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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501-510 订阅

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Cyber-Physical Attack Detection in Additive manufacturing: An LSTM-Based Autoencoder Approach Using Reconstruction Error Analysis from Side-Channel Monitoring

Cyber-Physical Attack Detection in Additive Manufacturing: A...

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2024 IEEE international Women in engineering (WIE) conference on Electrical and Computer engineering, WIECON-ECE 2024

作者： Chithras, T. Brijesh, K.J. Paul, M. Mano Raja Karthick, S. Bhuvanesh, A. Keerthanadevi, R. Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India Gregorian Institute of Technology Department of Automobile Engineering Kottayam India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

In order to identify cyber-physical threats in additive manufacturing systems, this study suggests a sophisticated technique that uses data from side-channel monitoring. Strong attack detection capabilities are guaranteed by the method's integration of many important approaches for pre-processing, analyzing, and classifying time-series data. In order to make the continuous time-series data more understandable and efficient to analyze, a pre-processing method based on defined Window Sliding (FWS) is used to divide the data into manageable windows of defined size. After that, we use Discrete Wavelet Transform (DWT) to get all the features out of each window, collecting important data from all the different frequency bands. Using Particle Swarm Optimization (PSO) to sift through the DWT coefficients and extract the most useful features improves classification performance by making sure that only the most informative characteristics are used. In order to train a deep learning (DL) approach that can identify anomalies via reconstruction errors, the optimal feature set is used. This technique is an LSTM-A autoencoder. We show that the suggested approach can differentiate between normal and attack cases with a 99% accuracy rate by applying it to the problem of side-channel attack detection in additive manufacturing. To protect cyber-physical systems from advanced cyberattacks, this method offers a scalable and adaptable solution while simultaneously increasing detection accuracy. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Digitalizing Sustainability: Product Carbon Footprint with Green Digital Twins 29

Digitalizing Sustainability: Product Carbon Footprint with G...

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29th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Balduf, Florian Müller-Zhang, Zai Treichel, Tagline Kuhn, Thomas Fraunhofer Iese Embedded Systems Engineering Kaiserslautern Germany Fraunhofer Iese Digital Twin Engineering Kaiserslautern Germany Fraunhofer Iese Embedded Systems Kaiserslautern Germany

ISBN: (纸本)9798350361230

Digitalization plays an important role in improving the sustainability of manufacturing systems. Documenting and sharing the Product Carbon Footprint (PCF) improves traceability along the value chain. However, current standards do not define in detail to what extent digitalization is necessary for calculating the PCF and what information is required. To address this problem, we present a novel approach based on Green Digital Twins (GDT) to determine the product carbon footprint in the manufacturing domain. The data models of the GDT provide a modeling approach to represent the necessary activity data required for PCF calculation. The calculation models provide a practical solution for companies to calculate the PCF based on the digitalization degree of the manufacturing system. The data models are implemented using Asset Administration Shell and submodels. The evaluation demonstrates PCF calculation for batches of products with GDT during the setting process of a drying chamber. © 2024 IEEE.

关键词： Green manufacturing

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Graph Analytics for Avian Science data 15

Graph Analytics for Avian Science Data

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15th international conference on knowledge Management and Information systems, KMIS 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Pandat, Ami Bhise, Minal Srivastava, Sanjay Distributed Databases Group DAIICT Gandhinagar India DAIICT Gandhinagar India

ISBN: (纸本)9789897586712

data management solutions are becoming increasingly necessary as more Big data applications are developed. One such area that deals with Big data is Big Graphs. Complex relationships exist in graph-based applications. Analytics and data extraction are better solutions for understanding such complex applications. data from Avian Science has shown significant growth in recent years. Graph analytics can be used to interpret complex scientific data and their relationships. This paper uses graph analytics to discuss the application of graph analytics in avian science. For the eBird dataset, four Graph Analytics techniques were identified and implemented. These methods extract information about path patterns, node popularity, connections to other nodes, and clustering. The dataset includes real-time data on bird observation and distribution. Each analytics technique extracts data from the birds’ observations. The findings show that graph analytics for avian science data can aid in predicting a wide range of crowd-sourced information. Additionally, the work can be expanded using machine learning methods. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Birds

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On deriving kernels for linear time-dependent systems 31

On deriving kernels for linear time-dependent systems

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31st international conference on Noise and Vibration engineering, ISMA 2024 and 10th international conference on Uncertainty in Structural Dynamics, USD 2024

作者： Jones, M.R. Cross, E.J. University of Sheffield Department of Mechanical Engineering Sir Frederick Mappin Building Mappin Street SheffieldS1 3JD United Kingdom

ISBN: (纸本)9789082893175

Embedding domain expertise within machine learning has allowed for hybrid models that possess both the expressivity of data-driven models whilst adhering to fundamental equations and constraints that ensure predictions are physically plausible. When forming physics-informed models, a question that is overlooked is what actually constitutes best practice for embedding prior knowledge that is held into the structure of the machine learner. Existing approaches largely ignore the influence of how physics is integrated into a model, and assumes that best practice is already being applied. To begin understanding whether the procedure of including domain expertise inside machine learning can be improved, we investigate physics-informed models that arise explicitly from a derivation path of the interaction between the physics and what remains unknown. Several case studies for modelling time-driven systems are considered, with the behaviour of the derived models compared to examples of what would likely be attempted in current practice. © 2024 Proceedings of ISMA 2024 - international conference on Noise and Vibration engineering and USD 2024 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

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CE:knowledge Tracking Model Uncertainty Assessment Method Under Trusted Artificial Intelligence

CE:Knowledge Tracking Model Uncertainty Assessment Method Un...

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2024 IEEE international conference on systems, Man, and Cybernetics, SMC 2024

作者： Bai, Jiping Kang, Bo Hu, Kexin Zheng, Qi Wang, Xiaodong Ocean University of China Faculty of Information Science and Engineering Qingdao China

ISBN: (纸本)9781665410205

Trustworthy AI is becoming a new focus in the field of artificial intelligence, and the study of its trustworthiness criteria is crucial for enhancing the trustworthiness of AI. Despite the plethora of methods that have been explored to assess the trustworthiness of AI, there remains a dearth of simple and intuitive assessment approaches. This paper focuses on the domain of knowledge tracing, combining various clustering techniques to propose a trustworthy evaluation method based on uncertainty computation. Through validation using clustering, Monte Carlo methods, and correlation analysis, our approach effectively examines the trustworthiness of multiple prominent knowledge tracing models. Employing open data sets and virtual data sets grounded in Item Response Theory (IRT), our method achieves commendable performance at a low cost, thereby providing a reference for research into the trustworthiness of knowledge tracing models. Finally, we summarize model errors and areas for improvement, and offer perspectives on scale and integrated trustworthy development. Experimental results demonstrate that our method achieved an accuracy rate of 81.04%. © 2024 IEEE.

关键词： data accuracy

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Operational System of systems Capability Evaluation: A Information Granule-Based Capability Representation and Evaluation Method 3

Operational System of Systems Capability Evaluation: A Infor...

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3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Yan, Jingjing Xiao, Weidong Zhang, Weiming Fu, Chen College of Systems Engineering National University of Denfense Technology Changsha China National Key Laboratory of Information Systems Engineering Nanjing China College of Systems Engineering National University of Denfense Technology National Key Laboratory of Information Systems Engineering Changsha China Nanjing Research Institute of Electronic Engineering National Key Laboratory of Information Systems Engineering Nanjing China

ISBN: (纸本)9798350365443

The modern military confrontations often involve simultaneous activities such as electromagnetic warfare, network information warfare, and weapon firepower confrontation. These confrontations typically take the form of a system of systems (SoS), which is a complex system with diverse data types, large volumes of data, and intricate data transmission networks. To ensure absolute superiority in military confrontations, combat commanders must be able to extract knowledge from the complex SoS data and intuitively understand and evaluate its various capabilities in order to promptly implement appropriate command measures. This study proposes a granular description-based method for evaluating SoS capabilities to achieve rapid and intuitive cognition of SoS capabilities. The method aims to transform the problem of evaluating SoS capabilities into a granular modeling problem consisting of three stages: the first stageinvolveso rganizing t he S oS data a nd constructing a dataset encompassing SoS capability;the second stage focuses on constructing capability information granules;the third stage develops an information granule-based model using historical SoS data. Based on the SoS capability dataset generated by the constructed SoS-level combat confrontation simulation environment, a series of experiments concerning the generated dataset are implemented to exhibit the process and effect of the proposed granular-based evaluation method. © 2024 IEEE.

关键词： Information granules

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Advanced Process Control in manufacturing Using IoT Devices and Artificial Neural Networks 4

Advanced Process Control in Manufacturing Using IoT Devices ...

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4th international conference on Sustainable Expert systems, ICSES 2024

作者： Sumathi, V. Ramesh, S. Basavaraddi, Chethan Chandra S. Visumathi, J. Ishwarya, M.V. Srinivasan, S. Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India R&d Don Bosco Institute of Technology Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. Recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems. © 2024 IEEE.

关键词： Adaptive control systems

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Temperature Drift Compensation of Light Scattering PM Sensor Based on BP Neural Network 18

Temperature Drift Compensation of Light Scattering PM Sensor...

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18th international conference on Intelligent systems and knowledge engineering, ISKE 2023

作者： Shen, Zhicong Niu, Junying You, Zongzheng Midea Refrigeration Equipment Co. Ltd R&D Department Foshan China Intelligent Manufacturing School Shunde Polytechnic Foshan China China Telecom Co. Ltd Department of Wireless Network Foshan China

ISBN: (纸本)9798350318401

As the core component of light scattering particulate matter (PM) sensor, laser diode is prone to temperature increase during operation, resulting in measurement errors. Thus, a backpropagation (BP) neural network-based temperature drift compensation algorithm is proposed to reduce the error. Simulation and experimentation illustrate the effectiveness of the proposed method in temperature drift compensation. © 2023 IEEE.

关键词： Light scattering

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Peculiarities of ADDIE Implementation When Teaching engineering Students Professional Foreign Language Online 26th

Peculiarities of ADDIE Implementation When Teaching Engineer...

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26th international conference on Interactive Collaborative Learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Mambetova, Roza Baltabaevna Semushina, Elena Yurievna Karakalpak State Univ Nukus Uzbekistan Kazan Fed Univ Kazan Russia

ISBN: (纸本)9783031526664;9783031526671

The purpose of the article is to present the use of instructional design ADDIE on the basis of the course "Professional foreign language" (English) worked out for the program "Translator in the sphere of professional communication" (Chemical engineers) which is operated online at technological university. The necessity of proper organization of workmakes it important to use the instructional design, which must be flexible as it can be necessary to change it quickly depending on the demands of the engineering industry and the interests of the graduates. The model of instructional design ADDIE is considered appropriate. The main peculiarity of it here is that the second stage "Design" is combined with the system of backward design, because the program has two forms of assessment: the final project and the standard exam. The final project is an example of interdisciplinary approach and can be strongly recommended as a part of ultimate assessment for advanced students who are selected and trained with the help of differentiated approach. Three learning paths (advanced, intermediate, basic) give the opportunity for students to demonstrate their skills and knowledge to potential employers from international companies when presenting the final project. Such methods of investigation are used: observation, analysis of previous experience and creative activity of the students, comparison of two forms of education, methods of data collection and accumulation, methods of control and measurement, and methods of implementing research results in pedagogical practice.

关键词： Instructional design ADDIE online study professional language English

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Optimization of Methods for Querying Formal Ontologies in Natural Language Using a Neural Network 16

Optimization of Methods for Querying Formal Ontologies in Na...

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16th international conference on knowledge engineering and Ontology Development, KEOD 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Ondo, Anicet Lepetit Capus, Laurence Bousso, Mamadou Dep. of Computer and Software Engineering Laval University QuebecQCG1V 0A6 Canada Dep. Computer Science Iba Der Thiam University Thies967 Senegal

ISBN: (纸本)9789897587160

A well-designed ontology must be capable of addressing all the needs it is intended to satisfy. This complex task involves gathering all the potential questions from future users that the ontology should answer in order to respond precisely to these requests. However, variations in the questions asked by users for the same need complicate the interrogation process. Consequently, the use of a question-answering system seems to be a more efficient option for translating user queries into the formal SPARQL language. Current methods face significant challenges, including their reliance on predefined patterns, the quality of models and training data, ontology structure, resource complexity for approaches integrating various techniques, and their sensitivity to linguistic variations for the same user need. To overcome these limitations, we propose an optimal classification approach to classify user queries into corresponding SPARQL query classes. This method uses a neural network based on Transformer encoder-decoder architectures, improving both the understanding and generation of SPARQL queries while better adapting to variations in user queries. We have developed a dataset on estate liquidation and Python programming, built from raw data collected from specialist forums and websites. Two transformer models, GPT-2 and T5, were evaluated, with the basic T5 model obtaining a satisfactory score of 97.22%. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Structured Query Language

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