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 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.

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.

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.

Multi-machine collaboration realization conditions and precise and efficient production mode of intelligent agricultural machinery

Multi-machine collaboration of agricultural machinery is one of the international frontier and hot research in the field of agricultural equipment.However,the current domestic multi-machine collaborative operation of agricultural machinery is limited to the research of task goal planning and collaborative path optimization in a single production link.In order to achieve the purpose of zero inventory of agricultural materials and precise and efficient production operations,a new technology of agricultural machinery multi-machine collaboration with multi-dimension and full chain was proposed,which takes into account the whole process of agricultural production,as well as agricultural machinery system and external supply chain,storage and transportation chain collaboration.The problems of data collaboration,process collaboration and organization collaboration were analyzed.And the realization conditions of new multi-machine cooperative technology were analyzed.Meanwhile,the zero inventory mode and precise operation mode of agricultural materials under the background of multi-machine cooperation of intelligent agricultural machinery were studied.Then,a precise and efficient agricultural production mode based on data-process-organization collaboration was constructed.The results showed that the multi-machine cooperative technology mode of multi-dimensional and full-chain agricultural machinery could greatly improve the efficiency of agricultural machinery,operation quality,land utilization rate and reduce production cost.

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

As a starting point in equipment manufacturing,sawing plays an important role in industrial production.Intelligent manufacturing equipment is an important carrier of intelligent manufacturing technologies.Due to the backwardness of intelligent technology,the comprehensive performance of sawing equipments in China is obviously different from that in foreign countries.State of the art of advanced sawing equipments is investigated along with the technical bottleneck of sawing machine tool manufacturing,and a new industrial scheme of replacing turning-milling by sawing is described.The key technologies of processing-measuring integrated control,multi-body dynamic optimization,the collaborative sawing network framework,the distributed cloud sawing platform,and the self-adapting service method are analyzed;with consideration of the problems of poor processing control stableness,low single machine intelligence level,no on-line processing data service and active flutter suppression of sawing with wide-width and heavy-load working conditions.Suggested directions for further research,industry implementation,and industry-research collaboration are provided.

PERFORMANCE-BASED INTELLIGENT RESOURCE DESCRIPTION MODEL FOR INTERNET-BASED PRODUCT DESIGN

Issues on intelligent resource description and multiple intelligent resources integration for lntemet based collaborative design are analyzed. A performance-based intelligent resource description model for lnternet-based product design is proposed, which can help to create, store, manipulate and exchange intelligent resource description information for applications, tools and systems in Interact-based product design. A method to integrate multiple intelligent resources to fulfill a complex product design and analysis via lntemet is also proposed. A real project for improving the bearing system design of a turbo-expander with many intelligent resources in prominent universities is presented as a case study.

Data Logic Structure and Key Technologies on Intelligent High-precision Map

Taking autonomous driving and driverless as the research object,we discuss and define intelligent high-precision map.Intelligent high-precision map is considered as a key link of future travel,a carrier of real-time perception of traffic resources in the entire space-time range,and the criterion for the operation and control of the whole process of the vehicle.As a new form of map,it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps.Thus,it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map.Accordingly,we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application.Next,we put forward the data logic structure of intelligent high-precision map,and analyze its application in autonomous driving.Then,we summarize the computing mode of“Crowdsourcing+Edge-Cloud Collaborative Computing”,and carry out key technical analysis on how to improve the quality of crowdsourced data.We also analyze the effective application scenarios of intelligent high-precision map in the future.Finally,we present some thoughts and suggestions for the future development of this field.

Empowering digital twins with eXtended reality collaborations

Background The advancements of Artificial Intelligence,Big Data Analytics,and the Internet of Things paved the path to the emergence and use of Digital Twins(DTs)as technologies to“twin”the life of a physical entity in different fields,ranging from industry to healthcare.At the same time,the advent of eXtended Reality(XR)in industrial and consumer electronics has provided novel paradigms that may be put to good use to visualize and interact with DTs.XR technologies can support human-to-human interactions for training and remote assistance and could transform DTs into collaborative intelligence tools.Methods We here present the Human Collaborative Intelligence empowered Digital Twin framework(HCLINT-DT)integrating human annotations(e.g.,textual and vocal)to allow the creation of an all-in-one-place resource to preserve such knowledge.This framework could be adopted in many fields,supporting users to learn how to carry out an unknown process or explore others’past experiences.Results The assessment of such a framework has involved implementing a DT supporting human annotations,reflected in both the physical world(Augmented Reality)and the virtual one(Virtual Reality).Con-clusions The outcomes of the interface design assessment confirm the interest in developing HCLINT-DT-based applications.Finally,we evaluated how the proposed framework could be translated into a manufacturing context.

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.

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。

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。

多机协同智能发展战略研究

多个自主智能系统通过信息、行为交互构成的多机协同智能,代表着未来智能系统的必然发展趋势,是我国新一代人工智能规划部署的主攻方向,也是支撑国防、社会安全的核心技术和推动制造业由大到强的必由之路。开展突破多机协同智能技术发展研究,对于推动我国军事智能、智能产业高质量发展、加快工业转型升级具有重要意义。本文基于多机协同智能系统当前面临的难以适应复杂任务这一挑战,从基础理论和核心关键技术两个层面出发,系统地梳理了多机协同智能的研究现状,分析了制约基础理论与关键技术发展的主要瓶颈性问题,并以多机协同智能制造为典型应用,剖析理论与技术发展中存在的问题。研究认为,多机协同智能将朝着人机群组智能的方向发展,为抢占发展先机,需及早布局人机群组智能的基础理论探索,加速核心技术突破,并加快应用示范。

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。

人机争论探究法:一种争论式智能会话机器人支持的学生高阶思维能力培养模式探索

大量学生利用生成式人工智能生成作业,导致了学生高阶思维能力难以有效培养。在人机会话中嵌入“争论噪音”,将是解决这一问题的可能途径。研究采用准实验研究设计,基于高阶思维、建构主义和争论学习理论,利用文心一言、学习分析和腾讯QQ等技术工具,开发了争论式智能会话机器人,构建了人机争论探究法教学模式。结果表明,实验组的学习成绩、批判性思维能力、问题解决能力和学习态度显著优于对照组,但是创新能力效果不显著。为提高人机争论学习效果,教师需要构建特定的高阶思维学习支架,优化人机争论学习的算法机制,培养学生的人机争论素养,研制师生机三元协同争论机制。

煤矿智能快速掘进关键技术研究现状及展望

针对煤矿巷道掘进智能化建设情况,探讨了现阶段分别以连采机、掘锚一体机、全断面掘进系统、掘锚机为核心的四大类智能快速掘进成套装备发展现状,分析了四大类智能快掘装备与地质条件适用性,提出了实现煤矿智能化掘进需要解决的关键技术的问题,探讨了以信息传输与智能分析技术、智能感知技术、精确定位导航技术,自主定型定向截割技术、掘进机远程自主截割控制技术以及多级多工序智能协同控制技术为基础的智能化掘进实现途径;基于复杂地质条件下煤矿巷道掘进亟待解决的关键问题,提出了以掘进作业功能需求、掘进工艺要求为依据的系统化分析、模块化设计、功能优化组合的设计方法;研究了以虚控实、虚实结合的多级数字孪生构架,基于掘进巷道设备、环境、地质构造多维信息再现实现掘进机远程控制;提出了单机智能化、系统协同化、井下智控与地面远程监控相协同的控制方法。开发了新型的无重复碾压柔性超前支护技术、截割部多级伸缩截割技术、智能锚固机组多钻机并行作业等技术,研制了具有掘、支、锚、运、探多工序并行作业的新型快速掘进作业联合机组,提出了掘进机器人、超前支护机器人、锚固机器人、辅助运输机器人组柔性并行作业技术;提出了基于数据驱动的多机多工序协同控制行为规划决策方法。提出构建完整的煤矿井下跨系统全时空信息数字感知体系,构建集井下现场生产状态、掘进巷道空间信息、掘进装备状态、风险信息等多参量、多尺度、全时空特性的数据感知智能化监控平台,实现虚拟系统与实际掘进系统实时通信,通过数据驱动规划分析实现智能化、少人化掘进。