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

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manufacturing knowledge Graph-Based Disentangled Prediction Framework and Industrial Practices 6

Manufacturing Knowledge Graph-Based Disentangled Prediction ...

引用

6th international conference on Robotics, Control and Automation engineering, RCAE 2023

作者： Sui, Qingkai Wang, Yalin Liu, Chenliang Central South University School of Automation Changsha China

ISBN: (纸本)9798350315301

Quality prediction assumes a pivotal role in manufacturing processes, serving as a linchpin to uphold product uniformity while mitigating the incidence of defects. However, the wealth of prior knowledge inherent in the manufacturing process is often underutilized, leading to limited predictive performance. Simultaneously, the integration of fragmented knowledge in the manufacturing industry often overlooks the ubiquitous presence of time series data, resulting in incomplete knowledge. To address these issues, this paper proposes a manufacturing knowledge graph-based disentangled prediction framework (MFKG-DP Framework). First, a comprehensive manufacturing knowledge graph construction method is proposed, which completes graph relationships through Bayesian network analysis of time series. The second part is the knowledge graph-based disentangled prediction method, which aims to design disentangled mapping matrixes according to the variable relationship provided by the knowledge graph. This facilitates the segmentation and disentanglement of hidden layer features, contributing to enhanced prediction capabilities. Furthermore, this paper incorporates the proposed disentanglement method into deep state space models, creating a disentangled deep state space model called DisSSM. Finally, the effectiveness of the proposed MFKG-DP Framework and DisSSM is validated in industrial processes within manufacturing enterprises. © 2023 IEEE.

关键词： Deep learning

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An adaptive time-convolutional network online prediction method for ocean observation data 36

An adaptive time-convolutional network online prediction met...

引用

36th international conference on Software engineering and knowledge engineering, SEKE 2024

作者： Li, Enjing Li, Xiang Zhao, Zhigang Wu, Lu Wang, Yinglong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Deep learning is particularly important in the field of time series data analysis, and has been applied to tasks such as marine data prediction. However, there is a’concept drift’ problem in marine observation data, which leads to performance degradation and catastrophic forgetting of traditional deep learning models used in online scenarios. For this reason, this paper proposes OL-TCN, an adaptive temporal convolutional neural network online prediction deep learning model, which is more suitable for marine online learning and inference scenarios. We make the following innovations: Inside the model, model performance is enhanced by incorporating a multi-head attention mechanism and introducing an automatic machine learning enhancement in the residual cell. Outside the model, a model repository approach is used to effectively cope with the complexity and evolutionary challenges of data streams. The experimental results verify that the OL-TCN model is effective and feasible in marine time series processing. © 2024 knowledge systems Institute Graduate School. All rights reserved.

关键词： Spatio-temporal data

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SYNERGIZING HUMAN AND MACHINE CAPABILITIES: ENHANCING HUMAN-CYBER-PHYSICAL INTERACTIONS FOR GRID COMPLIANCE IN SMART manufacturing 51

SYNERGIZING HUMAN AND MACHINE CAPABILITIES: ENHANCING HUMAN-...

引用

51st international conference on Computers and Industrial engineering, CIE 2024

作者： Gupta, Himanshu Sharma, Rishabh Dhanbad Jharkhand Dhanbad826004 India

The rapid progression of transformative technologies, including artificial intelligence (AI), the industrial Internet of Things (IIoT), big data analytics, and digital twin (DT), is revolutionizing smart manufacturing and reshaping human roles and interactions. This study focuses on Human-Cyber-Physical systems (HCPS) within the GRID (Green, Resilient, and Inclusive Development) framework to enhance human-cyber-physical interactions. The importance of integrating human-centric approaches in smart manufacturing is underscored, emphasizing sustainable practices, system resilience, and inclusivity. Using the Best-Worst Method (BWM) and Interpretive Structural Modeling (ISM), this research evaluates and ranks key factors affecting HCPS. Stakeholder Engagement via Virtual Collaboration Platforms (SEVC) emerged as a crucial independent variable, highlighting the importance of collaborative decision-making. Other significant factors include sustainable material usage, adaptive supply chains, and robust energy management systems. The ISM analysis structured these factors into a six-level hierarchy, revealing their dependencies and driving powers. MICMAC (Cross-impact matrix multiplication applied to classification) analysis further categorized these factors based on driving and dependence power, offering insights into their interdependencies. The results emphasize the critical role of inclusive and collaborative practices, sustainable materials, adaptive supply chains, and efficient energy management in enhancing HCPS. These insights guide practitioners and policymakers in prioritizing efforts to promote sustainable, resilient, and inclusive manufacturing practices. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Smart manufacturing

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Intelligent control algorithms for industrial automation systems

Intelligent control algorithms for industrial automation sys...

引用

2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 IEEE.

关键词： Genetic algorithms

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Optimizing Federated Learning Efficiency: Exploring Learning Rate Scheduling in Sparsified Ternary Compression

Optimizing Federated Learning Efficiency: Exploring Learning...

引用

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

作者： Nithyaniranjana Murthy, C. Manjula, Sh. University of Visvesvaraya College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350359688

Federated Learning facilitates the collaborative training of a deep learning model by leveraging the combined data of multiple entities, with a focus on maintaining the privacy of individual datasets. The Sparse Ternary Compression (STC) method improves gradient sparsification by integrating downstream compression, ternarization, and optimal Golomb encoding of weight updates. Although STC enhances compression techniques in federated learning, the broader scope of Federated Learning lies within the domain of machine learning. Researchers investigating improvements in STC compression techniques must consider neural network concepts and the impact of hyperparameters, such as learning rate, on convergence speed and accuracy. Increased communication across multiple devices poses challenges in achieving desired accuracy levels. Our research underscores the importance of selecting appropriate learning rate schedules when implementing algorithms like STC. Incorrect choice of learning rate schedules can hinder convergence, even if the algorithm functions effectively, emphasizing the crucial role of hyperparameters in reducing communication costs and achieving desired accuracy levels © 2024 IEEE.

关键词： Federated learning

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Research on the Impact of Listed Companies on Economic Growth: A Case Study of Hubei Province 14

Research on the Impact of Listed Companies on Economic Growt...

引用

14th international conference on Logistics and systems engineering

作者： Wang, Mengling Lv, Yishan Li, Danqing Business College Jianghan University Hubei Wuhan430056 China Manufacturing Industry Development Research Center on Wuhan City Circle Jianghan University Hubei Wuhan430056 China

ISBN: (纸本)9798331314347

Under the background of globalization and informationization, the growth pattern of regional economy is becoming more and more diversified, among which the role of listed companies is becoming more and more prominent, becoming the key force to promote the high-quality development of regional economy. Based on the panel data of 2003-2020 in prefecture-level cities of Hubei Province, this paper deeply discusses the influence of the development of listed companies on the regional economic growth. It is found that there is a positive correlation between the economic development of Hubei Province and the number of listed companies and their market value. In view of this, this paper holds that Hubei Province should make full use of the positive promotion effect of the number of listed companies and the market value growth on the economy, explore the deepening cooperation path, stimulate the vitality of enterprises, and constantly cultivate the new growth point and international competitive advantage of Hubei Province's economy by constructing the listing support system, strengthening the regional coordinated development, deepening the digital technology fusion, perfecting the talent mechanism and expanding the international cooperation, etc. © (2025) by Aussino Academic Publishing House All rights reserved.

关键词： Agricultural economics

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A Modified Deep Learning Architecture for Detecting MalariaInfected Cells

A Modified Deep Learning Architecture for Detecting MalariaI...

引用

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

作者： Shahshikiran, S. Srinivas Babu, N. Pramanik, Souvik Sathwik New Horizon College of Engineering Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798350359688

Malaria is a major public health problem because it is prevalent in tropical and subtropical regions with inadequate healthcare systems and limited resources. A correct diagnosis is necessary for prompt malaria intervention and treatment. Expert pathologists manually review blood smear images to obtain a microscopic diagnosis. Alternatively, they use a 50% accurate Rapid antigen malaria test. Advancements in Deep Learning(DL) have provided promising features in automating malaria diagnosis. Generating predictions based on patterns seen in large-scaledatasets. the DL approach, which is recommended work, provides a more reliable diagnosis by reducing the cost of diagnosis, especially in areas with limited resources. The approach we propose combines Image transformation, Feature extraction, Feature engineering, and data augmentation using the Convolutional Neural Network(CNN) architecture with an accuracy of 94.44% in classification, and by incorporating a Support Vector Machine (SVM) function as a method of activation in the final layer of the CNN design, it influences the layers. Our objective is to improve deep neural networks' performance in classifying cells that are affected by malaria parasite. © 2024 IEEE.

关键词： Diseases

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Reviewing Machine Learning Techniques in Credit Card Fraud Detection 16

Reviewing Machine Learning Techniques in Credit Card Fraud D...

引用

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： Medarhri, Ibtissam Hosni, Mohamed Ettalhaoui, Mohamed Belhaj, Zakaria Zine, Rabie MMCS Research Team LMAID ENSMR Rabat Morocco MOSI Research Team LM2S3 ENSAM Moulay Ismail University of Meknes Meknes Morocco School of Science and Engineering Al Akhawayn University in Ifrane Ifrane Morocco

ISBN: (纸本)9789897587160

The growing use of credit cards for transactions has increased the risk of fraud, as fraudsters frequently attempt to exploit these transactions. Consequently, credit card companies need decision support systems that can automatically detect and manage fraudulent activities without human intervention, given the vast volume of daily transactions. Machine learning techniques have emerged as a powerful solution to address these challenges. This paper provides a comprehensive overview of the knowledge domain related to the application of machine learning techniques in combating credit card fraud. To achieve this, a review of published work in academic journals from 2018 to 2023 was conducted, encompassing 131 papers. The review classifies the studies based on eight key aspects: publication trends and venues, machine learning approaches and techniques, datasets, evaluation frameworks, balancing techniques, hyperparameter optimization, and tools used. The main findings reveal that the selected studies were published across various journal venues, employing both single and ensemble machine learning approaches. Decision trees were identified as the most frequently used technique. The studies utilized multiple datasets to build models for detecting credit card fraud and explored various preprocessing steps, including feature engineering (such as feature extraction, construction, and selection) and data balancing techniques. Python and its associated libraries were the most commonly used tools for implementing these models. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Enhancing Waste Management Through Smart Bins 4

Enhancing Waste Management Through Smart Bins

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4th international conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Sheela, T. Snegaa, G. Rupa Devi, T. Sri Sai Ram Engineering College Department of Information Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Food waste (FW) generation is a major global issue and a top political priority. Lack of precise information regarding the volume, timing, and causes of waste creation is one of the primary causes of it. This article addresses the architecture, implementation, and exploration of an Internet of Things (IoT)-driven food waste (FW) monitoring system that gathers waste data in real-time during manufacturing and disseminates it to all parties involved in the food supply chain (FSC). A case study of a ready-meal factory includes the hardware and software design and architecture for tracking food waste (FW), how it is implemented in the plant, and the successful outcomes derived from data analysis. The case study highlights the advantages of using a digital FW tracking system, such as FW reduction. © 2024 IEEE.

关键词： Smart manufacturing

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Unlocking Water Conservation: Synergizing IoT and Machine Learning for Agricultural Irrigation Efficiency

Unlocking Water Conservation: Synergizing IoT and Machine Le...

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

作者： Praveen Kumar, K. Manjula, S.H. University of Visvesvaraya College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350359688

The traditional irrigation method often results in excessive water usage and wastage, highlighting the urgent need for smarter irrigation solutions. Using the Internet of Things (IoT) and Machine Learning (ML) to their full capacity offers a viable way to automate irrigation procedures with little to no human involvement. This paper suggests an IoT-enabled Machine Learning recommendation system that is taught to maximize water efficiency and minimize farmer intervention. IoT sensors are placed in fields to gather accurate environmental and ground data, which is then processed on a cloud-based server using Machine Learning methods to provide farmers with customized irrigation recommendations. Incorporating a feedback mechanism ensures the system's adaptability and robustness. By embracing IoT and ML technologies, farmers stand to benefit from significant improvements in water conservation, reduced operational costs, and enhanced agricultural productivity. This interdisciplinary approach not only promotes sustainable water management practices but also fosters innovation in precision agriculture, contributing to a more resilient and resource-efficient farming landscape. © 2024 IEEE.

关键词： Smart agriculture

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