Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cyber Threat Intelligence(CTI)can facilitate APT actors’profiling for an immediate response.However,it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics,Techniques and Common Knowledge(ATT&CK)of threat behavior description,this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network(HTN)and Graph Convolutional Network(GCN)to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional EncoderRepresentation fromTransformers(BERT)pretraining model to analyze the contextual semantics of cyber threat intelligence,the task of threat behavior identification is transformed into a text classification task,which automatically extracts attack behavior in CTI,then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86%and 92.15%for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45%and 99.48%,which are better than the benchmark model in the experiment and achieve state of the art.The model can effectivelymodel threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

Federated Feature Concatenate Method for Heterogeneous Computing in Federated Learning

Federated learning is an emerging machine learning techniquethat enables clients to collaboratively train a deep learning model withoutuploading raw data to the aggregation server. Each client may be equippedwith different computing resources for model training. The client equippedwith a lower computing capability requires more time for model training,resulting in a prolonged training time in federated learning. Moreover, it mayfail to train the entire model because of the out-of-memory issue. This studyaims to tackle these problems and propose the federated feature concatenate(FedFC) method for federated learning considering heterogeneous clients.FedFC leverages the model splitting and feature concatenate for offloadinga portion of the training loads from clients to the aggregation server. Eachclient in FedFC can collaboratively train a model with different cutting layers.Therefore, the specific features learned in the deeper layer of the serversidemodel are more identical for the data class classification. Accordingly,FedFC can reduce the computation loading for the resource-constrainedclient and accelerate the convergence time. The performance effectiveness isverified by considering different dataset scenarios, such as data and classimbalance for the participant clients in the experiments. The performanceimpacts of different cutting layers are evaluated during the model training.The experimental results show that the co-adapted features have a criticalimpact on the adequate classification of the deep learning model. Overall,FedFC not only shortens the convergence time, but also improves the bestaccuracy by up to 5.9% and 14.5% when compared to conventional federatedlearning and splitfed, respectively. In conclusion, the proposed approach isfeasible and effective for heterogeneous clients in federated learning.

Modeling heterogeneous behaviors with different strategies in a terrorist attack

Existing simulations of terrorist attacks do not consider individual variations.To overcome this lim-itation,we propose a framework to model heterogeneous behavior of individuals during terrorist attacks.We constructed an emotional model that integrated personality and visual perception for pedestrians.The emotional model was then integrated with pedestrian relationship networks to establish a decision-making model that sup-ported pedestrians’altruistic behaviors.A mapping model has been developed to correlate antisocial personality traits with attack strategies employed by terrorists.Experiments demonstrate that the proposed algorithm can generate practical heterogeneous behaviors that align with existing psychological research findings.

Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms

Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

Heterogeneous Vehicular Networks for Social Networks： Requirements and Challenges

Heterogeneous vehicular networks （HetVNETs） are regarded as a promising technique for meeting various requirements of intelli- gent transportation system （ITS） services. With the rapid development of mobile Internet in the past decade, social networks （SNs） have become an indispensable part of human life. Based on this indivisible relationship between vehicles and users, social charac- teristics and human behaviors can significantly affect vehicular network performance. Hence, we firstly present two architectures for SNs by introducing social characteristics into the HetVNETs. Then, several user cases are also given in this paper, in which service requirements are analyzed simultaneously. At last, we briefly discuss potential challenges raised by the HetVNETs for SNs.

Resource Allocation for Two-Tier RIS-Assisted Heterogeneous NOMA Networks

Reconfigurable intelligent surface(RIS)as a promising technology has been proposed to change weak communication environ-ments.However,most of the current resource allocation(RA)schemes have focused on RIS-assisted homogeneous networks,and there is still no open works about RA schemes of RIS-assisted heterogeneous networks(HetNets).In this paper,we design an RA scheme for a RIS-assisted HetNet with non-orthogonal multiple access to improve spectrum efficiency and transmission rates.In particular,we jointly optimize the transmit power of the small-cell base station and the phase-shift matrix of the RIS to maximize the sum rates of all small-cell users,subject to the unit modulus constraint,the minimum signal-to-interference-plus-noise ratio constraint,and the cross-tier interference constraint for protecting communication quality of microcell users.An efficient suboptimal RA scheme is proposed based on the alternating iteration ap-proach,and successive convex approximation and logarithmic transformation approach.Simulation results verify the effectiveness of the pro-posed scheme in terms of data rates.

Effective Approach to Elevate the Intelligence of Management Decision System

Based on the sticking point of the low intelligence of the existing management decision system,this paper puts forward the idea of enriching and refining the knowledge of the system and endowing it with the ability to learn by means of adopting three types of heterogeneous knowledge representation and knowledge management measures.At length,this paper outlines the basic framework of an intelligence system for the sake of management decision problem.

异质交通流环境下无信号T形交叉口控制策略

针对无信号控制T形交叉口,提出一种异质交通流环境下交叉口的分级限速控制模型.根据异质交通流的车辆运行特性提出分级限速的概念,并建立交叉口的车辆信息矩阵.构建以车流通过冲突区域时间最短为优化目标,以保证冲突车辆安全行驶的时间间隔为约束条件的线性规划模型.通过MATLAB搭建仿真平台对所提出的模型有效性进行试验验证.结果表明:在混合行驶的交通环境下,当车辆到达率为0.30~0.50时,本研究所提模型可以在传统控制模型的基础上提升交叉口通行能力约20%;分级限速控制模型下智能网联车辆渗透率达到0.90时,最多可以增加交叉口约17%的通行能力;所提模型能有效提高道路资源利用效率,保证主路运行通畅的情况下大幅降低支路车辆延误.

基于智慧计量实验室的多源异构检测数据智能提取技术研究

文章在解决智慧计量实验室中多源异构检测数据的提取与处理问题,对计量实验室检测数据类型和异构类信息提取面临的问题进行分析,提出基于图像处理的检测数据提取技术路线;设计了一种基于可微分二值化网络(differentiable binarization networks,DBNet)和卷积循环神经网络(convolutional recurrent neural network,CRNN)的检测数据智能提取技术,实现了对多源异构数据的自动检测、识别和提取。在此基础上,研制了多源异构检测数据智能提取装置,并进行了验证,结果表明,该装置能够有效地提取纸质报告或表单中的检测数据等关键信息,具有较高的准确性和较快的响应速度,为智慧计量实验室的数据管理和分析提供了有力支持。该研究对于推动智慧计量实验室的建设和试验检测数据应用具有重要意义。

人工智能与企业劳动收入份额——基于新一代人工智能创新发展试验区试点的准自然实验

城市数智化转型在人工智能发展下逐步推进,并起到赋能经济社会和企业高质量发展的作用。基于此,本文以国家新一代人工智能创新发展试验区为外生冲击,通过构建双重差分模型探究城市数智化转型对企业劳动收入分配的效应。研究结果表明,相对非试点城市而言,试点城市内企业的劳动收入份额显著提高。异质性分析发现,试验区设立对企业劳动收入份额的提升效应在国有企业、资本密集型企业、员工议价能力高的企业、东中部地区和市场化水平低的企业更明显。机制分析得出,城市数智化转型能够增加企业现金充裕性、提高企业人力资本水平,进而增加劳动收入份额。进一步分析表明试验区的设立能够改善企业内部的收入不平等,尤其是劳动错配程度较大地区的企业。本文丰富了人工智能试验区的经济后果研究,为推动人工智能落实发展、提高劳动收入份额提供了政策启示。

基于Agent人工智能的异构网络多重覆盖节点入侵检测系统设计

异构网络具有结构复杂、多重覆盖面积大等特征,使得网络入侵检测较为隐蔽,威胁网络运行的安全性;为此,对基于Agent人工智能的异构网络多重覆盖节点入侵检测系统进行了研究;通过检测Agent和通信Agent装设主机Agent,以Cisco Stealthwatch流量传感器作为异构网络传感器检测攻击行为,采用STM32L151RDT664位微控制器传输批量数据,由MAX3232芯片实现系统电平转化,实现硬件系统设计;软件部分设计入侵检测标准,采用传感器设备捕获网络实时数据,通过Agent技术解析异构网络协议并提取数据运行特征,综合考虑协议解析结果及与检测标准匹配度,实现异构网络多重覆盖节点入侵检测;经实验测试表明,基于Agent人工智能的异构网络多重覆盖节点入侵检测系统入侵行为的漏检率和入侵类型误检率的平均值仅为6%和5%,能够有效提高检测精度,减小检测误差。

基于异构多智能体自注意力网络的路网信号协调顺序优化方法

针对路网交通信号控制的复杂性,本文提出基于异构多智能体自注意力网络的路网信号协调顺序优化方法,提升路网范围内多交叉口信号控制策略性能。首先,模型考虑多交叉口交通流的空间相关性,采用基于自注意力机制的价值编码器学习交通观测表征,实现路网级通信;其次,面向多智能体策略更新的非稳态环境,模型在前序智能体的联合动作基础上,基于多智能体优势分解的策略解码器,顺序决策最优反应动作;最后,设计基于有效行驶车辆的动作掩码机制,在时效完备区间自适应调节决策频率,并提出考虑等待公平性的时空压力奖励函数,进一步提高策略性能与实用性。在杭州路网数据集上验证模型有效性,结果表明:所提模型在2个数据集和5个性能指标上均优于基准模型;相比最优基准模型,所提模型平均行程时间降低10.89%,平均排队长度降低18.84%,平均等待时间降低22.21%。此外,所提模型的泛化能力更强,且显著减少车辆等待时间过长的情形。

中国城市新型基础设施建设影响人工智能产业链发展的时空分异研究

近年来,新型基础设施建设(“新基建”)在国家战略层面密集部署,成为培育新兴产业和构筑新兴产业链的重要支撑。鉴于现有研究对城市空间尺度“新基建”影响新兴产业链不同环节发展效应的时空分异关注有限,该文分析2011—2022年中国主要城市“新基建”和人工智能产业链发展的时空特征,揭示城市“新基建”影响人工智能产业链上中下游不同环节发展效应的时空分异特征。研究发现:①2011年以来中国主要城市新型基础设施和人工智能产业链取得快速发展,在国家中心城市和城市群核心城市形成空间集聚;②城市“新基建”综合发展水平有效推动了人工智能产业链的发展,影响程度由高到低依次是下游应用层、中游技术层和上游基础层,推动效应随时间推移而强化;③城市“新基建”影响人工智能产业链发展的效应在空间上主要凸显于东部沿海地区和核心城市群,影响程度在城市群发展梯队之间的空间异质性超过东中西部三大地带之间的空间异质性,第一梯队城市群正成为城市“新基建”赋能人工智能产业链发展的重要空间载体。

基于异质集成的井漏预警模型

钻井井漏事故具有突发性和难以控制的特点。因此,迫切需要建立一种有效的井漏预测方法。将随机森林、支持向量机和反向传播神经网络模型相结合的异质积分器Stacking应用于青海省柴达木盆地英西地区。首先对目标区块的数据集进行处理,运用灰色关联对数据进行相关性分析,选择其中10个相关性高的参数,后设置两层堆叠集成,第一层选择随机森林、支持向量机和BP神经网络模型作为基础学习器,第二层选择逻辑回归模型作为元学习器。结果表明,异质集成模型提高了预测精度(0.981的准确率、0.970的精确率、0.963的召回率和0.960的F 1分数),克服了同质分类器的局限性。强调了综合井漏预警预报中考虑多种地质因素的重要性。

异质车型影响下智能汽车二维碰撞风险预测

行车碰撞风险的准确预测是保证智能汽车安全行驶的关键,但目前评估方法鲜有考虑异质车型风险差异性及横纵向二维风险耦合。为此,本文首先挖掘异质车型驾驶人的行为规律,解析车型对驾驶人风险敏感度的影响;其次,辨识获得不同车型组合的异质风险阈值,利用二维指标量化交通风险差异性;最终提出考虑车辆类型的二维碰撞风险耦合预测模型,并通过对比验证模型的准确性。该研究有助于提升智能汽车的行驶安全性,且可为人类驾驶汽车的碰撞预警系统开发提供理论依据。

基于PSO的飞行器姿态智能控制技术研究

以气动舵/推力矢量/直接力复合控制飞行器为对象,研究了多维异构复合姿态控制系统的控制律及分配律一体化智能设计问题。首先提出了基于PSO(Particle Swarm Optimization)算法的“智能控制律+智能分配律”飞行器姿态多维异构智能复合控制算法框架,并建立了控制系统数学模型,然后重点研究了控制律及分配律参数的智能优化算法实现方法,最后通过数学仿真验证了提出方法的有效性。仿真结果表明,所设计智能优化复合控制系统在标称模型以及模型摄动情况下均能够实现对姿态角指令的准确快速跟踪。

跨平台集成监控系统的研究与探索

本文首先分析了西北油田公司数字化监控系统的建设现状,并在研究集团公司统一标准和智能化发展需求的基础上,提出了跨平台集成监控系统的建设目标;其次围绕增强油田安全生产可视化、数字化、智能化管理水平,研究了该系统的体系架构和模型;最后研究了该系统的功能技术特性和应用效果。

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence:Cases,Applications,Issues,and Future Directions

This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities,challenges such as diverse diagnostic evidence,complex etiology,and interdisciplinary knowledge integration impede the interpretability,reliability,and applicability of AI-based DED detection methods.The research conducts a comprehensive review of datasets,diagnostic evidence,and standards,as well as advanced algorithms in AI-based DED detection over the past five years.The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques:(1)those with ground truth and/or comparable standards,(2)potential AI-based methods with significant advantages,and(3)supplementary methods for AI-based DED detection.The study proposes suggested DED detection standards,the combination of multiple diagnostic evidence,and future research directions to guide further investigations.Ultimately,the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations,advanced methods,challenges,and potential future perspectives,emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.