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

共 37040 条记录，以下是611-620 订阅

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AI-Driven Test and Measurement Automation in Electronics manufacturing 9

AI-Driven Test and Measurement Automation in Electronics Man...

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Agrawal, Anurag Vijay Raju, Kommisetti Murthy Aparna, P. Sravya, Gade Chandrashekhar, A. Ramya, J. J. P. Institute of Engineering And Technology Department of Electronics And Communication Engineering Uttar Pradesh Meerut250001 India Shri Vishnu Engineering College For Women Department of Electronics And Communication Engineering Andha Pradesh Bhimavaram534202 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Shri Vishnu Engineering College For Women Department of Electronics And Communication Engineering Andhra Pradesh Bhimavaram534202 India Faculty of Science And Technology Icfai Foundation For Higher Education Department of Mechanical Engineering Telangana Hyderabad501203 India St. Joseph's College of Engineering Omr Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600119 India

ISBN: (纸本)9798350365092

Innovative strategies to increase output while decreasing expenses and enhancing product quality are essential in the dynamic electronics manufacturing industry. A robust AI-based system for automating test and measurement tasks in electronics manufacture is detailed in this research. Manual testing and measurement is a part of traditional production procedures;it's labor-intensive, time-consuming, and prone to mistakes. Smarter, more adaptable, and more efficient processes are achieved through the automation of tests and measurements facilitated by AI. The proposed approach makes use of machine learning techniques to sift through mountains of industrial data. In order to identify patterns, outliers, and problems, the AI system examines both past and present data. Reducing the likelihood of defective items reaching the final stage of production, this proactive technique helps identify and rectify issues fast. Product quality, cost, scrap rate, and production yield are all enhanced by AI-driven test and measurement automation in electronics manufacturing. In keeping with Industry 4.0, this ground-breaking method improves the manufacturing processes of electronic devices. Industry intelligence, sustainability, and efficiency are all enhanced by it. © 2024 IEEE.

关键词： Learning systems

来源：评论

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Professional Course System of Intelligent manufacturing engineering Major 12

Professional Course System of Intelligent Manufacturing Engi...

引用

12th IEEE international conference on engineering Education, ICEED 2023

作者： Li, Yuefeng Han, Zhenyu Wu, Chunya Chen, Mingjun Zhao, Lizhong School of Mechatronics Engineering Harbin Institute of Technology Harbin City China

ISBN: (纸本)9798350307429

Major of intelligent manufacturing engineering is one of new engineering majors set up in China to serve Made in China 2025 Plan. Compared to traditional engineering majors, intelligent manufacturing engineering major is relatively new. Different universities have their own understandings of the major and set up different curriculum systems. This article studies the course system for current intelligent manufacturing majors from several universities. According to the characteristics of Harbin Institute of Technology, a course system is proposed around the concept of narrow sense manufacturing. The course system surrounds manufacturing data to form a complete system. The research in this article has a demonstrative role in the construction of intelligent manufacturing engineering major. © 2023 IEEE.

关键词： Industrial research

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ASMix: An Attention-based Smooth data Augmentation Approach 35

ASMix: An Attention-based Smooth Data Augmentation Approach

引用

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

作者： Guo, Rongxi Jiang, Wanrong Zhao, Xi Cao, Ding Liu, Guiquan University of Science and Technology of China Hefei China

data augmentation through linearly interpolating inputs and modeling targets of random samples has significantly improved predictive performance. However, data augmentation based on linear interpolation generates semantically cluttered and ambiguous text, resulting in ineffective augmentation. To address these issues, in this paper, we propose a novel data augmentation approach called Attention-based Smooth data Augmentation (ASMix). ASMix accepts the smoothed embeddings of pairwise data predicted by a masked language model (MLM) instead of one-hot embeddings, which makes the inputs more informative and context-rich. We employ the attention mechanism to select discriminative and more-attentioned parts of text hidden representations and mix up the parts containing key semantics in the hidden representations of pairwise data through a multi-token replacement strategy to augment the data of the minority class, which greatly reduces redundant information in the representations that hurts the performance of the model. On several public imbalanced text classification benchmarks, ASMix outperforms state-of-the-art data augmentation methods. In minority classes, the performance improvement of ASMix is particularly prominent. © 2023 knowledge systems Institute Graduate School. All rights reserved.

关键词： Embeddings

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25th international conference on Web Information systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

引用

25th international conference on Web Information systems engineering, WISE 2024

ISBN: (纸本)9789819605781

The proceedings contain 132 papers. The special focus in this conference is on Web Information systems engineering. The topics include: TAKE: Tracing Associative Empathy Keywords for Generating Empathetic Responses Based on Graph Attention;intent Identification Using Few-Shot and Active Learning with User Feedback;CLIMB: Imbalanced data Modelling Using Contrastive Learning with Limited Labels;equivariant Diffusion-Based Sequential Hypergraph Neural Networks with Co-attention Fusion for Information Diffusion Prediction;CL3: A Collaborative Learning Framework for the Medical data Ensuring data Privacy in the Hyperconnected Environment;selectivity Estimation for Spatial Filters Using Optimizer Feedback: A Machine Learning Perspective;on Adversarial Training with Incorrect Labels;model Lake : A New Alternative for Machine Learning Models Management and Governance;a Benchmark Test Suite for Multiple Traveling Salesmen Problem with Pivot Cities;Deconfounded Causality-Aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs;Regularized Multi-LLMs Collaboration for Enhanced Score-Based Causal Discovery;Combining Uncensored and Censored LLMs for Ransomware Generation;Therapying Outside the Box: Innovating the Implementation and Evaulation of CBT in Therapeutic Artificial Agents;iText2KG: Incremental knowledge Graphs Construction Using Large Language Models;"Is this Site Legit?": LLMs for Scam Website Detection;Towards Enhancing Linked data Retrieval in Conversational UIs Using Large Language Models;BioLinkerAI: Capturing knowledge Using LLMs to Enhance Biomedical Entity Linking;Enhancing LLMs Contextual knowledge with Ontologies for Personalised Food Recommendation;ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources;Web-Based AI Assistant for Medical Imaging: A Case Study on Predicting Spontaneous Preterm Birth via Ultrasound Images;satellite-Driven Deep Learning Algorithm for Bathymetry Extraction;Would You Trust an AI Doc

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AI-Driven Remote Parkinson's Diagnosis with BPNN Framework and Cloud-Based data Security

AI-Driven Remote Parkinson's Diagnosis with BPNN Framework a...

引用

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

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of medical diagnostics, the integration of Artificial Intelligence (AI) has shown promising advancements, especially in the diagnosis and management of chronic diseases such as Parkinson's Disease (PD). This paper presents a novel AI-Driven approach for Remote Parkinson's Diagnosis utilizing a Back Propagation Neural Network (BPNN) framework, coupled with a secure cloud-based data handling mechanism. The primary focus is to harness the computational prowess of BPNNs to analyze and interpret the subtle and complex patterns associated with Parkinson's symptoms, thus facilitating an accurate and early diagnosis. To address the critical concern of data privacy and security, which is paramount in the healthcare domain, we implement a robust cloud-based architecture ensuring the confidentiality, integrity, and availability of patient data. This architecture employs state-of-the-art encryption techniques alongside access control mechanisms to safeguard sensitive information against unauthorized access and potential cyber threats. The proposed system is evaluated through extensive experiments involving real-world datasets, demonstrating its effectiveness in diagnosing Parkinson's with high accuracy and efficiency. Furthermore, the implementation of this system on a cloud platform ensures scalability, making it accessible to a wider range of healthcare providers and patients. Through this study, we aim to contribute to the improvement of remote healthcare services for Parkinson's patients, offering a secure, accurate, and user-friendly diagnostic tool. © 2024 IEEE.

关键词： data integrity

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Development of a Numerical Model of a Turbogenerator to Create a database for Monitoring

Development of a Numerical Model of a Turbogenerator to Crea...

引用

2024 international conference on Industrial engineering, Applications and manufacturing, ICIEAM 2024

作者： Kostenko, Aleksandr Kurbatova, Ekaterina Kulayev, Yurii National Research University 'Moscow Power Engineering Institute' Moscow Russia

ISBN: (纸本)9798350395013

The paper discusses the results of scientific work on the creation of a digital model of the turbogenerator for online monitoring. The feature of the developed model is the use of numerical modeling of electromagnetic fields to obtain a database of parameters in various operating modes. The numerical model of a turbogenerator in a two-dimensional formulation is presented, intended to determine parameters in steady-state operating conditions. Method for determining model parameters for given operating modes is described. The developed model was verified based on comparison with data from the automatic process control system (APCS) of a real turbogenerator. © 2024 IEEE.

关键词： Numerical models

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25th international conference on Web Information systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

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25th international conference on Web Information systems engineering, WISE 2024

ISBN: (纸本)9789819605750

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25th international conference on Web Information systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

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25th international conference on Web Information systems engineering, WISE 2024

ISBN: (纸本)9789819605668

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Deep Dive into IIoT Intrusion Detection systems: A Review and Future Directions 3

Deep Dive into IIoT Intrusion Detection Systems: A Review an...

引用

3rd IEEE international conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Pawar, Sushama Laxman Karyakarte, Mandar S. BRACT's Vishwakarma Institute of Information Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

The Industrial Internet of Things (IIoT) has quickly become a cutting-edge technology that can transform various sectors by digitalizing and connecting them, boosting business opportunities and global economic growth. Sectors like manufacturing, logistics, transportation, energy, and aviation are increasingly adopting IIoT. However, IIoT is vulnerable to cyber threats, requiring robust security measures. The numerous sensors in IIoT systems generate vast amounts of data, attracting cybercriminals. An Intrusion Detection System (IDS), which observe network traffic to detect unusual behavior, is crucial for securing IIoT applications. Recently, machine learning and deep learning techniques have shown potential in mitigating security threats and enhancing IDS performance. This paper reviews different deep learning-based IDS methods for IIoT, including techniques, datasets, and comparative analysis, and highlights existing limitations and future research directions. © 2024 IEEE.

关键词： Network intrusion

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25th international conference on Web Information systems engineering, WISE 2024

25th International Conference on Web Information Systems Eng...

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25th international conference on Web Information systems engineering, WISE 2024

ISBN: (纸本)9789819605729

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