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2nd international conference on Advanced manufacturing Technology and manufacturing systems, ICAMTMS 2023

作者： Tong, Yulong Wang, Chenyu Shen, Hai College of Communication Engineering Jilin University Changchun130022 China College of Physics Science and Technology Shenyang Normal University Shenyang110034 China

ISBN: (纸本)9781510667327

The technical realization of temperature control system plays a key role in the development of industrial automation. This paper designs a temperature control system with the heating furnace model as the controlled object, the heating furnace water temperature as the data acquisition object and controlled parameters, and the Siemens S7-1500 PLC as the lower computer controller. The PLC ladder diagram programming language in the software carries out the programming design of the lower computer, and combines the configuration and monitoring screen of the upper computer to design the configuration monitoring screen. At the same time, the function expansion and operation debugging are carried out to realize the monitoring and automatic control of the temperature data. © 2023 SPIE.

关键词： Computer control systems

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CONTINUOUS LEARNING OF BIG data ANALYTICS OF EMPLOYEES: INTEGRATING UTAUT AND SITUATED EXPECTANCY-VALUE BELIEFS 18

CONTINUOUS LEARNING OF BIG DATA ANALYTICS OF EMPLOYEES: INTE...

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18th international conferences IADIS Information systems 2025 and 23rd international conference on e-Society 2025

作者： Lee, Seula Lee, Hyewon Cho, Youngsang Management of Technology Yonsei University Seoul03722 Korea Republic of Gyeongi-do 16824 Korea Republic of Department of Industrial Engineering College of Engineering Yonsei University Seoul03722 Korea Republic of

ISBN: (纸本)9789898704658

Despite recognizing the importance of big data analytics capabilities for competitive advantage, the understanding of employees’ skill development remains limited. While situated expectancy-value theory (SEVT) is useful for identifying factors influencing learning intention, it lacks actionable strategies for organizations to foster learning environments. Integrating the unified theory of acceptance and use of technology (UTAUT) with SEVT, this ongoing study addresses the gap in understanding how employees enhance their skills to use big data analytics effectively among semiconductor manufacturing engineers. data were collected from 144 engineers at a semiconductor manufacturing company in South Korea. Partial least squares structural equation modeling was used to analyze the relationships between the constructs. The results show that performance expectancy, effort expectancy, and social influence positively affect continued intention to use big data analytics applications, except facilitating conditions. Secondly, continued intention to use and social norms as supportive environments of learning positively influence intrinsic motivation, mediated by factors such as self-efficacy and perceived usefulness. Understanding these factors provides actionable insights into how technological investment creates learning environments that facilitate the continuous development of big data analytics capabilities. By focusing on mechanisms that foster continuous learning, this study offers strategic guidance for organizations that aim to drive data-driven innovation and maintain a competitive advantage in high-tech industries. © 2025 Proceedings of the international conferences IADIS Information systems 2025 and e-Society 2025. All rights reserved.

关键词： Competition

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Comparative Analysis of Topic Modelling Approaches on Student Feedback 16

Comparative Analysis of Topic Modelling Approaches on Studen...

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16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Hayat, Faiz Shatnawi, Safwan Haig, Ella School of Computing University of Portsmouth Buckingham Building Lion Terrace PortsmouthPO1 3HE United Kingdom International Research and Exchange Board Amman Jordan

ISBN: (纸本)9789897587160

Topic modelling, a type of clustering for textual data, is a popular method to extract themes from text. Methods such as Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and Non-negative Matrix Factorization (NMF) have been successfully used across a wide range of applications. Large Language Models, such as BERT, have led to significant improvements in machine learning tasks for textual data in general, as well as topic modelling, in particular. In this paper, we compare the performance of a BERT-based topic modelling approach with LDA, LSA and NMF on textual feedback from students about their mental health and remote learning experience during the COVID-19 pandemic. While all methods lead to coherent and distinct topics, the BERT-based approach and NMF are able to identify more fine-grained topics. Moreover, while NMF resulted in more detailed topics about the students’ mental health-related experiences, the BERT-based approach produced more detailed topics about the students’ experiences with remote learning. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Non-negative matrix factorization

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Selection of Industry 4.0 Technology to Support Lean manufacturing from the Perspective of Enterprise Interoperability 32nd

Selection of Industry 4.0 Technology to Support Lean Manufac...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Martins, G. R. D. N. Ramos, L. F. P. Loures, E. F. R. Deschamps, F. Loures, L. R. Pontifical Catholic Univ Parana PUC PR Curitiba Parana Brazil

ISBN: (纸本)9783031381645;9783031381652

Faced with paradigm shifts in the global manufacturing context promoted by the Fourth Industrial Revolution, many organizations are seeking to meet customer needs through the integration of Lean manufacturing (LM) philosophy principles with Industry 4.0 (I4.0) technologies. When there is the integration of technological enablers from I4.0 and deep advances in efficiency and productivity with LM, these systems tend to offer enhanced and more assertive results, since they are complementary concepts. The main goal of this paper is the selection of I4.0 technologies to support the LM system, considering the perspectives and barriers of Enterprise Interoperability (EI) and using multicriteria methods (MCDM) to support decision-making. Using the DEMATEL multicriteria method, it was possible to develop a diagnostic evaluation, analyze the existing influences between the elements of the LM, and support the elicitation of weights in the decisional evaluation, with the FITradeoff method. In this way, the decisional evaluation indicated as the I4.0 technology that must be implemented as a priority to raise the level of organizational maturity in LM is Big data Analytics. Big data integrated with Business Analytics (BA) can offer several advantages, such as assertiveness in decision-making;Keeping the company updated about the market;Indicating risks and improving data security;Promotes alignment between marketing and sales, among others.

关键词： Lean manufacturing Industry 4.0 Enterprise Interoperability MCDM methods

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Study on the reliability allocation of machining centers based on data envelopment analysis method 2

Study on the reliability allocation of machining centers bas...

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2nd international conference on Advanced manufacturing Technology and manufacturing systems, ICAMTMS 2023

作者： Jin, Guang-Yu Jin, Sheng-Jie Wang, De-Chao Piao, Cheng-Dao Yin, Feng-Zhe Engineering College of Yanbian University Yanji133002 China

ISBN: (纸本)9781510667327

For the problem that it is difficult to determine the type of failure occurring in machining centers, Minitab software is used to process and analyze the failure data, and the type of failure distribution is found to obey the Weibull distribution. Based on the CCR model in the data envelope method, the reliability index is applied to allocate the reliability of the machining center, and according to the practical applicability of the allocation method, the redistribution problem of reliability is better improved, and the reliability level of the whole machine is improved. © 2023 SPIE.

关键词： Machining centers

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Overview of Maintenance Scope and Reliability in the manufacturing Sector

Overview of Maintenance Scope and Reliability in the Manufac...

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2024 international conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Kayode, Joseph F. Afolalu, Sunday A. Monye, Stella I. Adaramola, Bernard A. Afe Babalola University Dept. of Mechanical & Mechatronics Engineering Ado Nigeria Afe Babalola University Dept of Mechanical & Mechatronics Engineering Ado-Ekiti Nigeria University of Johannesburg Dept. of Mechanical Engineering Science Johannesburg2006 South Africa

ISBN: (纸本)9798350358155

The manufacturing industry has been crucial to the expansion of economies around the world. However, industrial equipment must operate effectively and reliably to maintain a high degree of effectiveness, quality, and financial success. Consequently, the manufacturing industry places great reliance on maintenance, the process of keeping machines, equipment, and systems in top working condition. Predictive, preventative, and corrective maintenance methods, as well as the value of reliability engineering, are all discussed in this paper, along with their respective applications in the industrial industry. The utilization of data and analytics is the foundation of predictive maintenance, which is a relatively novel maintenance approach to keep tabs on machinery and foretell when fixing it will be necessary. An integral part of manufacturing facility upkeep is reliability engineering. Maintaining reliable and efficient machinery and systems requires the methodical application of engineering ideas and techniques. Equipment design, testing, analysis, and optimization are only a few of the many tasks included in reliability engineering. Maintenance costs can be decreased, equipment performance can be enhanced, and equipment lifespan can be lengthened if businesses prioritize reliability © 2024 IEEE.

关键词： Predictive maintenance

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A critical review of contribution of evolutionary techniques to machining parameter optimisation

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international Journal of Mechatronics and manufacturing systems 2024年第4期17卷 411-448页

作者： Dixit, Uday Shanker Sharma, Faladrum Department of Mechanical Engineering Indian Institute of Technology Guwahati Guwahati781 039 India Centre for Indian Knowledge Systems Indian Institute of Technology Guwahati Guwahati781 039 India

Optimisation of machining processes has been an active research area for over six decades. In the last three decades, several evolutionary optimisation methods have been utilised. This paper offers a brief history of optimisation in machining followed by a detailed discussion on the application of evolutionary optimisation methods. The focus is narrowed down to traditional machining processes, where a wedge shaped material removes the material in the form of chips. The paper highlights the challenges in choosing a suitable algorithm, given the empirical nature of available comparative studies lacking a solid mathematical basis. Further research is needed to understand the similarities and contrasts among evolutionary optimisation methods. It is also prudent to focus on the proper modelling of the processes (preferably with the help of big data), integrating the machine tools with proper sensors and developing strategies for online optimisation. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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Machine Learning for Fraud Detection in Blockchain Transaction

Machine Learning for Fraud Detection in Blockchain Transacti...

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2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Kaur, Kirandeep Venkatesh, R. Pundir, Sumit Brindha, S. Arul Mary Rexy, V. Singh, Ramanjeet University School of Business Chandigarh University Mohali Punjab Gharuan India Karpagam College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India Kpr Institute of Engineering and Technology Department of English Tamil Nadu Coimbatore India Saveetha College of Liberal Arts and Sciences Simats Department of Commerce General Tamil Nadu Chennai India Chandigarh University Mohali Punjab Gharuan India

ISBN: (纸本)9798350359688

Fraud detection in blockchain transactions is critical as the technology becomes more integrated into financial systems. Traditional rule-based systems have become ineffective against new fraudulent tactics. To solve these problems, a unique machine learning-based approach for detecting fraud in blockchain transactions is proposed in the study. Advanced machine learning techniques such as Random Forests, Support Vector Machines, and Isolation Forest are used to overcome existing systems' limitations. The data are collected meticulously and preprocessed, feature engineering is employed, and the models are trained to improve accuracy and adaptability. Compared to existing systems, the proposed technique outperforms with an accuracy of 0.98, precision of 0.94, recall of 0.93, and a false positive rate of 0.06. It also has an average processing time of 70 milliseconds, improving computational efficiency. The study represents a significant step forward in blockchain transaction security, providing dynamic protection against fraudulent activity and bolstering the integrity of decentralized financial ecosystems. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Large Language Models with Google knowledge Integration for Rapid Response in Healthcare Emergencies

Enhancing Large Language Models with Google Knowledge Integr...

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2024 IEEE international conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Das, Suparna Khondakar, Kamil Reza Mazumdar, Hirak Woxsen University School of Technology Hyderabad India Woxsen University School of Science Hyderabad India Adamas University Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331539948

We propose a novel framework that leverages knowledge from Google and utilizes Generative Pretrained Trans-former (GPT) to enhance the capabilities of GPT. The GPT's knowledge and the responses generated from it are based on the data till 2021, it may generate incorrect responses for the questions that require knowledge after the year 2021. With the proposed architecture Large Language Models (LLMs) like GPT can be enhanced to produce responses without a knowledge barrier. Also, it is expected to provide much better answers as having access to Google as the knowledge regarding the specific domain would have been more informative. Emerging diseases and global health crises require rapid, precise, and adaptive responses. While advanced Artificial Intelligence (AI) systems, like GPT, can provide valuable support in many aspects of healthcare, they are not a substitute for the expertise, experience, and real-time decision-making abilities of healthcare professionals and epidemiologists. The response to novel and recent diseases often necessitates extensive collaboration among scientists, healthcare workers, and public health organizations, as well as access to specialized medical knowledge and resources. AI systems can assist in data analysis, information retrieval, and decision support, but ultimately, it is the collective human effort that drives effective responses to these complex challenges, especially when the situation is rapidly evolving or novel in nature. © 2024 IEEE.

关键词： Diseases

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Ontological knowledge-Based Representation System to Prevent Web Attacks in iMANET's

Ontological Knowledge-Based Representation System to Prevent...

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2024 international conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Chandani, Priyanka Gupta, Anand Kumar Manivasagam Noida Institute Of Engineering And Technology Department Of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India

ISBN: (纸本)9798350366846

This paper implies securing Internet-based Mobile Ad-hoc Networks (iMANETs) with semantic web techniques. To illustrate safety issues, ontologies will be utilised rather than taxonomies. These ontologies can be added to an intrusion detection system's (IDS) information repository. By employing inference based on semantic relations, intrusion detection systems can identify and incorporate upcoming attacks into their current knowledge base. © 2024 IEEE.

关键词： Ontology

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