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

共 36972 条记录，以下是351-360 订阅

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Bayesian Detection for Maritime Radar Targets in CG-LN Sea Clutter without Training data 16

Bayesian Detection for Maritime Radar Targets in CG-LN Sea C...

引用

16th international conference on Signal Processing systems, ICSPS 2024

作者： Xue, Jian Feng, Zhuo Li, HongEn Pan, MeiYan School of Communications and Information Engineering Xi’an University of Posts and Telecommunications China Xi'an Electronic Engineering Research Institute China

ISBN: (纸本)9781510689251

This paper addresses the problem of Bayesian adaptive detection for maritime radar targets in compound Gaussian sea clutter with random texture and speckle covariance matrix. To adaptively match sea clutter characteristics well, the prior distributions of sea clutter components are employed. Specially, we model the sea clutter texture component as a lognormal random variable, and the sea clutter speckle covariance matrix as an inverse complex Wishart random matrix. A Bayesian radar target detector is designed based on the criteria of the generalized likelihood ratio test. The proposed detector incorporates the prior knowledge of sea clutter, and, importantly, does not rely on training data. Experimental results on simulated and measured radar data show that the proposed detector outperforms its competitors when the number of training data is limited. © 2025 SPIE.

关键词： Inverse problems

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Addressing Information Bias in Multi-Modal Recommendation systems Based on Expert systems

Addressing Information Bias in Multi-Modal Recommendation Sy...

引用

2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Wang, Shuo Yang, Yue Harbin Engineering University Harbin China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The proliferation of recommendation systems has revolutionized information retrieval by helping users efficiently navigate through large data sets. However, these systems often suffer from information bias, especially in multimodal recommendation setups. This paper addresses the issue of bias mitigation in multimodal recommendation systems using expert systems. Through a comprehensive literature review, various techniques such as knowledge graph integration, multimodal fusion, and deep learning architectures are explored. Furthermore, a novel approach using dynamic expert meeting algorithms for bias mitigation is proposed. Theoretical frameworks of expert systems are discussed, highlighting their adaptive capabilities and applicability to diverse domains. Then, the methodology for addressing information bias in multimodal recommendation systems is presented, including experimental analysis and relevance tagging. The results demonstrate the effectiveness of the proposed approach in reducing bias and improving recommendation accuracy. © 2025 SPIE.

关键词： Expert systems

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Integrating Digital Twins into manufacturing Execution systems (MES) Case Study on Wastewater Filtration Pilot 4th

Integrating Digital Twins into Manufacturing Execution Syste...

引用

4th international conference on Electronic engineering and Renewable Energy systems, ICEERE 2024

作者： Embarki, Soufiane El Kihel, Yousra Laboratory of Industrial Engineering and Seismic Engineering UMP Oujda Oujda Morocco LINEACT-CESI Bordeaux France

ISBN: (纸本)9789819799749

The integration of digital twins into manufacturing Execution systems (MES) represents a significant advancement in modern industrial settings, offering solutions to various intricate challenges like resource management, environmental considerations, regulatory compliance, and supply chain disruptions. Our examination focused on the practical implications of this integration, using a water filtration test bench as a case study. Through thorough analysis, we explored the benefits and obstacles of merging digital twins with MES, unveiling its potential to enhance production processes and support sustainable applications. This re-search showcases how combining digital twins with MES can optimize production processes and promote sustainable real-world applications. Digital twins simulate manufacturing scenarios, providing valuable insights into system performance for proactive maintenance, efficient resource allocation, and informed decision-making. Integration with MES enhances data flow, enabling real-time monitoring and control, thereby boosting operational efficiency and responsive-ness. By leveraging real-time data, digital twins enable proactive equipment health monitoring, reducing unplanned downtime and improving process efficiency. Challenges like data integration and model accuracy emphasize the importance of careful implementation and ongoing refinement. Nevertheless, the promising out-comes of this study underscore the transformative potential of digital twins in revolutionizing manufacturing operations toward a more resilient, efficient, and sustainable industrial ecosystem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart manufacturing

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Deep Learning-Based Predictive Skin Disease Diagnosis and Analysis

Deep Learning-Based Predictive Skin Disease Diagnosis and An...

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

作者： Karnati, Manikundan Sai, Pavan Pittala, Rohith Kumar Radhika, K. Sugamya, K. Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence and Data Science Hyderabad India Chaitanya Bharathi Institute of Technology Department of Information Technology Hyderabad India

ISBN: (纸本)9798350359688

In modern medical diagnostics, AI integration is crucial for enhancing the prediction or classification of diseases. The primary goal of this project is to develop a multimodal deeplearning system capable of diagnosing skin diseases. The project integrates image analysis for skin conditions and the interpretation of textual descriptions of symptoms, ultimately providing an accurate diagnosis for users. It integrates deep learning and natural language processing to diagnose skin diseases. It Enhances healthcare by providing accurate diagnoses, and remedies and improving accessibility for self-diagnostics. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-time constrained dual-resource flexible job shop scheduling based on multi-objective evolutionary algorithm 4

Multi-time constrained dual-resource flexible job shop sched...

引用

4th international conference on Machine Learning and Intelligent systems engineering (MLISE)

作者： Yang, Luda Lv, Zhuoxuan Du, Baigang Guo, Jun Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

ISBN: (纸本)9798350375084;9798350375077

This paper presents a new generalization of the uniform dual-resource constrained flexible job shop scheduling problem (DRCFJSP) for automated flexible manufacturing system, considering two multi-time constraints of setup time and transportation time. Design a mixed integer nonlinear programming model with multiple optimization objectives of minimizing maximum completion time, minimizing transportation time and minimizing setup time. And a hybrid multi-objective evolutionary algorithm (HMOEA) is proposed to solve the problem. Through simulation experiments on different data sets and algorithm comparison, the effectiveness and feasibility of the proposed algorithm are verified. Finally, we designed thirteen processing cases under different conditions and compared them with four different comparison algorithms, and concluded that the HMOEA algorithm is superior in solving the DRCFJSP problem, which is of great significance to the use of algorithms for optimal design in actual production.

关键词： Flexible manufacturing systems Dual-resource constrained flexible job shop scheduling Setup and transportation time Multi-objective evolutionary algorithm

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A Methodology to Promote Circular Economy in Design by Additive manufacturing 20th

A Methodology to Promote Circular Economy in Design by Addit...

引用

20th international conference on Product Lifecycle Management: Leveraging Digital Twins, Circular Economy, and knowledge Management for Sustainable Innovation (PLM)

作者： Ianniello, Simona Bruno, Giulia Chiabert, Paolo Mantelet, Fabrice Segonds, Frederic HESAM Univ Arts & Metiers Inst Technol Lab Concept Produits & Innovat LCPI 151 Blvd Hop F-75013 Paris France Politecn Torino Dept Management & Prod Engn Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9783031625848;9783031625824;9783031625817

[Context] Within the framework of PLM;Circular Economy (CE) is an important concept that seeks to design out waste and pollution, keep products and materials in use and regenerate natural systems. Early Design Stages (EDS) are crucial because they set the foundation for the rest of the production process and can determine the product's overall functionality, usability, and manufacturing processes. [Problem] As companies increasingly recognize the benefits of transitioning to a circular economy, there is a growing need for tools and methodologies to support the design of circular products and services. [Proposal] This paper presents a CE card deck as a novel approach to facilitate the early stages of product development. The deck consists of 10 cards based on Morseletto's "Targets for a circular economy" work that represents mainstream circular economy principles and strategies and can be used by designers, engineers, and other stakeholders to generate ideas, evaluate options, and make informed decisions. The results of a pilot study with design and engineering students (Master level) suggest that the card deck can support the exploration of CE concepts and facilitate the identification of circular solutions, in a Design by AM context (DbAM). The paper concludes with a discussion of the potential benefits and limitations of the card deck approach, and its integration into a PLM framework, and gives suggestions for future research.

关键词： Circular Economy Eco-design Green manufacturing Environmental Impact Life cycle engineering Design by Additive manufacturing

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Enhancing Cybersecurity in IIoT Smart Grids: Leveraging HGAMPO for PMU data Manipulation Detection

Enhancing Cybersecurity in IIoT Smart Grids: Leveraging HGAM...

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

作者： Janani, B. Keerthana, S.D. Subathra, S. Subiksha, N. K. S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode637 215 India Sri Sai Ram Engineering College Department of Computer Science and Business Systems Chennai600044 India

ISBN: (纸本)9798350359688

The integration of Industrial Internet of Things (IIoT) technology into smart grid systems has resulted in unprecedented advantages in terms of effectiveness and dependability. Nevertheless, this interconnectivity also brings about fresh cybersecurity obstacles, specifically with the identification and prevention of Phasor Measurement Unit (PMU) data manipulation threats. Efficient feature selection is vital in improving the robustness of IIoT smart grids against such attacks. The Hybrid Green Anaconda and Marine Predictor Optimization (HGAMPO) algorithm is a promising strategy for choosing significant features in the detection of manipulation in PMU data. This paper presents a comprehensive examination of PMU data manipulation attacks, emphasises the significance of feature selection, and introduces the HGAMPO algorithm. HGAMPO is a revolutionary approach that combines the Green Anaconda Optimization Algorithm (GAOA) and Marine Predictor Optimization (MPO) to optimise feature subsets. It draws inspiration from nature to provide an innovative solution. The effectiveness of HGAMPO in IIoT smart grid contexts is proved through real-world examples and compared with older approaches. The evaluation metrics and experimental results clearly demonstrate the exceptional performance of HGAMPO in detecting attacks that manipulate PMU data. This article provides an overview of upcoming study areas and the possible influence of HGAMPO on cybersecurity in IIoT smart grids. © 2024 IEEE.

关键词： Smart power grids

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REAL-TIME PRINT TRACKING IN METAL ADDITIVE manufacturing USING ACOUSTIC EMISSION SENSORS AND VISION TRANSFORMER ALGORITHMS 19

REAL-TIME PRINT TRACKING IN METAL ADDITIVE MANUFACTURING USI...

引用

ASME 2024 19th international manufacturing Science and engineering conference, MSEC 2024

作者： Akhavan, Javid Xu, Ke Krishna, Chaitanya Vallabh, Prasad Manoochehri, Souran Department of Mechanical Engineering Stevens Institute of Technology HobokenNJ United States

ISBN: (纸本)9780791888117

Directed Energy Deposition (DED) is an additive manufacturing (AM) method with applications in the aerospace, automotive, and healthcare sectors. In such complex and high-stakes applications, accurate and reliable monitoring is indispensable for assuring fabrication quality. Conventional monitoring systems using mechanical encoders and optical devices have limitations such as wear susceptibility and line-of-sight issues respectively, thereby necessitating alternative monitoring systems. One crucial aspect often overlooked in conventional monitoring systems is the real-time printing process tracking. Achieving high accuracy in tracking is paramount for identifying and mitigating defects in real-time, ultimately leading to improvements in fabrication quality. To address this problem, this research employs acoustic emission sensors for real-time monitoring in Laser DED. These sensors augment existing monitoring methods to improve both reliability and part quality. We developed and tested two machine learning models to study acoustic data correlation with the print process tracking. The first model, based on a Hybrid Convolutional Auto Encoder (HCAE), achieved over 94% accuracy in the print head spatial localization. The second, a Transformer-based model, excelled with a 98.5% accuracy rate and computational efficiency in process tracking. Our findings promise enhanced printing process tracking and pave the way for advanced AI algorithms incorporation into AM quality monitoring. The AI-enabled methods developed can be generalized to other manufacturing applications such as Laser Powder Bed Fusion. Copyright © 2024 by ASME.

关键词： Acoustic emission testing

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Constructing Industrial knowledge Graph Through Ontology and Link Prediction 19

Constructing Industrial Knowledge Graph Through Ontology and...

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

作者： Wan, Yuwei Chen, Zheyuan Liu, Ying Packianather, Michael Wang, Rui Institute of Mechanical and Manufacturing Engineering School of Engineering Cardiff University Cardiff United Kingdom Unicmicro Science Town Commercial Plaza Guangzhou510000 China

ISBN: (纸本)9798350320695

Given that the numerous data embedded in manufacturing processes and products are separated, it is challenging to tackle and integrate heterogeneous data in industrial scenarios. In this context, an industrial knowledge graph (iKG) has been developed as a promising semantic organisation to leverage the rich information from multiple resources. However, relations are usually missing and hidden in original iKGs, which results in the necessity for iKG completion. Given these two perspectives, a framework of iKG construction is proposed based on ontology and link prediction in this study. Firstly, an ontology design framework is deployed to generate domain-centric ontologies after extracting numerous data (e.g., entities and relations). Secondly, the missing relations between each couple of entities are discovered over existing knowledge to increase the number of edges that complete and refine iKGs. Thirdly, iKG visualisation is conducted by importing data into the generated ontology. The feasibility and effectiveness of the proposed framework are substantiated and demonstrated in a case study using real-world data. © 2023 IEEE.

关键词： Ontology

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TweetFeel: Analyzing Emotions in the Twittersphere

TweetFeel: Analyzing Emotions in the Twittersphere

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

作者： Adithya Gokulan, S. Reddy, Mahati Varshith, M. Venugopalan, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

The majority of people on the planet now have access to the Internet for text, image, audio, and video communication. Through social media, people from many backgrounds share knowledge about current events and express their own opinions on them. Analyzing people's emotions is necessary to comprehend and identify the behavior of such vast amounts of textual data about them. The study focuses on information gathered from Twitter, one of the most widely used social media platforms, by examining both historical and real-time feeds and extracting emotions from them. The necessary English-language Twitter data is transformed into a vector of six emotions, and supervised learning methods like Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest, Gradient Boosting, and Multi-Layer Perceptron are utilized to identify the label of one of the basic human emotions. Gradient Boosting and SVM are the top-performing models that reported the highest F1-Score of 0.88 among the other models. Sadness and joy came out to be the best-performing emotions whereas it was comparatively difficult to predict surprise emotion. © 2024 IEEE.

关键词： Tweets

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