The Experts below are selected from a list of 13422 Experts worldwide ranked by ideXlab platform

Pingyu Jiang - One of the best experts on this subject based on the ideXlab platform.

  • Social Manufacturing Paradigm: Concepts, Architecture and Key Enabled Technologies
    Social Manufacturing: Fundamentals and Applications, 2019
    Co-Authors: Pingyu Jiang
    Abstract:

    Following up the analysis based on the connecting and communicating behaviors of human beings in business mentioned in chapter 1, we firstly give a series of definitions to outline the concepts of social Manufacturing in this chapter, which describe a kind of new generation Manufacturing Paradigm. And then, the characteristics, basic architecture and runtime logic, key enabled technologies, computing methods to support the implementation of the Manufacturing Paradigm are discussed in detail. The comparison among different Manufacturing Paradigms indicates that the social Manufacturing Paradigm matches with the development trends of Internet-based connecting and communicating behaviors of human beings in business and opens up a new road to guide us how to enable Internet-based enterprises today and in the near future.

  • Analysis of personalized production organizing and operating mechanism in a social Manufacturing environment
    Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 2017
    Co-Authors: Pingyu Jiang, Kai Ding
    Abstract:

    Current Manufacturing industry has witnessed the trends of socialization, personalization, and servitization, which prompts the appearance of social Manufacturing Paradigm. This article addresses t...

  • Mining and Matching Relationships From Interaction Contexts in a Social Manufacturing Paradigm
    IEEE Transactions on Systems Man and Cybernetics, 2017
    Co-Authors: Jiewu Leng, Pingyu Jiang
    Abstract:

    There is an increasing use of social interaction contexts in the cross-enterprise Manufacturing problem solving. To transform these massive and unstructured data into decision-support information for cross-enterprise Manufacturing demand-capability matching, we present automated solutions to two phases: 1) extracting relationships based on a semi-supervised learning approach to derive formalized heterogeneous Manufacturing network from the unstructured text-based context that contains high levels of noise and irrelevant information and 2) matching group-level relationships among the entities in the established Manufacturing network. The extracting phase formulates network data using multiattributed graph that can encode various entities and relationships. The matching phase is based on probabilistic multiattributed graph matching, and implemented using distributed message passing algorithm. We developed a prototype system to verify the proposed model, which is also flexible to new domains of contexts and scale to large datasets. The ultimate goal of this paper is to facilitate knowledge transferring and sharing in the context of cross-enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprise.

  • Social Manufacturing as a sustainable Paradigm for mass individualization
    Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 2016
    Co-Authors: Pingyu Jiang, Jiewu Leng, Kai Ding, Yoram Koren
    Abstract:

    With increasing product personalization and open innovation, the Manufacturing Paradigm has been transforming to a more decentralized and socialized one. Social Manufacturing was proposed as a new ...

  • A deep learning approach for relationship extraction from interaction context in social Manufacturing Paradigm
    Knowledge-Based Systems, 2016
    Co-Authors: Jiewu Leng, Pingyu Jiang
    Abstract:

    There is an increasing unstructured text data produced in cross-enterprise social interaction media, forming a social interaction context that contains massive Manufacturing relationships, which can be potentially used as decision support information for cross-enterprise Manufacturing demand-capability matchmaking. How to enable decision-makers to capture these relationships remains a challenge. The text-based context contains high levels of noise and irrelevant information, causing both highly complexity and sparsity. Under this circumstance, instead of exploiting man-made features carefully optimized for the relationship extraction task, a deep learning model based on an improved stacked denoising auto-encoder on sentence-level features is proposed to extract Manufacturing relationships among various named entities (e.g., enterprises, products, demands, and capabilities) underlying the text-based context. Experiment results show that the proposed approach can achieve a comparable performance with the state-of-the-art learning models, as well as a good practicality of its' web-based implementation in social Manufacturing interaction context. The ultimate goal of this study is to facilitate knowledge transferring and sharing in the context of enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprises.

Jiewu Leng - One of the best experts on this subject based on the ideXlab platform.

  • Mining and Matching Relationships From Interaction Contexts in a Social Manufacturing Paradigm
    IEEE Transactions on Systems Man and Cybernetics, 2017
    Co-Authors: Jiewu Leng, Pingyu Jiang
    Abstract:

    There is an increasing use of social interaction contexts in the cross-enterprise Manufacturing problem solving. To transform these massive and unstructured data into decision-support information for cross-enterprise Manufacturing demand-capability matching, we present automated solutions to two phases: 1) extracting relationships based on a semi-supervised learning approach to derive formalized heterogeneous Manufacturing network from the unstructured text-based context that contains high levels of noise and irrelevant information and 2) matching group-level relationships among the entities in the established Manufacturing network. The extracting phase formulates network data using multiattributed graph that can encode various entities and relationships. The matching phase is based on probabilistic multiattributed graph matching, and implemented using distributed message passing algorithm. We developed a prototype system to verify the proposed model, which is also flexible to new domains of contexts and scale to large datasets. The ultimate goal of this paper is to facilitate knowledge transferring and sharing in the context of cross-enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprise.

  • Social Manufacturing as a sustainable Paradigm for mass individualization
    Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 2016
    Co-Authors: Pingyu Jiang, Jiewu Leng, Kai Ding, Yoram Koren
    Abstract:

    With increasing product personalization and open innovation, the Manufacturing Paradigm has been transforming to a more decentralized and socialized one. Social Manufacturing was proposed as a new ...

  • A deep learning approach for relationship extraction from interaction context in social Manufacturing Paradigm
    Knowledge-Based Systems, 2016
    Co-Authors: Jiewu Leng, Pingyu Jiang
    Abstract:

    There is an increasing unstructured text data produced in cross-enterprise social interaction media, forming a social interaction context that contains massive Manufacturing relationships, which can be potentially used as decision support information for cross-enterprise Manufacturing demand-capability matchmaking. How to enable decision-makers to capture these relationships remains a challenge. The text-based context contains high levels of noise and irrelevant information, causing both highly complexity and sparsity. Under this circumstance, instead of exploiting man-made features carefully optimized for the relationship extraction task, a deep learning model based on an improved stacked denoising auto-encoder on sentence-level features is proposed to extract Manufacturing relationships among various named entities (e.g., enterprises, products, demands, and capabilities) underlying the text-based context. Experiment results show that the proposed approach can achieve a comparable performance with the state-of-the-art learning models, as well as a good practicality of its' web-based implementation in social Manufacturing interaction context. The ultimate goal of this study is to facilitate knowledge transferring and sharing in the context of enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprises.

  • towards a cyber physical social connected and service oriented Manufacturing Paradigm social Manufacturing
    Manufacturing letters, 2016
    Co-Authors: Pingyu Jiang, Kai Ding, Jiewu Leng
    Abstract:

    Abstract Manufacturing industry is heading toward socialization, personalization, servitization and mass collaboration. Motivated by the infiltration of Cyber-Physical Systems (CPS) and social media usage in Manufacturing industry, this paper addresses a new Social Manufacturing (SocialM) Paradigm and provides a theoretical basis for future production organization. Definitions and organizational logic of SocialM are given. Three core aspects of SocialM are addressed from the configuration, operation and management perspectives. It is expected that SocialM would contribute to the production mode transformation and social innovation.

Lin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Diverse task scheduling for individualized requirements in cloud Manufacturing
    Enterprise Information Systems, 2017
    Co-Authors: Longfei Zhou, Lin Zhang, Chun Zhao, Yuanjun Laili
    Abstract:

    Cloud Manufacturing (CMfg) has emerged as a new Manufacturing Paradigm that provides ubiquitous, on-demand Manufacturing services to customers through network and CMfg platforms. In CMfg system, ta...

  • Enterprises in Cloud Manufacturing: A Preliminary Exploration
    Volume 3: Manufacturing Equipment and Systems, 2017
    Co-Authors: Yongkui Liu, Ananth Srinivasan, Lin Zhang
    Abstract:

    Cloud Manufacturing is a new Manufacturing Paradigm in which Manufacturing resources and capabilities offered by different enterprises are provided as cloud-based services over the Internet. In addition to the cloud Manufacturing platform, enterprises as resource providers are also essential part of a cloud Manufacturing system as customer orders that are submitted to the cloud platform will ultimately need to be dispatched to enterprises’ production sites for execution. So far, however, issues concerning enterprises in cloud Manufacturing has attracted little attention of researchers, and the most frequently mentioned issue in existing research that are concerned with enterprises is how to connect enterprises’ resources to the cloud infrastructure. This, to a large extent, hinders the development and implementation of cloud Manufacturing as the lack of research on enterprises fails to uncover the requirements for enterprises in cloud Manufacturing (i.e. Cloud Manufacturing Enterprises, CMEs), and thus enterprises have no reference for evaluating the changes that need to be made to adopt this new Manufacturing Paradigm. This paper focuses on this important issue and conducts a preliminary exploration on CMEs. We first analyze the requirements for CMEs, and then discuss some critical issues with CMEs in detail, including enterprise information systems, enterprise architecture, and enterprise modeling.

  • Cloud Manufacturing: a new Manufacturing Paradigm
    Enterprise Information Systems, 2014
    Co-Authors: Lin Zhang, Yongliang Luo, Anrui Hu, Bo Hu Li, Fei Tao, Hua Guo, Lei Ren, Xuesong Zhang, Ying Cheng, Yongkui Liu
    Abstract:

    Combining with the emerged technologies such as cloud computing, the Internet of things, service-oriented technologies and high performance computing, a new Manufacturing Paradigm - cloud Manufacturing (CMfg) - for solving the bottlenecks in the informatisation development and Manufacturing applications is introduced. The concept of CMfg, including its architecture, typical characteristics and the key technologies for implementing a CMfg service platform, is discussed. Three core components for constructing a CMfg system, i.e. CMfg resources, Manufacturing cloud service and Manufacturing cloud are studied, and the constructing method for Manufacturing cloud is investigated. Finally, a prototype of CMfg and the existing related works conducted by the authors' group on CMfg are briefly presented. © 2014 Taylor and Francis Group, LLC.

  • Lifecycle Management of Knowledge in a Cloud Manufacturing System
    Volume 2: Systems; Micro and Nano Technologies; Sustainable Manufacturing, 2013
    Co-Authors: Lin Zhang, Fei Tao
    Abstract:

    Cloud Manufacturing is a new service-oriented intelligent Manufacturing Paradigm. Knowledge is a core part and the foundation to realize its intelligence. In this paper, the importance and functions of knowledge to cloud Manufacturing was first investigated from the lifecycle of cloud service. Then a knowledge management system was designed and the layered architecture and key technologies were analyzed. A case study was conducted to demonstrate the feasibility of the proposed knowledge management system.

  • IEEM - Energy adaptive immune genetic algorithm for collaborative design task scheduling in Cloud Manufacturing system
    2011 IEEE International Conference on Industrial Engineering and Engineering Management, 2011
    Co-Authors: Yuanjun Laili, Lin Zhang, Fei Tao
    Abstract:

    A new Manufacturing Paradigm, i.e. Cloud Manufacturing (CMfg) has been proposed recently. In order to satisfy high efficiency and low cost collaborative design task scheduling in CMfg, a new energy adaptive immune genetic algorithm (EAIGA) was designed. With the introduction of potential energy storage and detection, the new algorithm can not only improve searching diversity based on immune strategy, but also adaptively adjust the probabilities of crossover and mutation with low time complexity. The experimental results demonstrate that the new algorithm can effectively solving collaborative design task scheduling problem with a good balance between searching diversification and intensification.

Fei Tao - One of the best experts on this subject based on the ideXlab platform.

  • Cloud Manufacturing Paradigm with ubiquitous robotic system for product customization
    Robotics and Computer-Integrated Manufacturing, 2019
    Co-Authors: Zhinan Zhang, Xin Wang, Xiaoxiao Zhu, Qixin Cao, Fei Tao
    Abstract:

    Abstract In the new era of Industry 4.0, traditional Manufacturing plants are forced to transform into smart factories with cyber-physical product creation systems. Robots are believed to be one of the key components of such systems. This paper presents an architecture of using cloud-based ubiquitous robotic systems for smart Manufacturing of the customized product. A framework for designing a cloud-based ubiquitous robotic system (URS) is developed, which consists of the function, structure and behavior of a URS. After that, a procedure for the development of such a URS is provided. Finally, the implementation of a cloud-based ubiquitous robotic system for smart producing and assembly of a customized product shows that the proposed approach can achieve the goal of smart Manufacturing of customized product by developing cloud-based ubiquitous robotic systems.

  • Cloud Manufacturing: a new Manufacturing Paradigm
    Enterprise Information Systems, 2014
    Co-Authors: Lin Zhang, Yongliang Luo, Anrui Hu, Bo Hu Li, Fei Tao, Hua Guo, Lei Ren, Xuesong Zhang, Ying Cheng, Yongkui Liu
    Abstract:

    Combining with the emerged technologies such as cloud computing, the Internet of things, service-oriented technologies and high performance computing, a new Manufacturing Paradigm - cloud Manufacturing (CMfg) - for solving the bottlenecks in the informatisation development and Manufacturing applications is introduced. The concept of CMfg, including its architecture, typical characteristics and the key technologies for implementing a CMfg service platform, is discussed. Three core components for constructing a CMfg system, i.e. CMfg resources, Manufacturing cloud service and Manufacturing cloud are studied, and the constructing method for Manufacturing cloud is investigated. Finally, a prototype of CMfg and the existing related works conducted by the authors' group on CMfg are briefly presented. © 2014 Taylor and Francis Group, LLC.

  • Lifecycle Management of Knowledge in a Cloud Manufacturing System
    Volume 2: Systems; Micro and Nano Technologies; Sustainable Manufacturing, 2013
    Co-Authors: Lin Zhang, Fei Tao
    Abstract:

    Cloud Manufacturing is a new service-oriented intelligent Manufacturing Paradigm. Knowledge is a core part and the foundation to realize its intelligence. In this paper, the importance and functions of knowledge to cloud Manufacturing was first investigated from the lifecycle of cloud service. Then a knowledge management system was designed and the layered architecture and key technologies were analyzed. A case study was conducted to demonstrate the feasibility of the proposed knowledge management system.

  • IEEM - Energy adaptive immune genetic algorithm for collaborative design task scheduling in Cloud Manufacturing system
    2011 IEEE International Conference on Industrial Engineering and Engineering Management, 2011
    Co-Authors: Yuanjun Laili, Lin Zhang, Fei Tao
    Abstract:

    A new Manufacturing Paradigm, i.e. Cloud Manufacturing (CMfg) has been proposed recently. In order to satisfy high efficiency and low cost collaborative design task scheduling in CMfg, a new energy adaptive immune genetic algorithm (EAIGA) was designed. With the introduction of potential energy storage and detection, the new algorithm can not only improve searching diversity based on immune strategy, but also adaptively adjust the probabilities of crossover and mutation with low time complexity. The experimental results demonstrate that the new algorithm can effectively solving collaborative design task scheduling problem with a good balance between searching diversification and intensification.

Yongkui Liu - One of the best experts on this subject based on the ideXlab platform.

  • Enterprises in Cloud Manufacturing: A Preliminary Exploration
    Volume 3: Manufacturing Equipment and Systems, 2017
    Co-Authors: Yongkui Liu, Ananth Srinivasan, Lin Zhang
    Abstract:

    Cloud Manufacturing is a new Manufacturing Paradigm in which Manufacturing resources and capabilities offered by different enterprises are provided as cloud-based services over the Internet. In addition to the cloud Manufacturing platform, enterprises as resource providers are also essential part of a cloud Manufacturing system as customer orders that are submitted to the cloud platform will ultimately need to be dispatched to enterprises’ production sites for execution. So far, however, issues concerning enterprises in cloud Manufacturing has attracted little attention of researchers, and the most frequently mentioned issue in existing research that are concerned with enterprises is how to connect enterprises’ resources to the cloud infrastructure. This, to a large extent, hinders the development and implementation of cloud Manufacturing as the lack of research on enterprises fails to uncover the requirements for enterprises in cloud Manufacturing (i.e. Cloud Manufacturing Enterprises, CMEs), and thus enterprises have no reference for evaluating the changes that need to be made to adopt this new Manufacturing Paradigm. This paper focuses on this important issue and conducts a preliminary exploration on CMEs. We first analyze the requirements for CMEs, and then discuss some critical issues with CMEs in detail, including enterprise information systems, enterprise architecture, and enterprise modeling.

  • Cloud Manufacturing: a new Manufacturing Paradigm
    Enterprise Information Systems, 2014
    Co-Authors: Lin Zhang, Yongliang Luo, Anrui Hu, Bo Hu Li, Fei Tao, Hua Guo, Lei Ren, Xuesong Zhang, Ying Cheng, Yongkui Liu
    Abstract:

    Combining with the emerged technologies such as cloud computing, the Internet of things, service-oriented technologies and high performance computing, a new Manufacturing Paradigm - cloud Manufacturing (CMfg) - for solving the bottlenecks in the informatisation development and Manufacturing applications is introduced. The concept of CMfg, including its architecture, typical characteristics and the key technologies for implementing a CMfg service platform, is discussed. Three core components for constructing a CMfg system, i.e. CMfg resources, Manufacturing cloud service and Manufacturing cloud are studied, and the constructing method for Manufacturing cloud is investigated. Finally, a prototype of CMfg and the existing related works conducted by the authors' group on CMfg are briefly presented. © 2014 Taylor and Francis Group, LLC.