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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37034 条记录，以下是481-490 订阅

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A Critical Analysis of the Block Chain in manufacturing System Implementation 9

A Critical Analysis of the Block Chain in Manufacturing Syst...

引用

9th international conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Gokilakrishnan, G. Prabha, D. Srinivasan, S. Thirumurugaveerakumar, S. Vigneshkumar, M. Anandakumar, H. Sri Eshwar College of Engineering Department of Mechanical Engineering Tamilnadu Coimbatore India Sri Krishna College of Engineering and Technology Computer Science and Engineering Tamilnadu Coimbatore India Nehru Institute of Technology Agriculture Engineering Tamilnadu Coimbatore India Kumaraguru College of Technology Department of Mechanical Engineering Tamilnadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350397376

As component of the industry 4.0 project, a novel concept of Cyber-Physical systems (CPS), fog computing, big data analytics, cloud manufacturing, the Internet of Things (IoT), and other technologies have been brought to the manufacturing sector. Some of the projected benefits and possibilities that these innovations might provide include self-prediction, self-maintenance, self-comparison, self-configuration, and self-awareness. In these concepts, the centralised corporate system and third-party trust operations are still in use. However, contemporary manufacturing has a slew of problems, including data security and reliability, adaptability, security, trust, and privacy. This paper provides an analysis of the Blockchain Technology (BT), reviews the application of blockchain in Artificial Intelligence (AI), proposes a model for blockchain-enabled CPS and discusses the potential challenges that face the implementation of blockchain in manufacturing systems. © 2023 IEEE.

关键词： Blockchain

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Development of Design, Control, and data Acquisition Modules for Fluid Power Education

Development of Design, Control, and Data Acquisition Modules...

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2024 ASEE Annual conference and Exposition

作者： Jabbour, Helene Azzam, Israa Lago, Isaac Elí Breidi, Farid Garcia, Jose M. Purdue University United States Tecnologico de Monterrey Mexico

In preparing the next generation of engineering workforce, fluid power courses aim to provide students in applied engineering technology with the technical abilities industry professionals require. Within fluid power, understanding the fundamentals of pneumatic systems constitutes a core area of study due to its various applications in construction, automotive, and manufacturing. Nevertheless, traditional curricula entail limited implementation of practical knowledge in applied disciplines such as fluid power, which does not meet the expectations of a technical college or university. Consequently, students not only tend to face challenges in assimilating core fluid power concepts but are also later confronted with a lack of hands-on skills in industry. Despite the efforts to incorporate interactive tools and methods into fluid power labs, the challenge remains in training students on the control and data acquisition of fluid power systems using flexible methods that foster learning and critical thinking. Therefore, this paper presents a set of laboratory modules designed to introduce pneumatic systems design and control to fluid power college and university courses following a practical and inexpensive approach. It investigates the control of a pneumatic manipulator through an air pump and the data acquisition of a pressure transducer using an Arduino Microcontroller to measure the pressure exhibited in the system. The laboratory modules are proposed with their respective mechanical and electrical designs, hardware components, codes, and testing results. © American Society for engineering Education, 2024.

关键词： Students

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Graph Analytics for Avian Science data 15

Graph Analytics for Avian Science Data

引用

15th international conference on knowledge Management and Information systems, KMIS 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Pandat, Ami Bhise, Minal Srivastava, Sanjay Distributed Databases Group DAIICT Gandhinagar India DAIICT Gandhinagar India

ISBN: (纸本)9789897586712

data management solutions are becoming increasingly necessary as more Big data applications are developed. One such area that deals with Big data is Big Graphs. Complex relationships exist in graph-based applications. Analytics and data extraction are better solutions for understanding such complex applications. data from Avian Science has shown significant growth in recent years. Graph analytics can be used to interpret complex scientific data and their relationships. This paper uses graph analytics to discuss the application of graph analytics in avian science. For the eBird dataset, four Graph Analytics techniques were identified and implemented. These methods extract information about path patterns, node popularity, connections to other nodes, and clustering. The dataset includes real-time data on bird observation and distribution. Each analytics technique extracts data from the birds’ observations. The findings show that graph analytics for avian science data can aid in predicting a wide range of crowd-sourced information. Additionally, the work can be expanded using machine learning methods. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Birds

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Model-Based Framework for Continuous Adaptation and Evolution of Quantum-Classical Hybrid systems 8

Model-Based Framework for Continuous Adaptation and Evolutio...

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8th international conference on the Art, Science, and engineering of Programming (Programming)

作者： Ishikawa, Fuyuki Saito, Shinobu Natl Inst Informat Chiyoda Ku Tokyo Japan NTT Comp & Data Sci Labs Musashino Tokyo Japan

ISBN: (纸本)9798400706349

Although quantum computing has been attracting increasing attention, hardware restrictions are tight in current implementations. Intensive design exploration is therefore essential to match requirements, such as the problem scale and acceptable error rate, with potential designs to combine quantum computing and classical computing. The design decision made in this way is often fragile as it is sensitive to the problem scale as well as still evolving quantum services. We need continuous design decision, or adaptation and evolution, given changes in requirements or environments. In this paper, we present a framework for model-based engineering to support the continuous adaptation and evolution of quantum-classical hybrid systems. Modeling in our framework involves not only potential designs, but also rationale or evidence of design decision, which often requires simulation and experiments. This focus allows for tracing and analyzing whether the past decision is still valid or not, or whether there is uncertainty and we need further simulation and experiments. The usage of the framework is demonstrated with an example problem from steel manufacturing.

关键词： Quantum Software engineering Hybrid Quantum Computing Services Computing DevOps Self-Adaptive systems Models@run.time

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Identifying Dependency Relationships Between Events in Production systems 32nd

Identifying Dependency Relationships Between Events in Produ...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Nunes, Pedro Lopes, Isabel Basto, Luis Pires, Claudia Univ Minho Sch Engn Dept Prod & Syst P-4800058 Guimaraes Portugal Univ Minho Sch Engn ALGORITMI Res Ctr LASI P-4800058 Guimaraes Portugal Technol Ctr Met Working Ind CATIM Rua Platanos 197 P-4100414 Porto Portugal

ISBN: (纸本)9783031381645;9783031381652

Industry 4.0 is directly related to the digital transformation of organizations. One of the main characteristics of this transformation is the ability to analyze and manage data to obtain relevant and previously unknown information from it. Specifically, in the context of operational risk management, an efficient analysis of the data that typically come from occurrence records, can support decision-making. This paper presents a new algorithm to identify dependencies between events in productive environments, such as equipment failures, defects, or safety and environmental incidents, supporting the decision-making process in the context of analyzing and managing operational risks. The algorithm was developed based on association rulemining algorithms, then tested with data from a Portuguese metalworking company, and finally validated. The test results show that the developed algorithm is capable of identifying dependencies, which have to be confirmed by specialized staff to conclude if they are causal relationships.

关键词： Association Rule Mining data Analysis Frequent Pattern Mining Risk Management

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Development of Joining Process Ontology for Ensuring data Consistency in knowledge Management systems 4th

Development of Joining Process Ontology for Ensuring Data Co...

引用

4th international conference on Innovative manufacturing, Mechatronics and Materials Forum, iM3F2023

作者： Zaini, Muhammad Alif Hafizan Bin Mohd Ali, Munira Binti Mohd Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia

ISBN: (纸本)9789819988181

Inconsistency data from the knowledge management system comes from unstable data and entities that have been defined. data not being saved on the proper platform will result in inconsistent data. This research aims to develop an ontology for the selected joining process that provides a standard understanding structure to support user interoperability across heterogeneous data. Based on that, it is important to develop the ontology by identifying and classifying the correct entities. The basic formal ontology has been adopted as the top-level ontology for the development of the ontology for the joining process. The ontology is then expanded with entities related to the joining process. The ontology needs to be evaluated based on consistency, accuracy, and adaptability. A sample of data from research related to the welding process was used to test the capability of the ontology to infer information. As a result, proper ontology development will solve the inconsistency of data in the knowledge management system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： knowledge management

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Research in Blockchain-Based manufacturing Supply Chain 3

Research in Blockchain-Based Manufacturing Supply Chain

引用

3rd IEEE international conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Langat, Alex Kibet Guo, Yu Nanjing University of Aeronautics and Astronautics College of Mechanical and Electrical Engineering Nanjing China

ISBN: (纸本)9798350337952

This paper proposes an innovative approach for enhancing the efficiency and security of manufacturing supply chains by integrating the Internet of Things, blockchain technology, and genetic algorithm-based consensus mechanism. The IoT devices are employed to monitor and collect real-time data from various stages of the supply chain, ensuring transparency and traceability. The blockchain technology is utilized to store and secure the collected data, enabling immutable and decentralized data management. To achieve consensus in the Blockchain network, a GA-based consensus algorithm is introduced, which optimizes the routing and allocation of resources in the supply chain. The GA considers factors such as distance, capacity, and demand to minimize transportation costs and maximize resource utilization. Experimental results demonstrate that the proposed approach significantly improves the efficiency and reliability of the manufacturing supply chain, reducing operational costs and enhancing customer satisfaction. This research contributes to the development of intelligent and trustworthy supply chain systems by leveraging the synergies among IoT, blockchain, and GA-based consensus mechanisms. © 2023 IEEE.

关键词： Supply chains

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CE:knowledge Tracking Model Uncertainty Assessment Method Under Trusted Artificial Intelligence

CE:Knowledge Tracking Model Uncertainty Assessment Method Un...

引用

2024 IEEE international conference on systems, Man, and Cybernetics, SMC 2024

作者： Bai, Jiping Kang, Bo Hu, Kexin Zheng, Qi Wang, Xiaodong Ocean University of China Faculty of Information Science and Engineering Qingdao China

ISBN: (纸本)9781665410205

Trustworthy AI is becoming a new focus in the field of artificial intelligence, and the study of its trustworthiness criteria is crucial for enhancing the trustworthiness of AI. Despite the plethora of methods that have been explored to assess the trustworthiness of AI, there remains a dearth of simple and intuitive assessment approaches. This paper focuses on the domain of knowledge tracing, combining various clustering techniques to propose a trustworthy evaluation method based on uncertainty computation. Through validation using clustering, Monte Carlo methods, and correlation analysis, our approach effectively examines the trustworthiness of multiple prominent knowledge tracing models. Employing open data sets and virtual data sets grounded in Item Response Theory (IRT), our method achieves commendable performance at a low cost, thereby providing a reference for research into the trustworthiness of knowledge tracing models. Finally, we summarize model errors and areas for improvement, and offer perspectives on scale and integrated trustworthy development. Experimental results demonstrate that our method achieved an accuracy rate of 81.04%. © 2024 IEEE.

关键词： data accuracy

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On deriving kernels for linear time-dependent systems 31

On deriving kernels for linear time-dependent systems

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31st international conference on Noise and Vibration engineering, ISMA 2024 and 10th international conference on Uncertainty in Structural Dynamics, USD 2024

作者： Jones, M.R. Cross, E.J. University of Sheffield Department of Mechanical Engineering Sir Frederick Mappin Building Mappin Street SheffieldS1 3JD United Kingdom

ISBN: (纸本)9789082893175

Embedding domain expertise within machine learning has allowed for hybrid models that possess both the expressivity of data-driven models whilst adhering to fundamental equations and constraints that ensure predictions are physically plausible. When forming physics-informed models, a question that is overlooked is what actually constitutes best practice for embedding prior knowledge that is held into the structure of the machine learner. Existing approaches largely ignore the influence of how physics is integrated into a model, and assumes that best practice is already being applied. To begin understanding whether the procedure of including domain expertise inside machine learning can be improved, we investigate physics-informed models that arise explicitly from a derivation path of the interaction between the physics and what remains unknown. Several case studies for modelling time-driven systems are considered, with the behaviour of the derived models compared to examples of what would likely be attempted in current practice. © 2024 Proceedings of ISMA 2024 - international conference on Noise and Vibration engineering and USD 2024 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

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Operational System of systems Capability Evaluation: A Information Granule-Based Capability Representation and Evaluation Method 3

Operational System of Systems Capability Evaluation: A Infor...

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3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Yan, Jingjing Xiao, Weidong Zhang, Weiming Fu, Chen College of Systems Engineering National University of Denfense Technology Changsha China National Key Laboratory of Information Systems Engineering Nanjing China College of Systems Engineering National University of Denfense Technology National Key Laboratory of Information Systems Engineering Changsha China Nanjing Research Institute of Electronic Engineering National Key Laboratory of Information Systems Engineering Nanjing China

ISBN: (纸本)9798350365443

The modern military confrontations often involve simultaneous activities such as electromagnetic warfare, network information warfare, and weapon firepower confrontation. These confrontations typically take the form of a system of systems (SoS), which is a complex system with diverse data types, large volumes of data, and intricate data transmission networks. To ensure absolute superiority in military confrontations, combat commanders must be able to extract knowledge from the complex SoS data and intuitively understand and evaluate its various capabilities in order to promptly implement appropriate command measures. This study proposes a granular description-based method for evaluating SoS capabilities to achieve rapid and intuitive cognition of SoS capabilities. The method aims to transform the problem of evaluating SoS capabilities into a granular modeling problem consisting of three stages: the first stageinvolveso rganizing t he S oS data a nd constructing a dataset encompassing SoS capability;the second stage focuses on constructing capability information granules;the third stage develops an information granule-based model using historical SoS data. Based on the SoS capability dataset generated by the constructed SoS-level combat confrontation simulation environment, a series of experiments concerning the generated dataset are implemented to exhibit the process and effect of the proposed granular-based evaluation method. © 2024 IEEE.

关键词： Information granules

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