Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile users.It encompasses several heterogeneous resource and communication standard in ensuring incessant availability of service.At the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,etc.In UAV networks,energy efficiency and data collection are considered the major process for high quality network communication.But these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted UAVs.These issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G environment.With this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G environment.The proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets minimized.The presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct clusters.The QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of UAVs.The performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

Collective Intelligence and Uncertain Knowledge Representation in Cloud Computing

The lasting evolution of computing environment, software engineering and interaction methods leads to cloud computing. Cloud computing changes the configuration mode of resources on the Internet and all kinds of resources are virtualized and provided as services. Mass participation and online interaction with social annotations become usual in human daily life. People who own similar interests on the Internet may cluster naturally into scalable and boundless communities and collective intelligence will emerge. Human is taken as an intelligent computing factor, and uncertainty becomes a basic property in cloud computing. Virtualization, soft computing and granular computing will become essential features of cloud computing. Compared with the engineering technological problems of IaaS (Infrastructure as a service), PaaS (Platform as a Service) and SaaS (Software as a Service), collective intelligence and uncertain knowledge representation will be more important frontiers in cloud computing for researchers within the community of intelligence science.

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.

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV networks.EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different dimensions.The experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

Development of a tracking-based system for automated traffic data collection for roundabouts

Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challenging due to more complex traffic scenes, data specifications and vehicle behavior. In this paper, the authors propose an automated traffic data collection system dedicated to roundabout scenes. The proposed system has mainly four steps of processing. First, camera calibration is performed for roundabout traffic scenes with a novel circle-based calibration algorithm. Second, the system uses enhanced Mixture of Gaussian algorithm with shaking removal for video segmentation, which can tolerate repeated camera displacements and background movements. Then, Kalman filtering, Kemel-based tracking and overlap-based opti- mization are employed to track vehicles while they are occluded and to derive the complete vehicle trajectories. The resulting vehicle trajectory of each individual vehicle gives the position, size, shape and speed of the vehicle at each time moment. Finally, a data mining algorithm is used to automatically extract the interested traffic data from the vehicle trajectories. The overall traffic data collection system has been implemented in software and runs on regular PC. The total processing time for a 3-hour video is currently 6 h. The automated traffic data collection system can significantly reduce cost and improve efficiency compared to manual data collection. The extracted traffic data have been compared to accurate manual measurements for 29 videos recorded on 29 different days, and an accuracy of more than 90% has been achieved.

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.

Extremal Coalitions for Influence Games Through Swarm Intelligence-Based Methods

An influence game is a simple game represented over an influence graph(i.e.,a labeled,weighted graph)on which the influence spread phenomenon is exerted.Influence games allow applying different properties and parameters coming from cooperative game theory to the contexts of social network analysis,decision-systems,voting systems,and collective behavior.The exact calculation of several of these properties and parameters is computationally hard,even for a small number of players.Two examples of these parameters are the length and the width of a game.The length of a game is the size of its smaller winning coalition,while the width of a game is the size of its larger losing coalition.Both parameters are relevant to know the levels of difficulty in reaching agreements in collective decision-making systems.Despite the above,new bio-inspired metaheuristic algorithms have recently been developed to solve the NP-hard influence maximization problem in an efficient and approximate way,being able to find small winning coalitions that maximize the influence spread within an influence graph.In this article,we apply some variations of this solution to find extreme winning and losing coalitions,and thus efficient approximate solutions for the length and the width of influence games.As a case study,we consider two real social networks,one formed by the 58 members of the European Union Council under nice voting rules,and the other formed by the 705 members of the European Parliament,connected by political affinity.Results are promising and show that it is feasible to generate approximate solutions for the length and width parameters of influence games,in reduced solving time.

Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data

Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box"nature of AI models often undermines trust due to the lack of transparency in their decision-making processes,even when these models demonstrate high accuracy.To address this challenge,we evaluated the performance of a transformer model against other AI approaches,utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments,enabling the identification of individual indicators'contributions to the model's predictions.We find that the transformer model outperforms others,achieving an accuracy of about 98%and an area under the receiver operating characteristic curve(AUC)of 0.891.Regionally,the environmental assessment values are predominantly classified as level Ⅱ or Ⅲ in the central and southwestern study areas,level Ⅳ in the northern region,and level Ⅴ in the western region.Through explainability analysis,we identify that water hardness,total dissolved solids,and arsenic concentrations are the most influential indicators in the model.Our AI-driven environmental assessment model is accurate and explainable,offering actionable insights for targeted environmental management.Furthermore,this study advances the application of AI in environmental science by presenting a robust,explainable model that bridges the gap between machine learning and environmental governance,enhancing both understanding and trust in AI-assisted environmental assessments.

An intelligent active probing and trace-back scheme for IoT anomaly detection

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

Intelligent pavement condition survey:Overview of current researches and practices

Automated pavement condition survey is of critical importance to road network management.There are three primary tasks involved in pavement condition surveys,namely data collection,data processing and condition evaluation.Artificial intelligence(AI)has achieved many breakthroughs in almost every aspect of modern technology over the past decade,and undoubtedly offers a more robust approach to automated pavement condition survey.This article aims to provide a comprehensive review on data collection systems,data processing algorithms and condition evaluation methods proposed between 2010 and 2023 for intelligent pavement condition survey.In particular,the data collection system includes AI-driven hardware devices and automated pavement data collection vehicles.The AI-driven hardware devices including right-of-way(ROW)cameras,ground penetrating radar(GPR)devices,light detection and ranging(LiDAR)devices,and advanced laser imaging systems,etc.These different hardware components can be selectively mounted on a vehicle to simultaneously collect multimedia information about the pavement.In addition,this article pays close attention to the application of artificial intelligence methods in detecting pavement distresses,measuring pavement roughness,identifying pavement rutting,analyzing skid resistance and evaluating structural strength of pavements.Based upon the analysis of a variety of the state-of-the-art artificial intelligence methodologies,remaining challenges and future needs with respect to intelligent pavement condition survey are discussed eventually.

论古代纵横家对情报搜集工作的探索——以《鬼谷子》和《长短经》为中心的考察

[研究目的]《鬼谷子》《长短经》等典籍中细致讨论了纵横家如何通过言辞探知他人内心想法。挖掘其中的内涵,对丰富现代情报搜集理论与实践具有重要意义。[研究方法]通过文献分析和比较研究,提炼古籍中的相关内容及独到见解,分析其成因与价值。[研究结果/结论]春秋战国时期,纵横学派代表作《鬼谷子》中的“揣情”理论形成了言辞获情的基本逻辑,此后这门学问只是间或受到关注,唐代的《长短经》继承并拓展《鬼谷子》,多维度探寻情报搜集的可行之道。研究认为,古代纵横家对情报搜集工作的探索,本质上是他们利用所掌握的心理学知识,进行情报搜集与外交工作的心得总结,且与当代西方的部分情报理论和实践有相通之处,尤其是在人力情报搜集、间谍招募控制、谎言甄别等环节契合度高,这有助于这门古老学问进一步批判地继承,发挥其应有的学术价值。

Four development stages of collective intelligence

The new generation of artificial intelligence(AI)research initiated by Chinese scholars conforms to the needs of a new information environment changes,and strives to advance traditional artificial intelligence(AI 1.0)to a new stage of AI 2.0.As one of the important components of AI,collective intelligence(CI 1.0),i.e.,swarm intelligence,is developing to the stage of CI 2.0(crowd intelligence).Through in-depth analysis and informative argumentation,it is found that an incompatibility exists between CI 1.0 and CI 2.0.Therefore,CI 1.5 is introduced to build a bridge between the above two stages,which is based on biocollaborative behavioral mimicry.CI 1.5 is the transition from CI 1.0 to CI 2.0,which contributes to the compatibility of the two stages.Then,a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is given.The meta-synthesis of wisdom,as an improvement of crowd intelligence,is an advanced stage of bionic intelligence,i.e.,CI 3.0.It is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0,and some elaboration is made.As a result,we propose four development stages(CI 1.0,CI 1.5,CI 2.0,and CI 3.0),which form a complete framework for the development of CI.These different stages are progressively improved and have good compatibility.Due to the dominant role of cooperation in the development stages of CI,three types of cooperation in CI are discussed:indirect regulatory cooperation in lower organisms,direct communicative cooperation in higher organisms,and shared intention based collaboration in humans.Labor division is the main form of achieving cooperation and,for this reason,this paper investigates the relationship between the complexity of behavior and types of labor division.Finally,based on the overall understanding of the four development stages of CI,the future development direction and research issues of CI are explored.

基坑工程智能化监测的研究与标准化

针对基坑工程现场人工监测的缺点,提出了智能化监测的必要性和优点。对智能化监测系统的采集、传输、数据分析进行了研究,重点探讨了基坑工程智能化监测中体现“智能”的采集和预测环节的核心问题,提出了智能短期增频采集和基于时序的智能分段预测方法。文章同时阐明了成像智能识别的方法核心思路,总结了智能图像识别在基坑工程宏观监测方面的优势,并为基坑工程智能监测的标准化提供了研究基础。

基于人工智能的通信电源监控数据采集方法研究

传统的通信电源监控系统在实际进行数据采集时不仅耗时较多,还在准确率方面存在着较多缺陷。为此,需要将人工智能技术融入其中,结合实际需求细化通信电源监控数据采集流程与内容,进而保证此类系统的运行效果。简述了融合人工智能的常见通信电源监控数据采集方式,并基于对其系统架构的分析,从硬件与软件两个角度出发,阐述了基于人工智能的通信电源监控数据采集系统的设计要点,希望能够为通信电源设计与使用提供一些帮助。

智能质量控制系统在水泥企业的应用实践

为及时发现水泥生产过程中出现的质量波动与生产隐患,开发了智能质量控制系统。该系统主要由样品发送装置、中心实验室、计算机控制中心、辅助实验室等组成,集成了移动通信网络、数据传感监测、信息交互集成和自适应控制等先进技术,通过与质量管理系统融合,实现了样品的自动取样、发送、检测、配比调整、弃料等自动化检验。介绍了智能质量控制系统应用于原材料检测与配比优化、产品质量实时监测、设备故障预测与维护、质量问题追溯等场景以及使用效果、应用中存在的难点,提出了提高智能质量控制系统数据分析能力的方式。

Research on Intelligent Agricultural Planting System Based on Internet of Things Technology

Agriculture is the basic industry that concerns the national economy and people’s livelihood. In the process of transforming to modern agriculture, the traditional agriculture in our country faces the problems of ensuring the quality of agricultural production, adjusting agricultural industrial structures, improving the low production efficiency and low utilization rate of resources, and environmental pollution, thus it cannot meet the needs of sustainable agricultural development. Therefore, the research on intelligent agriculture technology is imperative. This paper analyzes the key technologies of Internet of things applied in the intelligent agriculture, presents the application of Internet of things technology in agricultural planting system, constructs the intelligent agricultural planting system based on the Internet of things technology, and designs the framework of the management platform.

Expert recommendation on collection,storage,annotation,and management of data related to medical artificial intelligence

Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based diagnosis.China has a massive amount of medical data.However,a uniform criteria for medical data quality have yet to be established.Therefore,this review aimed to develop a standardized and detailed set of quality criteria for medical data collection,storage,annotation,and management related to medical AI.This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.

生命体征智能采录系统在监护患者护理中的应用研究

目的探索生命体征智能采录系统在监护患者护理中的应用效果,用以解决原有手工抄录生命体征数据带来的效率低下、误差率高问题,推进全院一体化生命体征数据监测中心的建立。方法利用设备集成模块,将各类生命体征测量设备统一接入中心监护系统,中心监护系统将接收到的生命体征数据进行统一化、标准化和智能化处理,再通过数据网关转发数据至医院各业务系统,完成生命体征智能采录系统的设计,实现医疗设备应用过程中,采集数据的自动化传输、智能化存储和标准化应用。选取5个普通病房和5个重症监护室的生命体征数据采集时间及采录准确性、护理人员满意度作为评价指标对生命体征智能采录系统的效果进行分析。结果生命体征智能采录系统应用后,普通病房及重症病房各科室的生命体征数据采录时间均较系统应用前显著降低,每天平均节省时间分别为14.14~72.59 h、17.00~81.70 h;数据采录准确性和护理人员满意度均高于系统应用前,且差异均有统计学意义(P<0.05)。结论生命体征智能采录系统的应用,实现了生命体征测量设备互联、数据互通、生命体征数据采集流程的数字化,提升了护理安全和护理质量,可帮助医生动态监测患者病情变化并作出准确诊断。

技术与商业驱动的群智创新设计

目的结合人工智能时代下社会面临的新问题及现代设计的新使命,提出了技术与商业驱动的群智创新设计思维。方法以设计全球化、新一代人工智能及数字化生存现状为时代背景,阐述了群智创新设计在新时代下的创新本质,并从技术和商业的多元视角阐述了群智创新设计的多元内涵。结果构建了以设计为牵引,技术与商业驱动的群智创新设计模型,并结合具体案例阐述了群智创新设计的优势。结论群智创新设计注重智能技术和群体智慧的结合,是一场复杂系统层面的螺旋式创新演化。以设计为牵引、技术为支撑、商业为激励的群智创新设计方法借用现代网络平台优势及大数据、人工智能、区块链等信息技术赋能,利用群体智慧的协同作用,聚集社会中更多、更好的知识源,以助力创新创造,为社会创造共赢价值。

基于群智图谱的图书馆智慧阅读推广服务模式研究

文章概述了群智图谱的概念和特征,对当前图书馆阅读推广服务研究现状进行了综述,从构建本体、抽取实体关系、建立映射、知识融合、群体验证五个方面设计了基于群智图谱的图书馆智慧阅读推广服务模型,并指出了该模型的具体应用,最后从塑造沉浸式阅读场景、智慧阅读指导服务、个性化阅读方案制定等方面提出了基于群智图谱的图书馆智慧阅读推广服务模式的具体实现途径。