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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581-590 订阅

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A Hybrid Approach to Extraction of knowledge From Scientific Texts Based on Large Language Models and Domain Dictionaries

A Hybrid Approach to Extraction of Knowledge From Scientific...

引用

2024 IEEE international Multi-conference on engineering, Computer and Information Sciences, SIBIRCON 2024

作者： Chusova, Alina Artemieva, Irina Chusov, Andrey Far Eastern Federal University Department Software Engineering and Artificial Intelligence Vladivostok Russia

ISBN: (纸本)9798331532024

The vast information landscape of the Internet constitute a significant challenge for extracting valuable content. The lack of standardized data models and structures necessitates ad hoc solutions, often requiring expert knowledge that developers may lack. While Large Language Models (LLMs) hold promise for addressing this challenge, their susceptibility to AI hallucinations and inaccuracies necessitates ongoing research. This paper introduces a hybrid approach for extracting information on computational methods that combines domain-specific dictionaries with LLMs in order to improve the accuracy of method categorization. Our system incorporates explainability, allowing users to understand the reasoning behind method assignments. Furthermore, user-driven training is facilitated by allowing users to select theories and highlight relevant keywords, enhancing learning capabilities of the system. Its implementation demonstrates the effectiveness of this approach, achieving an impressive Fl-score of up to 90.3 %. This research contributes to the ongoing effort to develop robust and accurate knowledge extraction systems for navigating the ever-expanding landscape of online information. © 2024 IEEE.

关键词： Online systems

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Robust Network For Segmenting Roof Planes From Sparse Point Clouds 36

Robust Network For Segmenting Roof Planes From Sparse Point ...

引用

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

作者： Fu, Hang Zeng, Cheng Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China School of Computer and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China

ISBN: (纸本)1891706594

The plane is the fundamental characteristic for describing the shape of polyhedral buildings. Roof plane segmentation of airborne LiDAR point clouds is an important step in 3D building model reconstruction. Existing methods are either non-automatic and efficient traditional methods, or hindered by the lack of datasets for roof plane segmentation and therefore fail to achieve good training for deep learning methods. To address the above issues, we proposes an end-to-end network called PUTr for roof plane segmentation of sparse point clouds. This network integrates a Point-cloud Upsampling module and Transformer to ensure effective generalization even when trained on low-density datasets, while also capturing dependency relationships within point cloud neighborhoods. Our model overcomes the low automation and manual intervention of existing methods, and the limited generalization of deep learning due to scarce high-quality datasets. Evaluations also demonstrate its effectiveness in roof plane segmentation of laser point cloud data. © 2024 knowledge systems Institute Graduate School. All rights reserved.

关键词： 3D reconstruction

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KDTSS: A Blockchain-Based Scheme for knowledge data Traceability and Secure Sharing 17th

KDTSS: A Blockchain-Based Scheme for Knowledge Data Traceabi...

引用

17th international conference on knowledge Science, engineering and Management (KSEM)

作者： Liang, Haochen Guo, Yunwei Yu, Jing Dong, Hao Gai, Keke Beijing Inst Technol Sch Cyperspace Sci & Technol Beijing Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing Zhejiang Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Beijing Muguo Tech Ltd Beijing 100081 Peoples R China

ISBN: (纸本)9789819755004;9789819755011

In the field of computer science, knowledge refers to organized and meaningful information derived from the processing and interpretation of data. It plays a pivotal role in guiding decision-making, understanding complex systems, solving problems, or predicting future events. Consequently, knowledge data often faces the risks of alteration or theft by attackers. Given that many knowledge datasets may contain sensitive information, such as personal privacy, trade secrets, or national security-related data, ensuring the security of knowledge data poses a significant challenge. knowledge provenance is a technology that has garnered considerable attention for its ability to ensure the integrity of knowledge data during transmission, thereby enhancing its credibility. This paper presents the design of a blockchain-based knowledge data provenance and sharing system, which leverages blockchain technology to achieve trustworthy provenance and secure sharing of knowledge data. Comprehensive comparative experiments conducted on a consortium blockchain confirm that our proposed solution exhibits robust performance and security.

关键词： knowledge Traceability Blockchain data Sharing data Storage Access Control

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Privacy-Preserving Brakerski-Gentry-Vaikuntanathan (BGV) Homomorphic Encryption for IoMT data Security

Privacy-Preserving Brakerski-Gentry-Vaikuntanathan (BGV) Hom...

引用

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

作者： Kumar, V. Vinoth Pabitha, P. Velammal Institute of Technology Department of Information Technology Chennai Ponneri601204 India Anna University Department of Computer Technology MIT Campus Chennai Chromepet600044 India

ISBN: (纸本)9798350359688

The rapid proliferation of Internet of Medical Things (IoMT) devices has revolutionized healthcare, offering unprecedented opportunities for remote patient monitoring, personalized treatment, and data-driven insights. However, the sensitive nature of health data collected by IoMT devices necessitates robust security and privacy measures to protect patient confidentiality. In this paper, we propose a novel approach to address these challenges by leveraging blockchain technology in conjunction with the privacy-preserving capabilities of the Brakerski-Gentry-Vaikuntanathan (BGV) homomorphic encryption scheme. Our proposed Blockchain-based Privacy-Preserving BGV Homomorphic Encryption mechanism aims to ensure the confidentiality and integrity of IoMT data while enabling secure computation for data analysis and utilization. Our mechanism employs the BGV homomorphic encryption scheme to enable efficient and secure computation on encrypted data, preserving patient privacy throughout the analysis process. we demonstrate the practical applicability and effectiveness of our approach in safeguarding sensitive health data while facilitating secure and privacy-preserving data analytics. Finally, we discuss potential extensions and future research directions, highlighting the significance of privacy-preserving mechanisms in advancing IoMT data security and enabling the realization of a trusted and interoperable healthcare ecosystem. © 2024 IEEE.

关键词： Patient treatment

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Statistical-Based Pick-and-Place Control 32nd

Statistical-Based Pick-and-Place Control

引用

32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Kim, Jaewoo Won, Daehan Yoon, Sang Won SUNY Binghamton Binghamton NY 13902 USA

ISBN: (纸本)9783031382406;9783031382413

Surface Mount Technology (SMT) is the most widely used method of manufacturing electronic assemblies to place electronic components on the surface of the printed circuit board (PCB). The miniaturization trend in electronics requires tighter placement control to satisfy this SMT connection criterion. With the ongoing miniaturization trend and increased usage of lead-free solder, the solder's misalignment has caused component misalignment, especially on the mini-scale components. This research proposes a statistical-based framework that collects data from inspection machines and identifies the optimal placement position to minimize the small passive component's side overhang (SO), which is one of the dimensional requirements in SMT assemblies. An experiment that involves four printer settings and 4,800 resistors R0402M (0.40 mm x 0.20 mm) is designed and conducted. In the experiment, our framework shows the effectiveness of reducing SO.

关键词： Surface Mount Technology smart manufacturing side overhang mini-scale passive components pick-and-place process

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Shaping Inter-Team knowledge Transfer Networks in EPC Projects: A Structural-Affective-Cognitive Framework

Shaping Inter-Team Knowledge Transfer Networks in EPC Projec...

引用

2024 international conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Zheng, Yuanrong Xia, Qilong Liu, Kangning Liu, Fang Xue, Huaping Chongqing Construction Engineering Group Third Construction Co. Ltd. Chongqing China School of Economics and Management Chongqing Jiaotong Univ. Chongqing China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Inter-team knowledge transfer is a key issue for collaboration in EPC construction projects. However, the understanding of knowledge transfer in the context of multi-team systems is limited. Based on multi-team systems theory, social identity theory, and mental model theory, this study proposes a structural-affective-cognitive framework to explore how inter-team knowledge transfer shapes EPC construction projects. Relationship data from 193 project members of 12 core teams participating in a large-scale EPC construction project were collected through sociometric and questionnaire surveys. The results show that knowledge transfer network of multi-team EPC construction projects exhibits reciprocity and transitivity path closure, and there are differences in the reciprocity structure patterns between intra-team knowledge transfer networks and inter-team knowledge transfer networks. Task dependence relationships positively influence the formation of inter-team knowledge transfer, while the impact of contractual relationships is not significant. Inter-team task dependence relationships enhance the positive impact of members' dual identification and mental models on the formation of inter-team knowledge transfer. The findings enrich the understanding of inter-team knowledge transfer network in multiteam EPC construction projects. © ASCE.

关键词： System theory

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Ontology Modelling of Smart Product-Service System Components towards Mass Personalization

Ontology Modelling of Smart Product-Service System Component...

引用

2024 IEEE international conference on Industrial engineering and engineering Management, IEEM 2024

作者： Chen, Lucheng Sheng, Guojun Qin, Chenggang Wang, Chao Wu, Alexander T.Y. Nuertai, Ayala Zhou, Tongtong Ming, Xinguo SJTUSME-COSMOPlat Joint Research Center for New Generation Industrial Intelligent Technology School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Co. Ltd Shandong Qingdao266100 China State Key Laboratory of Massive Personalized Customization System and Technology Shandong266100 China School of Design Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9798350386097

Customer products have become increasingly driven by mass personalization as industries are shifting to knowledge-driven user-centric design approaches such as Smart Product-Service systems (Smart PSS) to better meet expectations and remain competitive. However current user-centric design methods lack a semantic framework that properly models the components of a Smart PSS, leading to a lack in the usefulness of the knowledge generated from user research. This paper aims to create an ontological model that defines the components of a Smart PSS and their relationships to help enterprises better support dynamic personalization requirements. A case-study of electric vehicles is then presented to explore the usage of the ontology-based knowledge model on a Smart PSS. © 2024 IEEE.

关键词： Smart manufacturing

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Recurrent Penalized Splines for Real-Time Forecasting of the Parameters of Technical systems

Recurrent Penalized Splines for Real-Time Forecasting of the...

引用

2023 international conference on Industrial engineering, Applications and manufacturing, ICIEAM 2023

作者： Kochegurova, Elena Kaida, Anastasiia Galkina, Maria Tomsk Polytechnic University Information Systems and Technologies Dep. Tomsk Russia Tomsk Polytechnic University School of Nuclear Science and Engineering Tomsk Russia

ISBN: (纸本)9781665475952

Forecasting time series (TS) from their historical data is used in many preventive monitoring and decision support systems in industry. For systems operating in real-time, an urgent problem is to reduce forecasting time without compromising other quality indexes (accuracy and trend continuation). The purpose of this paper is to describe a forecasting model based on a recurrent penalized P-spline. The spline model has a compact computational scheme where the efficiency is achieved by using a short input of historical data a simple mathematical description. The model was compared to 11 most well-known forecasting algorithms. The performance of the proposed model compared quite well to the competitive forecasting algorithms. The model was used to forecast a real-world TS of a technological parameter of a boiler plant. © 2023 IEEE.

关键词： Decision support systems

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data-Driven Decision-Making in Education: Leveraging AI for School Improvement

Data-Driven Decision-Making in Education: Leveraging AI for ...

引用

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

作者： Alsbou, Majida Khalaf Khaleel Alsaraireh, Ra'Ed Abdalhafed Ibrahim Mutah University Faculty of Educational Science Department of Curriculum and Instruction Jordan Mu'tah University Faculty of Educational Science Department of Curriculum and Instruction Mu'tah Karak61710 Jordan

ISBN: (纸本)9798350359688

In the ever-evolving landscape of education, the integration of Artificial Intelligence (AI) stands as a transformative force, poised to revolutionize traditional approaches to decision-making. This paper addresses the imperative of harnessing AI to inform data-driven strategies for school improvement. Our comprehensive framework navigates the multifaceted realm of educational data, offering stakeholders a sophisticated toolset for insightful decision-making. By leveraging AI algorithms, we empower educators and administrators to decipher intricate patterns within student performance data, assess teaching methodologies, and optimize resource allocation. This model not only responds to the growing importance of data in education but also aligns with the broader movement towards smart, adaptive educational systems. Through an extensive literature survey, we contextualize our work within the existing body of knowledge, emphasizing the transformative potential of AI in educational settings. Our proposed model intricately intertwines data collection, preprocessing, analysis, and visualization, employing machine learning techniques to predict student outcomes and refine teaching strategies. Ethical considerations underscore our commitment to responsible AI usage, ensuring data privacy and safeguarding against biases. The implementation model details the practical deployment of our framework, emphasizing scalability and real-world applicability. The results section showcases tangible improvements in student outcomes and institutional efficiency through the application of our model to diverse educational datasets. In conclusion, our research underscores the capacity of AI-driven decision-making to catalyze substantial advancements in education, marking a pivotal moment in the ongoing journey towards data-driven educational excellence. © 2024 IEEE.

关键词： Resource allocation

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The Role of Artificial Intelligence in Advancing Fracture Mechanics: Modeling, Prediction, and data Analysis 4

The Role of Artificial Intelligence in Advancing Fracture Me...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Srivastava, Vivek Gupta, Nitin Kumar Raturi, Ojas Somani, Nalin Bhandwal, Mohit Chaudhary, Naina Sgt University Department of Mechanical Engineering Gurgaon India Dit University Department of Mechanical Engineering UK Dehradun248009 India Amity University Tashkent Uzbekistan

ISBN: (纸本)9798350387490

With the Internet of Things (loT) and the Industrial Internet of Things (1IoT) as key paradigms, industries and societies have been advanced by allowing systems to interconnect and making intelligent decisions. This paper examines the progress of the loT and IloT by specifically define the taxonomy, communication protocols and system integration for the two technologies. Whenever loT solutions are deployed, taxonomies provide the categorization of diverse constituents, functional relationships, and compositional hierarchies within these arrangements. MQTT, DDS, and OPC UA are discussed as important protocols and their functions, differences, and performance characteristics are compared and analyzed. The paper also aims to look at the integration framework of the IloT as this has the key function of ensure the compatibility between industrial operations and smart technologies. The concepts garnered through loT and IloT are explained by applying them in fields such as Smart Cities, Healthcare, manufacturing and logistics etc. Nevertheless, some issues still present major questions such as scalability, interoperability and security. That is why this work underscores that there is a need to develop and improve protocols, which are necessary to overcome these challenges. As a synthesis of the current knowledge regarding loT and IloT, this work offers a valuable guide to researchers and practitioners trying to meet the great challenges of building reliable and efficient solutions to next generation interconnected systems. © 2024 IEEE.

关键词： Taxonomies

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