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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531-540 订阅

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Enhancing Waste Management Through Smart Bins 4

Enhancing Waste Management Through Smart Bins

引用

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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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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AI-Enhanced Pedagogy in Higher Education: Redefining Teaching-Learning Paradigms

AI-Enhanced Pedagogy in Higher Education: Redefining Teachin...

引用

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

作者： Kakhkharova, Matlyuba Tuychieva, Sayyora International Islamic Academy of Uzbekistan Department of International Relations and Social Sciences Tashkent Uzbekistan

ISBN: (纸本)9798350359688

This paper delves into the transformative landscape of higher education through the lens of AI, introducing a paradigm shift in teaching and learning methodologies. The exploration of AI-enhanced pedagogy emerges as a pivotal response to the dynamic challenges faced by educational institutions, aiming to redefine conventional approaches. The core objective is to harness the potential of artificial intelligence in order to individualize and optimize the learning experience. By synthesizing advanced machine learning algorithms and intelligent systems, the proposed framework creates an adaptive learning environment tailored to the unique needs and preferences of each student. The paper conducts a thorough literature survey, unveiling gaps in current research, and positions the proposed work within the context of existing knowledge. The envisioned methodology involves the strategic integration of data collection, preprocessing, and algorithm selection, ensuring the efficacy of the AI-enhanced pedagogical model. The implementation model, discussed in detail, incorporates natural language processing, recommendation systems, and learning analytics to construct a scalable and customizable educational framework. The results, drawn from the implementation, showcase improvements in student performance, engagement, and satisfaction. In conclusion, this paper advocates for the widespread adoption of AI-enhanced pedagogy, emphasizing its potential to revolutionize higher education by providing a personalized and effective learning experience for students and educators alike. Future research directions and challenges are also contemplated, contributing to the ongoing discourse on the transformative role of AI in education. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Visualization of Association Rule Mining Using the Trie of Rules 16

Efficient Visualization of Association Rule Mining Using the...

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： Kudriavtsev, Mikhail McCarren, Andrew Lee, Hyowon Bezbradica, Marija Dublin City University Dublin Ireland Insight Centre for Data Analytics Dublin City University Dublin Ireland Adapt Research Centre Dublin City University Dublin Ireland

ISBN: (纸本)9789897587160

Association Rule Mining (ARM) is a popular technique in data mining and machine learning for uncovering meaningful relationships within large datasets. However, the extensive number of generated rules presents significant challenges for interpretation and visualization. Effective visualization must not only be clear and informative but also efficient and easy to learn. Existing visualization methods often fall short in these areas. In response, we propose a novel visualization technique called the"Trie of Rules." This method adapts the Frequent Pattern Tree (FP-tree) structure to visualize association rules efficiently, capturing extensive information while maintaining clarity. Our approach reveals hidden insights such as clusters and substitute items, and introduces a unique feature for calculating confidence in rules with compound consequents directly from the graph structure. We conducted a comprehensive evaluation using a survey where we measured cognitive load to calculate the efficiency and learnability of our methodology. The results indicate that our method significantly enhances the interpretability and usability of ARM visualizations. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Association rules

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REDUCING data REQUIREMENTS FOR WIENER SERIES IDENTIFICATION WITH GAUSSIAN PROCESS PRIORS

REDUCING DATA REQUIREMENTS FOR WIENER SERIES IDENTIFICATION ...

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ASME international Design engineering Technical conferences / 43rd Annual Computers and Information in engineering conference (IDETC-CIE) / 19th international conference on Multibody systems, Nonlinear Dynamics, and Control (MSNDC)

作者： Massingham, Joseph Nielsen, Ole Butlin, Tore Univ Cambridge Cambridge England Bose Corp Framingham MA USA

ISBN: (纸本)9780791887387

System identification of nonlinear dynamical systems aims to predict the output of a system for a given input. In many engineering applications, the underlying physics are not fully understood and so there is no analytical solution. The Wiener series is a classical data-driven technique that decomposes the system response into a set of orthogonal functionals of increasing order. Unlike standard black-box algorithms, such as neural networks, the series is highly interpretable and can offer insight into the nonlinearities present. To date, in order to calculate higher order terms in the Wiener series, vast quantities of data are needed. In this paper, a novel formulation of the Wiener series is developed in the frequency domain which applies to general stochastic inputs with an arbitrary spectrum. It is enhanced by placing Gaussian process priors over the Wiener kernels to enforce prior knowledge of their structure. This significantly reduces the quantity of data required for inference and has the benefit of enabling the calculation of the third order kernel for systems with long memory. The benefits were demonstrated in initial investigations using an idealised nonlinear oscillatory system. Decomposition of the system response into Wiener functionals also sheds light on the learnability of nonlinear dynamical systems, which could be used to assess the value of collecting additional data.

关键词： Artificial neural networks System identification Algorithms Nonlinear dynamical systems Physics engineering systems and industry applications

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Improving Healthcare data Security Using Cheon-Kim-Kim-Song (CKKS) Homomorphic Encryption

Improving Healthcare Data Security Using Cheon-Kim-Kim-Song ...

引用

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

作者： Sathishkumar, P. Pugalarasan, K. Ponnparamaguru, C. Vasanthkumar, M. K. S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode637 215 India

ISBN: (纸本)9798350359688

In recent years, the proliferation of mobile devices in healthcare settings has revolutionized patient care delivery and medical data management. However, the increased reliance on mobile platforms for collecting, transmitting, and storing sensitive healthcare data has raised concerns regarding data security and privacy. To address these challenges, this paper proposes a novel approach for healthcare mobile data security leveraging the CKKS (Cheon-Kim-Kim-Song) fully homomorphic encryption scheme. CKKS offers the ability to perform secure computations on encrypted data, enabling healthcare applications to protect patient privacy while preserving the confidentiality of sensitive medical information. This paper presents a comprehensive analysis of the CKKS scheme's applicability to healthcare mobile data security, including parameter selection, encryption, homomorphic operations, and performance optimization techniques tailored to resource-constrained mobile devices. Furthermore, the paper discusses the integration of CKKS encryption into healthcare mobile applications, highlighting its role in safeguarding patient records, sensor data, and diagnostic information. Through experimental evaluations and performance benchmarks, we demonstrate the feasibility and effectiveness of the proposed approach in ensuring healthcare mobile data security without compromising computational efficiency. Overall, this paper contributes to advancing the state-of-the-art in healthcare information security by introducing a practical and scalable solution for protecting sensitive medical data on mobile devices using the CKKS encryption scheme. © 2024 IEEE.

关键词： Differential privacy

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Monitoring of manufacturing systems: Applying Deep Learning Algorithms to Improve Gas Turbine Performance and Sustainability 12

Monitoring of Manufacturing Systems: Applying Deep Learning ...

引用

2024 12th international conference on systems and Control, ICSC 2024

作者： Madjour, Hassina Zermane, Hanane Batna2 University Laboratory of Automation and Manufacturing Engineering The Department of Industrial Engineering Algeria

ISBN: (纸本)9798331531812

Gas turbines are considered one of the most important energy-generating systems in the world, covering the energy shortage resulting from the increasing electricity demand. Although the energy they produce is clean, they face a major environmental challenge, and this is due to the pollution they cause through the gases emitted during their operation. Carbon Oxides (CO) and Nitrogen Oxides (NOx) gas are some of the most important of these gases, and measuring their proportions in emissions has become an urgent matter. This measure contributes to many positive improvements. First, preserving the environment and air pollution by reducing the emission rates of greenhouse gases, which affect warming. Second, knowledge of the proportions of these gases makes us fully aware of the system's status, which allows us to develop methods for monitoring it using modern technologies. A prognosis of the proportions of emitted gases is one of the good solutions that contribute to the sustainability of gas turbines. In this work, we have proposed an intelligent model to predict the proportion of (CO) and (NOx), based on the Long Short-Term Memory (LSTM) this algorithm gave good results compared to the convolution neural network (CNN 1D). © 2024 IEEE.

关键词： Low emission

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Enabling Digital Twin Using MEMS and Sensor Technology with Historic data: From data Fusion to knowledge Fusion 6

Enabling Digital Twin Using MEMS and Sensor Technology with ...

引用

6th international conference on Electronic engineering and Informatics, EEI 2024

作者： Xie, Haiyan Sally Shi, Owen Gandla, Sai Ram Bhattacharya, Mangolika Illinois State University Dept. of Technology NormalIL United States Univ. of Illinois at U-C Dept. of Mathematics ChampaignIL United States Illinois State University Dept. of Cs NormalIL United States

ISBN: (纸本)9798350353594

Microelectromechanical systems and Sensor Technology (MEMS-ST) can be used together with historical data to enable digital twins. This paper presents a novel framework that transitions from the data fusion of MEMS-ST to the knowledge fusion necessary for estimating the strength capacity of highway bridges. This innovative approach provides critical information for bridge design, transportation planning, and the assessment of existing pavement and infrastructure. The unique framework integrates real-time data from MEMS-ST systems installed on a bridge with historical records and engineering standards, addressing a significant gap in current methodologies. Through a comprehensive literature review, this study evaluates the existing reports and articles on developing bridge digital twins to measure live loads on highways. Utilizing datasets from sensors and the National Bridge Inventory, the proposed framework processes data based on the Load and Resistance Factor Design method and continuous multivariate probability distributions. The innovative application of the Dempster-Shafer Theory within the knowledge fusion framework distinguishes this work, enabling the combination of different sources (86, 126% increase) of evidence into a cohesive global belief. The results demonstrate that this framework can automate the analysis of vast amounts of data, improve efficiency (615,000 bridges in NBI and 93.1M truck data), and provide more reliable insights compared to traditional methods. Specifically, it enhances the safety and stability of bridge structures (Credibility 1.088;Plausibility 1.089) while offering new ways to design and maintain bridges more effectively. This paper sheds light on bridge digital twin systems. © 2024 IEEE.

关键词： Microchannels

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A Comprehensive Analysis of knowledge Graph-based Social Recommendation System 1

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Navuluri, Madhavilatha Bhuyan, Hemanta Kumar RGUKT-IIIT Department of Computer Science and Engineering Ongole India Department of Information Technology and Computer Applications Guntur India

ISBN: (纸本)9798331529246

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems. © 2025 IEEE.

关键词： knowledge graph

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Application of UML Descriptive Modeling Techniques in Model Driven engineering of Information Security systems 8

Application of UML Descriptive Modeling Techniques in Model ...

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

作者： Gaidarski, Ivan Institute of Robotics Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9798350313246

To be competitive, today's organizations must protect their corporate information. Directives, regulations, and legal provisions, information security standards such as ISO, as well as sector-specific regulations and best practices such as SOX, HIPAA, and PCI are commonly used to protect information. Usually, however, this approach is not sufficient, and therefore a complex approach is needed, which only an information security system (ISS), specially designed for the relevant activity can provide. For this purpose, we present a method for designing ISS using Model-driven engineering and the use of the object-oriented language UML. We show how it can be used to represent models, both their architecture and their dynamic behavior. We also show how model transformation can be implemented using UML. © 2023 IEEE.

关键词： Security of data

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