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

共 37467 条记录，以下是771-780 订阅

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Leveraging Trustworthy AI and IIoT for Cost-Efficient Multimodal Quality Control in Metallurgic Additive manufacturing 5

Leveraging Trustworthy AI and IIoT for Cost-Efficient Multim...

引用

5th IEEE international conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Buescher, Jan Zajaczkowski, Jonas Blecking, Christian Baumann, Ingor Mohle, Roman Wagstyl, David Rademacher, Hans-Georg Tillmann, Wolfgang Deuse, Jochen Institute of Production Systems TU-Dortmund Dortmund Germany Institute of Materials Engineering TU-Dortmund Dortmund Germany Kupferverband e.V. Department of Industrial Applications Duesseldorf Germany RIF e.V. Production Systems Dortmund Germany RIF e.V. Materials Engineering Dortmund Germany Centre for Advanced Manufacturing UTS Sydney Sydney Australia

ISBN: (纸本)9798350364743

Ensuring consistent quality in metallurgic Additive manufacturing (AM) processes, such as Cold Gas Spraying (CGS), is a significant challenge due to the inherent process variability in part quality. This study explores the integration of trustworthy Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) to multimodal quality control, with the goal of increasing in-situ test coverage of manufactured parts and process stability. An open-source software solution for data engineering and analysis was developed using the Design Science Research methodology. Experiments were conducted on a CGS system utilizing CuZn30, a copper-zinc alloy material, as a representative case study. The developed AI models demonstrated high efficiency in identifying quality deviations across multiple quality characteristics, including oxidation, geometric inaccuracies and porosity (F1-Score=0.998). The results suggest that the combination of IIoT-measurement components and AI is a cost-effective way to significantly improve test coverage, enabling the control of multiple quality attributes and process reliability in metallurgical AM using low-cost sensors and open-source software. This research contributes to the advancement of cost-effective quality control systems and provides a case study of how AI can be used to improve the reliability and stability of advanced industrial manufacturing processes with respect to multiple quality characteristics and data modalities, paving the way for mass customization as proposed in the Industry 4.0 vision. © 2024 IEEE.

关键词： Zinc alloys

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Ethical and Legal Implications of AI on Business and Employment: Privacy, Bias, and Accountability

Ethical and Legal Implications of AI on Business and Employm...

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

作者： Reddy, K. Saketh Kethan, Manyam Mahabub Basha, S. Singh, Arti Kumar, Praveen Ashalatha, D. International Institute of Business Studies Department of Management Bengaluru India International Institute of Business Studies Department of Commerce Bengaluru India Kristu Jayanti College Autonomous Department of Commerce Bangalore India Chandigarh University India Telangana Medchal India

ISBN: (纸本)9798350359688

The proliferation of Artificial Intelligence (AI) in business and employment contexts necessitates a critical examination of the ethical and legal implications surrounding privacy, bias, and accountability. As AI systems become integral to decision-making processes, concerns about data privacy violations and algorithmic biases have heightened. This paper delves into these challenges, presenting a comprehensive framework to address the ethical and legal intricacies associated with AI deployment. Drawing on a thorough literature survey, we identify the gaps in current practices and propose a multifaceted approach to mitigate privacy infringements, combat bias, and establish accountability mechanisms. Our methodology combines quantitative and qualitative analyses, examining existing AI systems to gauge their impact on privacy and bias. The proposed implementation model integrates advanced encryption for privacy preservation, bias-detection algorithms for algorithmic fairness, and transparent decision-making processes to enhance accountability. The results showcase significant advancements in each domain, providing a foundation for responsible AI deployment in business and employment. This study contributes to the ongoing discourse on ethical AI by offering practical solutions to the evolving challenges, ultimately promoting a harmonious integration of AI technologies that align with societal values and legal standards. © 2024 IEEE.

关键词： Differential privacy

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A Robust system for disaster detection and management model

A Robust system for disaster detection and management model

引用

2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Karthikeyan, M.P. Mani, A. Indhumathi, S. Jothika, B. Deepika, Kongara Department of CSE R.M.K.Engineering College India Department of CSE S.A.Engineering College India

ISBN: (纸本)9798350305708

Numerous advancements are produced as a result of the development of embedded computer systems and the science of sensors. In this research, a unique sensing network framework for the disaster gathering and analysis, comprising data on the actual surroundings and prospective surviving' messages, is discussed. A centralized information server and a large number of sensor devices make up the overall structure. The main database server supervises the sensor devices' internal ZigBee system, which is coordinated by them. For the purpose of being able to deliver comprehensive catastrophic event data globally, the server must be linked to the internet. An automated configuration that can travel to catastrophe sites and assess the viability of an individual's involvement was built into the framework. © 2023 IEEE.

关键词： Communication systems Embedded systems Neural networks Sensors Wireless communications

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Fault analysis and impact weight calculation of marine diesel engine unable to start based on fault tree and analytic hierarchy process 4

Fault analysis and impact weight calculation of marine diese...

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4th international conference on Mechanical engineering, Intelligent manufacturing, and Automation Technology, MEMAT 2023

作者： Li, Zhaoqiang Yang, Haijun Hou, Linli Wang, Lingling Non-Commissioned Officer Department Binzhou Polytechnic 919 Huanghe 12th Road Shandong Binzhou256600 China Training Management Department Binzhou Polytechnic 919 Huanghe 12th Road Shandong Binzhou256600 China Mechanical and Electrical Engineering Department Binzhou Polytechnic 919 Huanghe 12th Road Shandong Binzhou256600 China

ISBN: (纸本)9781510674820

In response to the malfunction of "marine diesel engine unable to start", the possible causes of the malfunction were deduced and a fault tree model was established. On this basis, the fault tree model was transformed into a hierarchical analysis model, and the target layer, criterion layer, and solution layer were constructed. Through expert consultation and data collection, a judgment matrix for each layer was constructed, and the effectiveness of the judgment matrix was verified. The impact weights of each base event on the top event were determined, and targeted suggestions were proposed for the management of marine diesel engine systems. © 2024 SPIE.

关键词： Diesel engines

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Expediting Multiple Projects Timelines with State-of-the-Art Dashboard Management systems to Improving Efficiency and Minimizing Delays 8

Expediting Multiple Projects Timelines with State-of-the-Art...

引用

8th international conference on Information Technology, InCIT 2024

作者： Saputra, Muhardi Pratami, Devi Chaeruddin, Andi Muhammad Fachri Telkom University Department of Infomation System Indonesia Telkom University Department of Industrial Engineering Indonesia Universiti Teknologi Malaysia Faculty of Mechanical Engineering Department of Materials Manufacturing and Industrial Engineering Malaysia

ISBN: (纸本)9798350366303

A program is a collection of interrelated projects, programs, and other related activities that cannot be managed individually. One Higher Education Institution (HEI) was awarded a program worth over USD 3 million to build an education-integrated application that will serve academic and non-academic processes. The program had severe challenges since it required tight monitoring and control due to its enormous project value. This study aims to design a dashboard to reduce delays and enhance efficiency. The solution was achieved by integrating a Dashboard Management System using the waterfall method. It was developed using Google Spreadsheet due to its familiarity and usability aspect compared to other project management systems that require huge investment and knowledge to use. The study shows that the program management dashboard significantly improved overall program efficiency. The program has reached 37.29% schedule acceleration of work completion. It indicates that the simple information system and adopting the formal standard of program management will increase the success of program completion. © 2024 IEEE.

关键词： Efficiency

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A Domain Adaptive Feature Relevance Analysis for Human Activity Recognition using Transfer Learning

A Domain Adaptive Feature Relevance Analysis for Human Activ...

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2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Preethy Jemima, P. Madhurika, K. Singh, Gobind Pandey, Adarsh Department of Computer science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

Wearable electronics are electronic gadgets designed to be incorporated into clothing, worn on the body, or integrated into other items. These devices collect data, provide information, and perform specific tasks. Human Activity Recognition (HAR) utilizes Transfer Learning algorithms to track movements using sensor data from devices like accelerometers and gyroscopes. HAR finds applications in security, fitness tracking, and health monitoring systems. Utilizing pretrained models and engineered features from these models aids in creating accurate HAR systems. Transfer learning reduces data requirements and simplifies feature engineering, making it a valuable approach for HAR, especially in diverse real-world scenarios. © 2023 IEEE.

关键词： Deep learning Fine-tuning HAR Neural Networks Pre-processing Transfer learning

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Enhancing Object Detection with Hybrid dataset in manufacturing Environments: Comparing Federated Learning to Conventional Techniques 1

Enhancing Object Detection with Hybrid dataset in Manufactur...

引用

1st international conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Hegiste, Vinit Walunj, Snehal Antony, Jibinraj Legler, Tatjana Ruskowski, Martin Rptu Kaierslautern-Landau Machine Tools and Control Systems Kaiserslautern Germany Kaiserslautern Germany

ISBN: (纸本)9798350348637

Federated Learning (FL) has garnered significant attention in manufacturing for its robust model development and privacy-preserving capabilities. This paper contributes to research focused on the robustness of FL models in object detection, hereby presenting a comparative study with conventional techniques using a hybrid dataset for small object detection. Our findings demonstrate the superior performance of FL over centralized training models and different deep learning techniques when tested on test data recorded in a different environment with a variety of object viewpoints, lighting conditions, cluttered backgrounds, etc. These results highlight the potential of FL in achieving robust global models that perform efficiently even in unseen environments. The study provides valuable insights for deploying resilient object detection models in manufacturing environments. © 2024 IEEE.

关键词： Federated learning

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Electric Vehicle Fault Battery Analyzer Using Internet of Things

Electric Vehicle Fault Battery Analyzer Using Internet of Th...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Kamatchi Kannan, V. Muniraj, C. Sankarganesh, R. Parimalsundar, E. Thrinath, B.V. Sai Ponmurugan, P. Knowledge Institute of Technology Salem India K.S.R. College of Engineering Tiruchengode India Mohan Babu University Tirupati India Chennai600078 India

ISBN: (纸本)9798331509828

This research work focuses on a comprehensive solution that evaluates and monitors the health of electric vehicle batteries in real time is the Internet of Things (IoT)-based Electric Vehicle (EV) Battery Fault Analyzer. A micro controller, an IoT module, sensors, a cloud platform, and a user interface are all included in the framework to facilitate efficient data collection, investigation, and client alerting. The core components are individual battery cell management and monitoring, a micro controller for data acquisition, and an Internet of Things module for seamless cloud connectivity. A few of the sensors that provide crucial information about the state of the battery include accelerometers, voltage, current, and temperature. After this, data has been securely transmitted, the condition of the battery is evaluated by fault detection algorithms and data analysis processes on a cloud platform. © 2025 IEEE.

关键词： Fault detection

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From data to Learning: The Scientific Approach to AI-Enhanced Online Course Design 8

From Data to Learning: The Scientific Approach to AI-Enhance...

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8th international conference on Big data, knowledge and Control systems engineering, BdKCSE 2023

作者： Blagoev, Ivaylo Vassileva, Gergana Monov, Vladimir Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9798350313246

This article explores a transformative approach to online education, driven by the integration of Generative Artificial Intelligence (AI). We present a comprehensive methodology for the creation of interactive online courses, emphasizing four essential steps: content generation powered by AI, division of content into manageable knowledge bites, diversification of content types, and the automated design of course screens with interactive elements and visual assets. The journey begins with AI-driven content creation, leveraging advanced algorithms to generate educational materials efficiently. These materials are then segmented into bite-sized, self-contained knowledge bites, following the principles of microlearning. The diverse array of content types within each knowledge bite enhances engagement and comprehensive understanding. © 2023 IEEE.

关键词： Curricula

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Multi-Task Regression with Process knowledge-Based Forest Learners in Process Industries 19

Multi-Task Regression with Process Knowledge-Based Forest Le...

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19th IEEE international conference on Automation Science and engineering, CASE 2023

作者： Sun, Linjin Ji, Yangjian Peng, Tao School of Mechanical Engineering Zhejiang University The State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang Key Laboratory of Advanced Manufacturing Technology Hangzhou310058 China

ISBN: (纸本)9798350320695

The multi-task regression is of great significance for engineers in process control. It can not only help achieve more than one target variable estimation at once, but more importantly, it offers an opportunity for target variables to share knowledge. However, due to the complexity and nonlinearity of the production process that contains interconnected variables in process industries, it is challenging for conventional regression methods to obtain accurate results. To fill the gaps, more black-box artificial intelligence models are proposed and applied to multi-task regression, while the process knowledge is often ignored during modeling. In this paper, a solution for adopting process knowledge-based learners for multi-task regression is proposed. Specifically, a two-stage learning structure with multiplicative integration weighted-random forest is offered. The initial estimated values of target variables are achieved from the separated base learners in the first stage, and then an integrated model is learned based on the previous predictions. In this manner, the knowledge of target variables and process knowledge among process data can ultimately be learned after training. The proposed method is finally applied to a VRM system in a case study, and the results verify its superiority over baseline methods. © 2023 IEEE.

关键词： Regression analysis

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