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

共 37441 条记录，以下是771-780 订阅

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

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

引用

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...

引用

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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A Fuzzy Neural Network-Based Time Series Analysis Model for Condition Monitoring of Work Equipment 4

A Fuzzy Neural Network-Based Time Series Analysis Model for ...

引用

4th international conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Chen, Shizhe Dong, Wei Fang, Lei Qian, Weini Hangzhou Electric Power Equipment Manufacturing Co. Ltd Hangzhou China Hangzhou Electric Power Equipment Manufacturing Co. Ltd Jiande Guanyuan Complete Electrical Manufacturing Branch Hangzhou China

ISBN: (纸本)9798331531409

This paper aims to overcome the shortcomings of existing technologies and provide a fuzzy neural network-based time series analysis model for tool status monitoring, which has good thermal insulation effect, long service life of sound insulation materials, and good sound insulation effect. Its basic principle is as follows. Fuzzy neural network (FNN) is a comprehensive combination of fuzzy logic (FL) and neural network (NN), which is based on the principle that fuzzy inference systems can be represented by multi-level feedforward neural networks. Compared with end-to-end neural network deep learning algorithms, it has faster processing speed, lower hardware cost requirements, and more accurate data detection accuracy. © 2024 IEEE.

关键词： Thermal insulation

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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...

引用

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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Aligning the Representation of knowledge Graph and Large Language Model for Causal Question Answering

Aligning the Representation of Knowledge Graph and Large Lan...

引用

2024 IEEE international conference on Big data, Bigdata 2024

作者： Zeng, Zefan Cheng, Qing Hu, Xingchen Liu, Zhong Shen, Jingke Zhang, Yahao Laboratory for Big Data and Decision National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350362480

Causal Question Answering (CQA) is essential for knowledge discovery, focusing on the intricate dynamics between events and entities without predefined contexts. Despite advancements of CQA models through knowledge Graphs (KGs) and Pre-Trained Language Models (PLMs), existing approaches are hindered by knowledge conflict, insufficient capacity, and limitations in information fusion. Large Language Models (LLMs) have significantly improved natural language understanding and reasoning but often suffer from causal hallucinations. To address these challenges, we introduce KLop, a framework that aligns representations of Causal knowledge Graph (CKG) and Large Language Models for CQA. KLop pre-trains a graph embedding model for entity embedding and uses a frozen LLM for text embedding. The main components of KLop are the descriptor module and the aligner module. The descriptor leverages descriptive texts generated by LLMs to create training data for knowledge alignment, while the aligner utilizes self-attention to train query tokens for modality alignment. Experiments on public CQA datasets validate that KLop outperforms various advanced baselines in reasoning accuracy, as well as achieving causal knowledge integration and joint reasoning. © 2024 IEEE.

关键词： knowledge graph

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Branchy-TEE: Deep Learning Security Inference Acceleration Using Trusted Execution Environment 35

Branchy-TEE: Deep Learning Security Inference Acceleration U...

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35th international conference on Software engineering and knowledge engineering, SEKE 2023

作者： Wang, Yulong Deng, Kai Meng, Fanzhi Chen, Zhi Yin, Mingyong Yang, Run Institute of Computer Application China Academy of Engineering Physics Mianyang China School of Cyberspace Science Harbin Institute of Technology Harbin China

Deep Learning as a Service (DLaaS) has become a remarkable trend in modern data-driven online services. Both data holders and service providers need to build on trust in third-party cloud infrastructure platforms. However, once the trust is broken, data holders’ sensitive data and service providers’ intellectual property rights will face significant security and privacy risks. In this paper, we propose a secure and efficient inference framework for deep learning in untrustworthy cloud platforms, termed Branchy-TEE, which aims to protect the confidentiality and integrity of data and models of multiple participating actors throughout the inference process using the Trusted Execution Environment (TEE). Branchy-TEE dynamically loads the inference network into the TEE on-demand based on early-exit mechanism, expecting to break the hardware performance bottleneck of the TEE. Moreover, a joint training method based on knowledge distillation for multi-exit networks is proposed, by flowing"knowledge" from the final exit with high accuracy to the early branch exit with lower accuracy. Finally, the effectiveness and efficiency of Branchy-TEE are verified through extensive experiments in real environments, while achieving an optimal balance between performance and hardware resources. © 2023 knowledge systems Institute Graduate School. All rights reserved.

关键词： Distillation

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