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Optimal production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Machine learning and deep learning techniques for poultry tasks management: a review

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Multimedia Tools and Applications 2025年第2期84卷 603-645页

作者： Subramani, Thavamani. Jeganathan, Vijayakumar. Kunkuma Balasubramanian, Sruthi. Department of Electronics and Instrumentation Bharathiar University Tamil Nadu Coimbatore641046 India

In recent years the poultry production industry has adopted automation with the help of different kinds of technological advancements like verities of monitoring and sensing tools, IoT devices, sensors, monitoring devices, and more. These advanced techniques will offer numerous advantages in poultry product production. Only with human resources based large-scale poultry production is not an easy task because the public health threads from ingesting foods with high antibiotic remains will residues a problem. Sometimes, zoonotic diseases and foodborne diseases present a crucial task to poultry producers. These repeated tasks concern massive and hazardous work accomplished within a suffering work domain, leading to high human health safety, labor cost, and sustainable risk of cross-infection through manufacturing facilities. In recent years, Artificial Intelligence technology has played a vital role in all sectors including poultry production has utilized advanced Machine Learning and Deep Learning technologies which is a subfield of AI, this technology has an acceptable potential to manage numerous challenges in the establishment of information-based farming and various task-handling systems in the poultry production sector. This article comprehensively reviews some recently established machine learning and deep learning algorithms for poultry management tasks like chicken activity monitoring, farm weather monitoring and control, weight prediction, earlier identification of diseased chickens, and more. It is divine that this proposed review work will compose a piece of helpful information for all who are interested in enhancing recognition of the future scope of utilizing the ML and DL techniques in the poultry production sectors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Investigating Research Trends in Computer Vision for Food Quality control: A Bibliometric Approach 6

Investigating Research Trends in Computer Vision for Food Qu...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Lysova, Natalya Solari, Federico Montanari, Roberto Department of Engineering for Industrial Systems and Technologies University of Parma Parco Area delle Scienze 181/A Parma43124 Italy

The production throughputs of the food industry grow continuously due to the ever-increasing customer demand. At the same time, food safety measures become increasingly strict. To this end, efficient quality control is essential to ensure quality and safety for consumers, and to guarantee efficient and valuable production for industrial stakeholders. Computer vision is often applied to this end, usually integrated with other Industry 4.0 technologies and process automation. Computer vision applications in quality control, indeed, can greatly enhance supply chain management in the food industry by maintaining consistent product quality and minimizing waste. In this paper, a keyword-based analysis is carried out to assess the main research trends and topics in the field of computer vision for quality control in the food industry. The bibliometric analysis aims to assess the main applications of the technology in terms of processes and products and the main issues targeted by the analyses. Finally, the results of the bibliometric investigation allow to identify and highlight topics that present potential for future investigations in the field. © 2024 The Authors. Published by Elsevier B.V.

关键词： Food industry

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Audit framework for control Robotic process automation projects 16

Audit framework for control Robotic Process Automation proje...

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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Patrício, Leonel Silva, José Costa, Carlos Varela, Leonilde Silveira, Zilda Cruz-Cunha, Maria Manuela Universidade Do Minho Department of Production and Systems Guimarães4804-533 Portugal ALGORITMI Research Centre Universidade Do Minho Guimarães4804-533 Portugal University of Aveiro Department of Economics Management Industrial Engineering and Tourism Aveiro3810-193 Portugal Bambuí Brazil Department of Mechanical Engineering Sao Carlos School of Engineering University of Sao Paulo Sao Paulo Brazil 2Ai-Applied Artificial Intelligence Laboratory Polytechnic Institute of Cávado and Ave Portugal

This study aims to review existing literature on audits within the technological domain, with a specific focus on Robotic process automation (RPA), in order to develop a framework tailored to the RPA technology lifecycle for effective audit control. The methodology involves a systematic review of pertinent literature sourced from academic and scientific databases, followed by an analysis to identify gaps. A proposed framework, aligned with the RPA technology lifecycle, integrates essential criteria for conducting audits focused on RPA project control, thereby addressing identified deficiencies. The results of this synthesis highlight areas for further investigation and the development of a comprehensive framework that caters to the specific needs of RPA projects. In conclusion, this research consolidates pertinent findings from the literature review and introduces a novel framework aligned with the RPA technology lifecycle, integrating crucial audit criteria. The ultimate goal is to bridge research gaps and offer practical guidance to RPA technology users, thereby enhancing project control and performance. © 2025 The Author(s).

关键词： Project management

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DYNAMIC SCHEDULING OF MULTI-AGENT ELECTROMECHANICAL production LINE BASED ON BIOLOGICAL ITERATIVE ALGORITHM

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Scalable Computing 2025年第3期26卷 1326-1335页

作者： Zhang, Yan Li, Zipeng Yellow River Conservancy Technical Institute Department of mechanical engineering Henan Kaifeng475000 China

In order to solve the dynamic job scheduling problem in current intelligent machining systems, the author proposes a multi-agent electromechanical production line dynamic scheduling based on iterative algorithms. The author designed a collaborative control method for hybrid micro assembly production lines based on multi-agent technology. Firstly, a mathematical model is used to describe the collaborative control objectives of the production line, and a hybrid micro assembly production line information collection and integration framework is constructed to obtain production line information. By combining the dynamic coordination performance of multi-agent technology, a collaborative control model for production lines is constructed, with the goal of minimizing processing costs as the collaborative control objective. The optimal collaborative control scheme is solved to achieve collaborative control of hybrid micro assembly production lines. The experimental results show that compared with traditional methods, the collaborative control task allocation time obtained by applying this method is shorter, with a minimum value of 15.38 seconds, indicating that this method has higher efficiency in collaborative control of production lines. Compared with traditional methods, the collaborative control task allocation time after applying this method is shorter, effectively reducing the production line processing cost, proving the feasibility of this method. © 2025 SCPE

关键词： Scheduling algorithms

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Machine-learning-based sampling inspection under OQC capacity for real-time quality monitoring in the TFT-LCD industry

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International Journal of production Research 2025年第6期63卷 2090-2113页

作者： Shih, Ming-Sung Chen, James C. Chen, Tzu-Li Chiang, Chih-Hsiung Hsu, Ching-Lan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei Taiwan Division of Quality Control AUO Corporation Taichung Taiwan

In the thin film transistor-liquid crystal display (TFT-LCD) industry, the competition is fierce, and the external failure cost is high. To improve the quality of released products, sampling inspection under outgoing quality control (OQC) is conducted before products are released. Owing to the variety and complexity of inspection items, the task must be carried out manually while realising automation is difficult. Traditional random sampling inspection is limited by sampling quantity, manpower, and material resources. To effectively improve acceptable quality levels, this paper proposes a quality predictive monitoring framework tailored to the production and inspection characteristics of the TFT-LCD industry. This framework includes real-time automatic quality monitoring based on an optimal machine learning prediction model with two-phase feature creation, and it also addresses OQC inspection capacity constraints. Experimental results show the prediction performance based on proposed model is better than the traditional random sampling process. In practice, superior average outgoing quality improves by more than 70% at the same sampling level. In addition, the features of the process can be further controlled and managed based on the prediction model to monitor the key parameters in real time and respond to abnormalities quickly, thereby further improving the quality of released products. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liquid crystal displays

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Data-Driven Fault-Tolerant Bipartite Consensus Tracking for Multi-Agent Systems With a Non-Autonomous Leader

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 279-281页

作者： Yan Zhou Guanghui Wen Jialing Zhou Tao Yang the Department of Systems Science Southeast University the State Key Laboratory of CNS/ATM Beijing Institute of Technology the Beijing Institute of Technology (Zhuhai) the State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the continuous fault-tolerant control protocol via observer design is developed. In addition, it is strictly proved that the multi-agent system driven by the designed controllers can still achieve bipartite consensus tracking after faults occur.

关键词： Time varying control systems

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An investigation on real-time insights: enhancing process control with IoT-enabled sensor networks

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Discover Internet of Things 2025年第1期5卷 1-17页

作者： Pandey, Shivam Chaudhary, Mahi Tóth, Zsolt Department of Chemistry School of Applied and Life Sciences Uttaranchal University Dehradun248007 India Faculty of Wood Engineering and Creative Industries Institute of Basic Sciences University of Sopron Bajcsy-Zsilinszky u. 4. Sopron Hungary

The Internet of Things (IoT) and sensor networks have significantly advanced process monitoring and control in multiple sectors, including manufacturing, agriculture, healthcare, and smart cities. Given the substantial volume of IoT-generated data, design-oriented solutions are necessary to accelerate data processing and improve scalability and flexibility. This article provides a comprehensive examination of the principal applications, technology, challenges, and future trajectories of IoT and sensor networks in process control as the control and monitoring of processes across industries are being transformed by the accelerated development of the IoT and sensor networks. Nevertheless, its pervasive adoption is impeded by obstacles such as data overload, scalability, and energy efficiency. This study is an investigation that presents a comprehensive framework for the integration of IoT-enabled sensor networks into real-time process control systems, thereby addressing these issues. We illustrate methods to enhance the collection, processing, and decision-making processes by studying advanced communication protocols such as MQTT and CoAP and key enabling technology of sensor networks. By conducting case studies of smart cities, agriculture, healthcare, and production, this investigation has extensive implications, enabling various industries to achieve enhanced operational efficiencies, robust scalability, and improved safety. Despite significant breakthroughs, challenges such as security, energy efficiency, and scalability remain to be addressed. Finally, we have presented advanced technologies capable of addressing these challenges and shaping the trajectory of IoT-based process management systems, including block chain, 5G, and AI integration and discussed future implication of study. © The Author(s) 2025.

关键词： process control

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Optimizing cooling approach of spiral coil for an electromagnetic steel teeming system of ladle in continuous casting production

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Journal of Iron and Steel Research International 2025年第1期32卷 85-94页

作者： Ming He Qing-wei Wang Li-jia Zhao Wang-zhong Mu Xing-an Liu Qiang Wang College of Horticulture Shenyang Agricultural UniversityShenyang110866LiaoningChina Key Laboratory of Electromagnetic Processing of Materials(Ministry of Education) Northeastern UniversityShenyang110819LiaoningChina Key Laboratory of Protected Horticulture(Ministry of Education) Shenyang Agricultural UniversityShenyang110866LiaoningChina Department of Materials Science and Engineering KTH Royal Institute of TechnologyStockholm10044Sweden

To address the current issues with the conventional slide gate system utilized in the steel teeming process,a unique electromagnetic induction controlled automated steel teeming(EICAST)technology has been *** means of spiral coil in this technology is directly related to its service ***,heat transfer processes of air cooling and spray cooling were compared and ***,the impacts of water temperature,water flow rate and air flow rate were examined in order to maximize the spray cooling *** maintain coil temperature at a low value consistently throughout the entire thermal cycle process of the ladle,a combined cooling mode was finally *** simulation was applied to examine the coil temperature variation with different cooling systems and *** coil operation,spray cooling is said to be more *** controlling the water flow rate and air flow rate,the spray cooling effect is ***,water temperature has little or no impact when using spray *** cooling during the secondary refining process and spray cooling prior to coil operation are combined to further lower coil *** the direction of the spray cooling is from bottom to top,the coil temperature is lowered below 165℃.A practical induction coil cooling plan was provided for the EICAST technology’s production process.

关键词： Electromagnetic induction controlled automated steel teeming technology Spiral coil Spray cooling Heat transfer Continuous casting

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Hierarchical Data-driven Scheduling-control Framework for Energy-efficient Industrial Flexible production 24

Hierarchical Data-driven Scheduling-Control Framework for En...

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2nd International Conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Jiang, Yuliang Zhu, Shanying Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400710681

This paper proposes a hierarchical data-driven scheduling-control framework for a multi-machine/mode production system to operate in an energy-efficient manner. At the scheduling layer, the scheduling problem incorporating process dynamics is formulated into a quadratic programming to arrange candidate processing machines/modes and corresponding production objectives. Based on the dynamic linearization model, scheduling results are executed by model-free adaptive control at the control layer. The real-time process output is feedback to the scheduling layer for the next decision-making. The practicality of the proposed framework is validated in a thread-tapping process featuring four distinct operational modes. On average, operating in a single mode maintains output accuracy while saving 14.2% on energy. The multi-mode operation further optimizes energy usage through its capability for dynamic mode switching. © 2024 Copyright held by the owner/author(s).

关键词： Quadratic programming

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