|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies显示文摘This paper presents a comprehensive review of emerging technologies for the internet of things(IoT)-based smart agriculture.We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT,such as unmanned aerial vehicles,wireless technologies,open-source IoT platforms,software defined networking(SDN),network function virtualization(NFV)technologies,cloud/fog computing,and middleware platforms.We also provide a classification of IoT applications for smart agriculture into seven categories:including smart monitoring,smart water management,agrochemicals applications,disease management,smart harvesting,supply chain management,and smart agricultural practices.Moreover,we provide a taxonomy and a side-by-side comparison of the state-ofthe-art methods toward supply chain management based on the blockchain technology for agricultural IoTs.Furthermore,we present real projects that use most of the aforementioned technologies,which demonstrate their great performance in the field of smart agriculture.Finally,we highlight open research challenges and discuss possible future research directions for agricultural IoTs. | Othmane Friha Mohamed Amine Ferrag Lei Shu Leandros Maglaras Xiaochan Wang | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，4： | 15 |
| 2 | Parallel Manufacturing for Industrial Metaverses:A New Paradigm in Smart Manufacturing显示文摘To tackle the complexity of human and social factors in manufacturing systems, parallel manufacturing for industrial metaverses is proposed as a new paradigm in smart manufacturing for effective and efficient operations of those systems, where Cyber-Physical-Social Systems(CPSSs) and the Internet of Minds(Io M) are regarded as its infrastructures and the 'Artificial systems', 'Computational experiments'and 'Parallel execution'(ACP) method is its methodological foundation for parallel evolution, closed-loop feedback, and collaborative optimization. In parallel manufacturing, social demands are analyzed and extracted from social intelligence for product R&D and production planning, and digital workers and robotic workers perform the majority of the physical and mental work instead of human workers, contributing to the realization of low-cost, high-efficiency and zero-inventory manufacturing. A variety of advanced technologies such as Knowledge Automation(KA), blockchain, crowdsourcing and Decentralized Autonomous Organizations(DAOs) provide powerful support for the construction of parallel manufacturing, which holds the promise of breaking the constraints of resource and capacity, and the limitations of time and space. Finally, the effectiveness of parallel manufacturing is verified by taking the workflow of customized shoes as a case,especially the unmanned production line named Flex Vega. | Jing Yang Xiaoxing Wang Yandong Zhao | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，12： | 6 |
| 3 | Parallel Factories for Smart Industrial Operations:From Big AI Models to Field Foundational Models and Scenarios Engineering显示文摘The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence,internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems,QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called Eu Artisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0. | Jingwei Lu Xingxia Wang Xiang Cheng Jing Yang Oliver Kwan Xiao Wang | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，12： | 6 |
| 4 | Digital Twin for Human-Robot Interactive Welding and Welder Behavior Analysis显示文摘This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to enhance human users'operational productivity and comfort;while data-driven welder behavior analysis benefits to further novice welder training.This HRI system includes three modules:1)a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles;2)a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite;3)a DT system that is developed based on virtual reality(VR)as a digital replica of the physical human-robot interactive welding environment.The DT system bridges a human user and robot through a bi-directional information flow:a)transmitting demonstrated welding operations in VR to the robot in the physical environment;b)displaying the physical welding scenes to human users in VR.Compared to existing DT systems reported in the literatures,the developed one provides better capability in engaging human users in interacting with welding scenes,through an augmented VR.To verify the effectiveness,six welders,skilled with certain manual welding training and unskilled without any training,tested the system by completing the same welding job;three skilled welders produce satisfied welded workpieces,while the other three unskilled do not.A data-driven approach as a combination of fast Fourier transform(FFT),principal component analysis(PCA),and support vector machine(SVM)is developed to analyze their behaviors.Given an operation sequence,i.e.,motion speed sequence of the welding torch,frequency features are firstly extracted by FFT and then reduced in dimension through PCA,which are finally routed into SVM for classification.The trained model demonstrates a 94.44%classification accuracy in the testing dataset.The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training. | Qiyue Wang Wenhua Jiao Peng Wang YuMing Zhang | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，2： | 5 |
| 5 | The Metaverse of Mind:Perspectives on DeSci for DeEco and DeSoc显示文摘First of all, I would like to take this opportunity to express my sincere and deep thanks to our Editor-in-Chief, Professor Meng Chu Zhou, who took over my position after I was drafted for rejuvenating IEEE Transactions on Computational Social Systems in 2017. During the past five years, Meng Chu’s professional leadership and dedication has transformed IEEE/CAA Journal of Automatica Sinica(JAS) from its infancy to a young and high-impact publication in the world that is full of vitality and actively engaged by a group of talented and charged associate Ei Cs and editors, which is clearly demonstrated in Meng Chu’s farewell editorial [1]. I am very glad that Professor Qing-Long Han, an influential and leading scientist of the world-class in AI, control, automation, and intelligent science and technology from Australia, as well as a staunch supporter and great leader of this journal from its beginning, will take over the Ei C torch from Meng Chu next year, since I am extremely confident that our journal will reach a new high for its service and quality under his new leadership. | Fei-Yue Wang | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，12： | 4 |
| 6 | Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network显示文摘Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components.To surmount the springback occurring in sheet metal forming processes,numerous studies have been performed to develop compensation methods.However,for most existing methods,the development cycle is still considerably time-consumptive and demands high computational or capital cost.In this paper,a novel theory-guided regularization method for training of deep neural networks(DNNs),implanted in a learning system,is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter,e.g.,loading stroke,in sheet metal bending processes.By directly bridging the workpiece shape to the process parameter,issues concerning springback in the process design would be circumvented.The novel regularization method utilizes the well-recognized theories in material mechanics,Swift’s law,by penalizing divergence from this law throughout the network training process.The regularization is implemented by a multi-task learning network architecture,with the learning of extra tasks regularized during training.The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base,respectively.One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface.In this research,the neural models were found to outperform a traditional machine learning model,support vector regression model,in experiments with different amount of training data.Through a series of studies with varying conditions of training data structure and amount,workpiece material and applied bending processes,the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs,especially when only scarce and scattered experiment data are available for training which is often the case in practice.The theory-guided DNN could also be applicable to other sheet metal forming processes.It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry. | Shiming Liu Yifan Xia Zhusheng Shi Hui Yu Zhiqiang Li Jianguo Lin | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，3： | 2 |
| 7 | Editorial: Evolution from AI, IoT and Big Data Analytics to Metaverse显示文摘Time flies.Since I took over the Editor-in-Chief position from this journal’s founding Editor-in-Chief,Professor Fei-Yue Wang in 2018,five years has past just like a second.Looking back from today,as a team,we,including editorial board members,editorial staff members,our early career advisory board members,all contributing authors and all reviewers,should be proud of what this journal has achieved.This journal is the first of its kind of journals,resulting from IEEE’s collaboration with Chinese Association of Automation-an outside-USA professional organization/institution.We have overcome many barriers and difficulties,and well proven that we,united and working together,can accomplish a challenging mission.We indeed set a great example for IEEE and its many societies to pursue more and more collaboration with other publishers,organizations and institutions from not only China but also such countries as India,Brazil,and Japan. | MengChu Zhou | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，12： | 2 |
| 8 | 考虑细分市场的产品线设计生产模型及优化显示文摘不同细分市场对个性化产品日益增长的需求给产产线的设计和生产带来挑战。企业管理者需要同时考虑产产组件之间的兼容性、产品的差异化、产品的交货期以及细分市场中产品的竞争优势等因素。对上述问题构建考虑细分市场的产品线设计生产模型,以最大化产品单位成本效用。设计生物地理学优化算法进行求解,得到产品属性的配置决策、产品生产的外包决策以及细分市场中产产投放决策的优化方案。数值实验结果表明,同样的计算时间内生物地理学优化算法的性能要优于遗传算法。 | 柳春锋 杨萧 王居凤 | 2022 | 信息与管理研究2022，7，6： | 0 |
| 9 | 基于混合进化算法的卫星网络星间数传方法显示文摘星间链路在卫星网络数据传输中发挥着非常重要的作用,可以解决我国地面站布局受限的问题。然而,卫星网络拓扑时变,网络资源有限,使得星间数据传输具有很大的挑战性。为了克服这个难点,首先利用存储时间聚合图建模卫星网络,在考虑网络资源约束的条件下,构建了数据传输整数规划模型。然后,设计了知识型混合进化算法(knowledge-guided hybrid evolutionary algorithm,KGHEA)对模型进行求解,该算法融合了局部搜索算法、路径流量分配算法,以及多种知识型算子。最后,设计了仿真实验,验证了KGHEA的性能,并分析了各项参数对数据传输性能的影响,为星间网络建设提供参考。 | 邓勇 姚锋 邢立宁 何磊 | 2023 | 系统工程与电子技术2023，45，9： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员