了居住功能体检器的重要性,该系统融合了人工智能和物联网技术,已在住建部好房子科技展厅试点探索。文章从住房存量问题、房屋体检制度、居住功能体检器功能设定及可能带来的改变等方面进行分析。

面向安全高效运行的重载列车智能操控关键技术

面向我国重载铁路自动化和智能化发展以及重载铁路运输需求持续增长对既有列车运行控制带来的严峻挑战,文章围绕重载列车智能操控关键技术展开了研究现状介绍,并分析了目前亟待解决的难题与未来发展趋势。首先,针对列车纵向动力学建模,已有研究多考虑单一环境、固定工况下机理建模及分析,难以精确表征列车运行过程中的内、外部参数时空渐变特性,因此,在已有机理模型基础上考虑未建模动态,并进行降阶等效是未来一个重要研究方向。其次,长编组列车车辆间非线性强耦合特性、空气制动特性对列车运行过程中纵向冲动的影响依旧为重载列车操纵优化带来了巨大的挑战,借鉴高速列车多车协同控制技术,研究考虑主动抑制纵向冲动的重载列车多机车分布式协同控制具有重要意义。最后,结合数据驱动与人工智能算法、多源信息融合等新兴技术分析了列车控制领域一些新的机遇,而耦合仿真平台与评估体系的搭建也将成为重载列车安全运行分析、评估与优化的一种有效途径。

人工智能背景下运筹学课程教学改革探索

在当今人工智能飞速发展的时代背景下,针对数学专业运筹学课程的教学,以同济大学数学科学学院“运筹学与优化”的课程实践为例,从教材选取、教学内容与教学设计、课程思政几个方面进行了探讨,以提高教学质量,提升专业课程的育人效果,培养学生的专业素养与创新能力。

基于大语言模型的电力系统通用人工智能展望:理论与应用

大语言模型(LLM)是一种利用大规模文本语料库进行预训练和微调的深度学习语言模型。目前,在通识问答、文本生成和科学推理等方面已展现出强大的能力。在此背景下,文中探索了基于LLM构建面向电力系统的通用人工智能技术,并展望其在电力系统中的潜在应用。首先,介绍了LLM的基本原理、神经网络架构以及训练方法,特别是与传统人工智能模型相比,LLM在逻辑推理、编程和代码理解以及数学推理方面的突破。然后,展望了LLM在电力系统负荷与新能源发电出力预测、电力系统规划、电力系统运行、电力系统故障诊断与系统恢复、电力市场等领域的潜在应用。最后,阐述了基于LLM构建电力系统通用人工智能技术所面临的挑战,包括电力系统数据的质量与可获取性、输出结果可解释性以及隐私保护问题。

美军认知电子战关键技术发展方向分析

美军认知电子战相关概念发展迅速,也开展了较多的项目研究。为分析其关键技术发展现状并研判其未来发展趋势,从美高校、智库、军事科研机构、厂商4个角度,通过研究论文、报告等公开文献,对美军认知电子战关键技术发展情况,特别是其在机器学习、人工智能方面的探索进行了深入的分析,对美军认知电子战技术领域研究方向、潜在难题及对应的关键技术思路进行了较为全面的梳理,并从数据、模型、平台、应用等方面给出了启示。

网络服务异常事件告警因果图构造方法

网络服务系统中,异常事件的发生经常导致系统中产生大量告警事件,形成告警风暴。运维人员需要花费大量的时间和精力从这些告警数据中寻找关键信息、确定异常事件的根源。为了减少运维人员所需处理的告警数量,智能化、自动化地提取告警风暴中的根源告警,基于网络服务告警的传播模式分析,提出了一种告警因果图构造方法,并将其应用于提取异常事件发生时的告警风暴关键信息。实验使用运营商现网管理系统的真实数据集,通过告警风暴摘要提取实验,验证了告警因果图生成的效果,并进行了相关案例的物理意义分析。结果表明,使用告警因果图生成的方式进行告警风暴摘要提取,达到了96%的召回率,保留了绝大部分关键信息。同时,使用该方法对系统产生的告警进行压缩,对较难压缩的告警码的压缩率能够达到66.5%。