A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.

A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

As an emerging hot technology,smart grids(SGs)are being employed in many fields,such as smart homes and smart cities.Moreover,the application of artificial intelligence(AI)in SGs has promoted the development of the power industry.However,as users’demands for electricity increase,traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities.This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation,electrical security,and other aspects.Accordingly,this study proposes a distributed power trading scheme based on blockchain and AI.To protect the legitimate rights and interests of consumers and producers,credibility is used as an indicator to restrict untrustworthy behavior.Simultaneously,the reliability and communication capabilities of nodes are considered in block verification to improve the transaction confirmation efficiency,and a weighted communication tree construction algorithm is designed to achieve superior data forwarding.Finally,AI sensors are set up in power equipment to detect electricity generation and transmission,which alert users when security hazards occur,such as thunderstorms or typhoons.The experimental results show that the proposed scheme can not only improve the trading security but also reduce system communication delays.

An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

Recent years witness a great deal of interest in artificial intelligence(AI)tools in the area of optimization.AI has developed a large number of tools to solve themost difficult search-and-optimization problems in computer science and operations research.Indeed,metaheuristic-based algorithms are a sub-field of AI.This study presents the use of themetaheuristic algorithm,that is,water cycle algorithm(WCA),in the transportation problem.A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution.Since the parameters are stochastic,the corresponding constraints are probabilistic.They are converted into deterministic constraints using the stochastic programming approach.In this study,we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems.WCA is influenced by the water cycle process of how streams and rivers flow toward the sea(optimal solution).WCA is applied to the stochastic transportation problem,and obtained results are compared with that of the new metaheuristic optimization algorithm,namely the neural network algorithm which is inspired by the biological nervous system.It is concluded that WCA presents better results when compared with the neural network algorithm.

Serverless distributed learning for smart grid analytics

The digitization,informatization,and intelligentization of physical systems require strong support from big data analysis.However,due to restrictions on data security and privacy and concerns about the cost of big data collection,transmission,and storage,it is difficult to do data aggregation in real-world power systems,which directly retards the effective implementation of smart grid analytics.Federated learning,an advanced distributed learning method proposed by Google,seems a promising solution to the above issues.Nevertheless,it relies on a server node to complete model aggregation and the framework is limited to scenarios where data are independent and identically distributed.Thus,we here propose a serverless distributed learning platform based on blockchain to solve the above two issues.In the proposed platform,the task of machine learning is performed according to smart contracts,and encrypted models are aggregated via a mechanism of knowledge distillation.Through this proposed method,a server node is no longer required and the learning ability is no longer limited to independent and identically distributed scenarios.Experiments on a public electrical grid dataset will verify the effectiveness of the proposed approach.

Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature

The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

基于DAI的FMS智能检测监控系统研究

详细介绍了基于分布式人工智能（ＤＡＩ）技术的ＦＭＳ智能检测监控系统。采用多Ａｇｅｎｔ方法建立了ＦＭＳ检测监控系统的控制模型，确立每一Ａｇｅｎｔ核心是由基于知识的监控模块，诊断模块，融合、协调与控制模块构成，并研究了ＦＭＳ智能检测监控的融合、协调与控制策略和系统组建技术。

建立DAI系统的组织模型

在DAI系统中组织结构是多主体间实现有效协同的基本要素之一。针对处理大粒度结点集成的DAI系统提出“系统组织模型”的概念。系统组织模型定义DAI系统的组织结构、形式和组织原则，这些定义形成关于一个组织的法律和规范，各主体求解时的意图和行为必须以遵循这些法律和规范为前提。建立系统组织模型的目的在于明确而完整地表示系统组织的结构和整体性质，以引导各主体的设计、实现和协作求解，增强系统的全局连贯性。

DAI中面向对象的并发程序设计

1引言 对象具有一定的独立性且具有一统一的通信协件所以将系统分解成为并行运行的伟的集合。

Artificial Intelligence Based Smart Energy Community Management: A Reinforcement Learning Approach

This paper presents a smart energy community management approach which is capable of implementing P2P trading and managing household energy storage systems.A smart residential community concept is proposed consisting of domestic users and a local energy pool,in which users are free to trade with the local energy pool and enjoy cheap renewable energy while avoiding the installation of new energy generation equipment.The local energy pool could harvest surplus energy from users and renewable resources,at the same time it sells energy at a higher price than Feed-in-Tariff(FIT)but lower than the retail price.In order to encourage the participation in local energy trading,the electricity price of the energy pool is determined by a real-time demand/supply ratio.Under this pricing mechanism,retail price,users and renewable energy could all affect the electricity price which leads to higher consumers’profits and more optimized utilization of renewable energy.The proposed energy trading process was modeled as a Markov Decision Process(MDP)and a reinforcement learning algorithm was adopted to find the optimal decision in the MDP because of its excellent performance in on-going and model-free tasks.In addition,the fuzzy inference system makes it possible to use Q-learning in continuous state-space problems(Fuzzy Q-learning)considering the infinite possibilities in the energy trading process.To evaluate the performance of the proposed demand side management system,a numerical analysis is conducted in a community comparing the electricity costs before and after using the proposed energy management system.

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

Study and application of reinforcement learning based on DAI in cooperative strategy of robot soccer

A dynamic cooperation model of multi-agent is established by combining reinforcement learning with distributed artificial intelligence(DAI),in which the concept of individual optimization loses its meaning because of the dependence of repayment on each agent itself and the choice of other agents.Utilizing the idea of DAI,the intellectual unit of each robot and the change of task and environment,each agent can make decisions independently and finish various complicated tasks by communication and reciprocation between each other.The method is superior to other reinforcement learning methods commonly used in the multi-agent system.It can improve the convergence velocity of reinforcement learning,decrease requirements of computer memory,and enhance the capability of computing and logical ratiocinating for agent.The result of a simulated robot soccer match proves that the proposed cooperative strategy is valid.

Implementation of artificial intelligence techniques in microgrid control environment:Current progress and future scopes

Microgrids are gaining popularity by facilitating distributed energy resources(DERs)and forming essential consumer/prosumer centric integrated energy systems.Integration,coordination and control of multiple DERs and managing the energy transition in this environment is a strenuous task.Classical control techniques are not enough to support dynamic microgrid environments.Implementation of Artificial Intelligence(AI)techniques seems to be a promising solution to enhance the control and operation of microgrids in future smart grid networks.Therefore,this paper briefly reviews the control architectures,existing conventional controlling techniques,their drawbacks,the need for intelligent controllers and then extensively reviews the possibility of AI implementation in different control structures with a special focus on the hierarchical control layers.This paper also investigates the AI-based control strategies in networked/interconnected/multi-microgrids environments.It concludes with the summary and future scopes of AI implementation in hierarchical control layers and structures including single and networked microgrids environments.

大庆油田CIFLog测井数智云平台建设应用实践

针对大庆油田生产中测井数据量大、类型多和数据来源复杂等问题,以中国石油天然气集团有限公司大型测井处理解释软件CIFLog为基础,以业务需求为主导,采用微服务架构和测井分布式云计算技术体系,研发测井大数据存储管理、中间服务层和云端测井处理解释应用等新功能,形成了大庆油田测井数智云应用平台。目前,平台已全面安装部署到大庆油田相关单位,应用效果显著。特别在大庆油田智能决策中心,平台直接用于重点水平井随钻地质导向的现场决策,大幅提升了Ⅰ类储层的钻遇率。未来平台将重点围绕新功能研发、油田数智化应用场景建设和标准化技术体系构建等开展工作,并将取得的成果及时推广复制到西南油田、塔里木油田等油气田。CIFLog云平台作为中国油气工业软件数智化建设应用的先行典范,必将发挥越来越重要的示范引领作用。

改进流向算法的无线传感器网络覆盖优化

针对标准流向算法易陷入局部最优和收敛精度低等问题,提出一种融合莱维(Levy)飞行和入侵杂草策略的改进流向算法.首先,该算法在选择水流流向时引入Levy飞行机制,使水流沿最优水流位置方向做Levy飞行运动,避免陷入局部最优;其次,利用入侵杂草策略,对每一代水流进行繁殖、空间扩散和竞争操作,增加水流的多样性,扩大搜索范围,提高全局寻优能力.最后,将改进流向算法应用于无线传感器网络覆盖优化中,并与标准流向算法及其他改进算法进行实验对比.仿真结果表明,相比标准流向算法及其他改进算法,所提改进流向算法的覆盖性能有大幅提升,覆盖率可达98.52%,可实现更均匀的节点分布和更低的部署成本.

数学哲学与人生智慧

数学概念深远地影响着人类的思维方式和世界观。文章探讨了贝叶斯定理、泊松分布和傅里叶变换在日常生活中的应用,揭示了这些数学公式背后的深层人生哲理。数学理论不仅能够解释科学现象,也为个人决策提供了可参照的方案,指导我们如何更新信念、把握机遇,从不同角度理解复杂问题。

工业机器人应用会加剧中国城乡收入差距吗?

基于IFR数据库中国工业机器人数据,在科学测度2007—2019年中国31个省级区域工业机器人渗透度及城乡收入差距的基础上,细致刻画工业机器人应用对城乡收入差距的作用机制。研究发现:工业机器人应用显著加剧了中国城乡收入差距,因而有效处理机器人应用的收入分配效应能为高质量增长和共同富裕战略的协同共进提供重要支撑;工业机器人应用会通过提升就业技术结构高级化水平和资本利润率两个渠道,加剧中国城乡居民收入差距,且工业机器人应用在高劳动收入份额区域内引致的城乡收入差距效应较为显著;工业机器人应用有利于激发产业结构服务化及合理化的演进动力,并对制造业与生产性服务业协同集聚具有催化剂作用。为此,中国政府应及时和准确地预判工业机器人应用对中国目前及未来较长一段时间内经济与社会发展的动态影响,并在享受人工智能时代红利的同时,平抑其对城乡收入差距造成的负面冲击。

引入激活扩散的类分布关系近邻分类器

针对同质性关系分类器基于一阶Markov假设简化处理的局限性,在类分布关系近邻分类器构建类向量和参考向量时,引入局部图排序激活扩散方法,并结合松弛标注的协作推理方法,通过适当扩大分类时邻居节点的范围增加网络数据中待分类节点的同质性,从而降低分类错误率.对比实验结果表明,该方法扩大了待分类节点的邻域,在网络数据上分类精度较好.

面向战场态势感知的信息融合应用及发展趋势研究

在战争信息化变革的需求牵引以及计算机、网络通信技术的推动下,信息融合技术取得了巨大的发展,并在战场态势感知领域中得到广泛应用。介绍了近年来信息融合技术的发展动态及其在战场态势感知领域的应用成果,分析了信息融合技术在战场态势感知领域中的发展趋势,并着重探讨了信息深度融合处理、分布式协同态势感知、基于人工智能的融合处理等未来研究方向。

人工智能对企业收入分配的非线性影响——基于2007—2022年上市公司数据的检验

人工智能带来的新的技术变革促使企业收入分配格局发生变化。基于2007—2022年沪深A股上市公司数据,通过文本分析法对人工智能技术和应用进行科学测算,并就人工智能对企业利润与工资之间收入分配的影响展开分析。研究发现:人工智能技术和应用对企业利润与工资收入分配的影响呈“U”型,即在人工智能技术和应用发展的早期,利润与工资比率不断下降,差距逐渐缩小;但当人工智能技术和应用超过阈值时,利润不断吞噬工资,导致二者之间差距逐渐扩大。该结论在经历了替换被解释变量、分时段检验、剔除直辖市样本等一系列稳健性检验后依然成立。机制分析发现,人工智能技术和应用能够通过就业规模效应、就业结构效应和生产率效应间接影响企业利润与工资之间的分配。进一步研究发现,财务柔性能够正向调节人工智能与利润和工资之间收入分配的“U”型关系,对于财务柔性较高的企业来说,人工智能技术和应用与企业内部分配效应的“U”型关系较为明显,说明人工智能技术和应用对财务柔性较高的企业分配效应的影响相对更大。研究为在人工智能加速发展背景下提高员工工资、抑制企业间资本和劳动差距扩大提供了重要的政策建议。

基于分布式系统的植保机智能与安全性能优化

针对植保机智能化程度和安全性能无法满足要求以及过多的信息资源造成植保机无法进行合理的任务分配问题,基于分布式系统对植保机进行了设计,并对其智能和安全性能进行优化。为了对多植保机进行调度,解决任务分配问题,对植保机的分布式系统软件进行了设计,包括建立植保机的一对多任务分配模型,并对模型进行人工免疫算法设计,以在较短的时间内得到最优的任务分配方式。为了验证植保机的性能,对植保机进行了多任务分配试验和智能变量喷洒试验,结果表明:植保机可根据飞行任务自动调整喷药量,实现智能变量喷洒,且可快速地对植保机进行任务分配。