Construction and Implementation of Multi Collaborative Service Platform Based on Industrial Competitive Intelligence

With the economic globalization and the increasingly fierce industrial competition at home and abroad, the importance of industrial competitive intelligence service is becoming increasingly prominent. Under the policy background of cooperation and sharing, pluralistic coordination has become a new trend in regional economic development. The multi collaborative online service platform of industrial competitive intelligence is jointly constructed by all service subjects. The platform is guided and promoted by the government. Colleges and universities provide support for industrial competitive intelligence theory and professionals, scientific research institutes provide talent and advanced technology support, industry associations are responsible for dynamic monitoring of industrial development, and profit-making institutions are responsible for supplementing industrial competitive intelligence achievements. All service subjects integrate and explore existing intelligence resources and services through the unified online industrial competitive intelligence sharing platform, so as to realize benign cooperation, collaborative management, resource integration, user integration and service integration among subjects, so as to realize multiple collaborative services of industrial competitive intelligence.

Multiple Collaborative Service Model and System Construction Based on Industrial Competitive Intelligence

This paper constructs a multiple collaborative service model of industrial competition intelligence with the main purpose of promoting the development of regional industries. The multiple service subjects include enterprises, governments, colleges and universities, scientific research institutes, industry associations and for-profit institutions. This article starts from the overall development of regional industrial economy, weighs the mutual relationship between the elements of the service model, and promotes multiple service subjects such as enterprises, governments, universities, research institutes, industry associations, and profit-making organizations to realize the collaborative service of resource intelligence, demand intelligence and data intelligence provides linkage intelligence service for the development and innovation of regional industries. This service model can improve the efficiency of industrial competitive intelligence services and the overall competitiveness of regional industries.

Deep Reinforcement Learning-Based Collaborative Routing Algorithm for Clustered MANETs

Flexible adaptation to differentiated quality of service(QoS)is quite important for future 6G network with a variety of services.Mobile ad hoc networks(MANETs)are able to provide flexible communication services to users through self-configuration and rapid deployment.However,the dynamic wireless environment,the limited resources,and complex QoS requirements have presented great challenges for network routing problems.Motivated by the development of artificial intelligence,a deep reinforcement learning-based collaborative routing(DRLCR)algorithm is proposed.Both routing policy and subchannel allocation are considered jointly,aiming at minimizing the end-to-end(E2E)delay and improving the network capacity.After sufficient training by the cluster head node,the Q-network can be synchronized to each member node to select the next hop based on local observation.Moreover,we improve the performance of training by considering historical observations,which can improve the adaptability of routing policies to dynamic environments.Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion.In addition,the effectiveness of the routing policy in a dynamic environment is verified.

Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep learning approaches for capturing user and product information from a short text.However,such previously used approaches do not fairly and efficiently incorporate users’preferences and product characteristics.The proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations.To overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or not.The proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed problems.Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.

Artificial Intelligence and the Sustainable Development Goals: An Exploratory Study in the Context of the Society Domain

Artificial Intelligence (AI) experienced significant advancements in recent years, and its potential power is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential influence of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather information on GPT-3’s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

Collaborative Clustering Parallel Reinforcement Learning for Edge-Cloud Digital Twins Manufacturing System

To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments.

Optimal edge-cloud collaboration based strategies for minimizing valid latency of railway environment monitoring system

Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.

Global Collaboration in Artificial Intelligence:Bibliometrics and Network Analysis from 1985 to 2019

Purpose:This study aims to explore the trend and status of international collaboration in the field of artificial intelligence(AI)and to understand the hot topics,core groups,and major collaboration patterns in global AI research.Design/methodology/approach:We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science(WoS)and studied international collaboration from the perspectives of authors,institutions,and countries through bibliometric analysis and social network analysis.Findings:The bibliometric results show that in the field of AI,the number of published papers is increasing every year,and 84.8%of them are cooperative papers.Collaboration with more than three authors,collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns.Through social network analysis,this study found that the US,the UK,France,and Spain led global collaboration research in the field of AI at the country level,while Vietnam,Saudi Arabia,and United Arab Emirates had a high degree of international participation.Collaboration at the institution level reflects obvious regional and economic characteristics.There are the Developing Countries Institution Collaboration Group led by Iran,China,and Vietnam,as well as the Developed Countries Institution Collaboration Group led by the US,Canada,the UK.Also,the Chinese Academy of Sciences(China)plays an important,pivotal role in connecting the these institutional collaboration groups.Research limitations:First,participant contributions in international collaboration may have varied,but in our research they are viewed equally when building collaboration networks.Second,although the edge weight in the collaboration network is considered,it is only used to help reduce the network and does not reflect the strength of collaboration.Practical implications:The findings fill the current shortage of research on international collaboration in AI.They will help inform scientists and policy makers about the future of AI research.Originality/value:This work is the longest to date regarding international collaboration in the field of AI.This research explores the evolution,future trends,and major collaboration patterns of international collaboration in the field of AI over the past 35 years.It also reveals the leading countries,core groups,and characteristics of collaboration in the field of AI.

Frontiers of collaborative intelligence systems

The development of information technology has propelled technological reform in artificial intelligence(AI).To address the needs of diversified and complex applications,AI has been increasingly trending towards intelligent,collaborative,and systematized development across different levels and tasks.Research on intelligent,collaborative and systematized AI can be divided into three levels:micro,meso,and macro.Firstly,the micro-level collaboration is illustrated through the introduction of swarm intelligence collaborative methods related to individuals collaboration and decision variables collaboration.Secondly,the meso-level collaboration is discussed in terms of multi-task collaboration and multi-party collaboration.Thirdly,the macro-level collaboration is primarily in the context of intelligent collaborative systems,such as terrestrial-satellite collaboration,space-air-ground collaboration,space-air-ground-air collaboration,vehicle-road-cloud collaboration and end-edge-cloud collaboration.Finally,this paper provides prospects on the future development of relevant fields from the perspectives of the micro,meso,and macro levels.

Edge-Cloud Collaborative Optimization Scheduling with Micro-Service Architecture

The architecture of edge-cloud cooperation is proposed as a compromising solution that combines the advantage of MEC and central cloud. In this paper we investigated the problem of how to reduce the average delay of MEC application by collaborative task scheduling. The collaborative task scheduling is modeled as a constrained shortest path problem over an acyclic graph. By characterizing the optimal solution, the constrained optimization problem is simplified according to one-climb theory and enumeration algorithm. Generally, the edge-cloud collaborative task scheduling scheme performance better than independent scheme in reducing average delay. In heavy workload scenario, high blocking probability and retransmission delay at MEC is the key factor for average delay. Hence, more task executed on central cloud with abundant resource is the optimal scheme. Otherwise, transmission delay is inevitable compared with execution delay. MEC configured with higher priority and deployed close to terminals obtain more performance gain.

A Multi-Agent Reinforcement Learning-Based Collaborative Jamming System: Algorithm Design and Software-Defined Radio Implementation

In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect.

An Intelligent Control Method for the Low-Carbon Operation of Energy-Intensive Equipment

Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning,linking control and optimization with prediction,and integrating decision-making with control.This method,which consists of setpoint control,self-optimized tuning,and tracking control,ensures that the energy consumption per tonne is as low as possible,while remaining within the target range.An intelligent control system for low-carbon operation is developed by adopting the end-edge-cloud collaboration technology of the Industrial Internet.The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.

A Survey on Collaborative DNN Inference for Edge Intelligence

With the vigorous development of artificial intelligence(AI),intelligence applications based on deep neural networks(DNNs)have changed people’s lifestyles and production efficiency.However,the large amount of computation and data generated from the network edge becomes the major bottleneck,and the traditional cloud-based computing mode has been unable to meet the requirements of realtime processing tasks.To solve the above problems,by embedding AI model training and inference capabilities into the network edge,edge intelligence(EI)becomes a cutting-edge direction in the field of AI.Furthermore,collaborative DNN inference among the cloud,edge,and end devices provides a promising way to boost EI.Nevertheless,at present,EI oriented collaborative DNN inference is still in its early stage,lacking systematic classification and discussion of existing research efforts.Motivated by it,we have comprehensively investigated recent studies on EI-oriented collaborative DNN inference.In this paper,we first review the background and motivation of EI.Then,we classify four typical collaborative DNN inference paradigms for EI,and analyse their characteristics and key technologies.Finally,we summarize the current challenges of collaborative DNN inference,discuss future development trends and provide future research directions.

Research on reconfigurable collaborative remote diagnosis system

The function-layer model and working model of collaborative remote fault diagnosis system （FDS）, which includes three layers： task layer, collaboration layer and diagnosing layer, are proposed. The running mechanism of the system is discussed. A collaborative FDS may consist of several subsystems running at different places and the subsystem consists of several fimction modules. A structure centered on data-bus is adopted in subsystem. All the function modules in subsystem are encapsulated into software intelligent chips （SICs） and SIC can but connect with data-bus. So, it is feasible to reuse these diagnosis fimction modules and the structure of subsystem in different diagnosis applications. With the reconfigurable SICs, several different function modules can reconstruct quickly some different diagnosis subsystems in different combinations, and some subsystems can also reconfigure a specified collaborative FDS.

Collaborative Design Theory and Related Key Technology Study Based on Cloud Computing

Analyzes the main way of product distribution for collaborative design. According to the requirement of manufacturing collaborative design, apply cloud computing in manufacturing collaborative design and come up the concept of product collaborative cloud design. Study the product collaborative design theory based on cloud computing and the general key technology of cloud computing, semantic web, intelligent matching selection algorithm, STEP and XML technology, version management and conflict resolution arithmetic and so on which related to this theory. The study object of this article is automotive product. Construct an automotive collaborative design system with the key technology to verify the feasibility and validity of the cloud basing collaborative design theory and related technology. This collaborative design system will overcome the weakness that resource and information can not be shared between different department in the same enterprise or different enterprises. Join up this system will help directly enterprise for collaborative design and the repetition construction of collaborative design platform of each enterprise will be avoid. It will reduce the investment of enterprises for constructing and managing collaborative design platform and further reduce the cost of product R&D with a better and more efficient design.

A New Generation of Intelligent Mapping and Remote Sensing Scientific Test Satellite Luojia-301

With the continuous improvement of the performance and the increasing variety of optical mapping and remote sensing satellites,they have become an important support for obtaining global accurate surveying and mapping remote sensing information.At present,optical mapping and remote sensing satellites already have sub-meter spatial resolution capabilities,but there is a serious lag problem in mapping and remote sensing information services.It is urgent to develop intelligent mapping and remote sensing satellites to promote the transformation and upgrading to real-time intelligent services.Firstly,based on the three imaging systems of the optical mapping and remote sensing satellites and their realization methods and application characteristics,this paper analyzes the applicable system of the intelligent mapping and remote sensing satellites.Further,according to the application requirements of real-time,intelligence,and popularization,puts forward the design concept of integrated intelligent remote sensing satellite integrating communication,navigation,and remote sensing and focuses on the service mode and integrated function composition of intelligent remote sensing satellite.Then expounds on the performance and characteristics of the Luojia-301 satellite,a new generation of intelligent surveying and mapping remote sensing scientific test satellite.And finally summarizes and prospects the development and mission of intelligent mapping remote sensing satellites.Luojia-301 satellite integrates remote sensing and communication functions.It explores an efficient and intelligent service mode of mapping and remote sensing information from data acquisition to the application terminal and provides a real service verification platform for on-orbit processing and real-time transmission of remote sensing data based on space-ground internet,which is of great significance to the construction of China’s spatial information network.

数智时代人机协同的研究现状与未来方向

数智时代因人机协同情境的普遍存在而出现新的决策范式。本专栏旨在促进学者研究与人机协同中的管理决策与组织行为有关的重要问题。尽管在20世纪人工智能的起始阶段学者注意到人与机器共同决策的重要问题,但因人工智能技术在很长时间内没有取得重要突破而没有被继续关注。伴随着人工智能的广泛应用以及认知科学在过去几十年取得的长足进步,学界研究人机协同中的决策和行为问题有了丰富的数据、场景、理论和方法。同时,实践界对于人机协同的新现象还存在认识上的不足和适应性困难,阻碍了企业和组织成员借助数智技术实现智能增强。本文总结了在商业与管理、计算机和心理学领域的学者所从事的有关人们对于机器算法的信任或依赖的研究,特别介绍了在企业情境下开展的人机协同研究。在简要介绍本专栏收录的七篇文章的主要研究发现后,我们指出了未来人机协同研究的重要方向,旨在倡导学界同行开展深入的研究,既能够建立数智化情境下的人机协同理论,又为中国企业顺利实现数智化转型提供参考。

智能采煤机器人关键技术

采煤机是综采工作面的核心装备,研发智能采煤机器人是实现综采工作面智能化的关键。综合分析当前采煤机机器人化研究进程中的传感检测、位姿控制、速度控制、截割轨迹规划与跟踪控制等技术的研究现状,提出研发智能采煤机器人必须破解的“智能感知、位姿控制、速度控制、截割轨迹规划与跟踪控制、位-姿-速协同控制”五大关键技术,并给出解决方案。针对智能感知问题,提出了构建智能感知系统思路,给出了智能采煤机器人智能感知系统的架构,实现对运行状态、位姿、环境等全面感知,为智能采煤机器人安全、可靠运行提供保障;针对位姿控制问题,提出了智能PID位姿控制思路,给出了改进遗传算法的PID位姿控制方法,实现了智能采煤机器人位姿精准控制;针对速度控制问题,提出了融合“力-电”异构数据的截割载荷测量思路,给出了基于神经网络算法的截割载荷测量方法,实现了截割载荷的精准测量;提出牵引与截割速度自适应控制思路,给出了人工智能算法牵引与截割速度决策方法和滑模自抗扰控制的牵引与截割速度控制方法,实现了智能采煤机器人速度精准自适应控制;针对截割轨迹规划与跟踪控制问题,提出了截割轨迹精准规划思路,给出了融合地质数据和历史截割数据的截割轨迹规划模型,实现了截割轨迹的精准规划;提出了截割轨迹精准跟踪控制思路,给出了智能插补算法的截割轨迹跟踪控制方法,实现了智能采煤机器人截割轨迹高精度规划与精准跟踪控制;针对“位-姿-速”协同控制问题,提出了“位-姿-速”协同控制参数智能优化思路,给出了基于多系统互约束的改进粒子群“位-姿-速”协同控制参数优化方法,实现了智能采煤机器人智能高效作业。深入研究五大关键技术破解思路,有利于加快推动研发高性能、高效率、高可靠的智能采煤机器人。

智能助产术教学法——以“智能苏格拉底会话机器人”教学实践为例

当前,生成式人工智能与学生的人机会话主要是“知识讲述”型会话关系,这会影响学生的高阶思维能力发展。解决这一问题的关键是,如何将人机“知识讲述”型会话关系,转变为“知识转化”型会话关系。为此,研究者以助产术理论、ChatGPT、学习分析和腾讯QQ工具为基础,探索智能助产术教学法的学习发生机制,开发智能苏格拉底会话机器人,构建智能助产术教学模式。本研究采用准实验方法,以“远程教育学”课程为教学内容,以教育技术学专业本科生为对象,开展以智能会话机器人支持的教学实验。实验结果表明,智能助产术教学与直接使用ChatGPT的教学相比,能显著提升学生的问题解决能力、创新能力和协作学习能力,但是对学习绩效、批判性思维能力和自我效能感的影响不显著。为提高教学效果,研究者需提升计算机的系统算力,开发批判性思维学习支架,构建人机适切性互动机制,研制自我效能感提升策略。

人机协同的大学生个性化教育评价方法研究

大学生个性化培养是实现因材施教人才培养目标的重要内容,而个性化教育的智能化评价是衡量培养质量的有效手段,更是智能技术与教育结合的必然要求。针对传统个性化教育评价中数据采集获取难、评价过程环节多、指标数据粒度粗、实时反馈效果差,难以应对规模化、过程性、全方位、个性化发展评价难题,提出人机协同的大学生个性化教育评价方法。首先明确了人工智能技术赋能个性化教育评价的内容,然后提出了人机共建个性化教育评价指标体系与人机协同实施过程,最后展示了人机协同个性化教育评价的典型案例。人机协同的智能化评价手段,具有全天候、超时长的服务能力,可大幅提升评价的效率与质量,是新时代教育评价改革成功的重要手段,是实现教育评价现代化的有效途径。