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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621-630 订阅

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Hybrid Algorithm for Real-Time data Forecasting with Density-Based Clustering and Penalty Splines

Hybrid Algorithm for Real-Time Data Forecasting with Density...

引用

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

作者： Kochegurova, Elena Denisov, Vladislav Galkina, Maria Tomsk Polytechnic University Information Systems And Technologies Dep. Tomsk Russia School Of Nuclear Science And Engineering Tomsk Polytechnic University Tomsk Russia

ISBN: (纸本)9798350395013

We propose a hybrid forecasting model that includes time series segmentation, which is based on the DBScan clustering algorithm, and a penalty spline. Each step of the hybrid model required modification for real-time use. The DBScan density-based algorithm was supplemented with technology for working with noise and transition between clusters. A recurrent penalty spline model has low computational complexity and, accordingly, high forecasting speed inside the hybrid model. The forecast efficiency was assessed by a number of indicators that reflect predictive efficiency and extrapolation accuracy of using synthetic, model and real TS. Accuracy indicators (MSPE, MAPE) are below 4%. In comparison with other widely-spread forecasting methods, the proposed hybrid algorithm ranks among the known algorithms. © 2024 IEEE.

关键词： Efficiency

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ChatGPT for Intelligent Spatial Analysis Workflow Construction 24

ChatGPT for Intelligent Spatial Analysis Workflow Constructi...

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32nd ACM SIGSPATIAL international conference on Advances in Geographic Information systems, ACM SIGSPATIAL 2024

作者： Nie, Ying Gao, Song Geospatial Data Science Lab University of Wisconsin MadisonWI United States

ISBN: (纸本)9798400711077

The capability of constructing executable scientific workflows that integrate data, models, and domain-specific knowledge using AI tools remains quite limited. This study utilizes large language model (LLM)-powered chatbots such as ChatGPT to streamline the process of automating spatial analysis workflow construction. By exploring prompt engineering techniques in ChatGPT, we aim to generate scientific workflows that address spatial analysis problems using ArcGIS geoprocessing tools. We identify key challenges and propose strategies involving geo-knowledge and structured prompts to enhance GIS workflow generation. Our preliminary results show that the accuracy and effectiveness of ChatGPT-generated GIS workflow are improved by adopting these strategies. © 2024 Copyright held by the owner/author(s).

关键词： GeoAI

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Research on track fault diagnosis method of mechanical parking equipment based on deep learning 3

Research on track fault diagnosis method of mechanical parki...

引用

3rd international conference on Advanced manufacturing Technology and manufacturing systems, ICAMTMS 2024

作者： Wang, Teng Xu, Bin Ge, Xudong Gao, Zhengxin Department of Mechanical Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9781510681804

With the development of China's urbanization and economy, the problem of parking difficulties in big cities has gradually become prominent, and the parking equipment used to alleviate such problems has emerged, but the accompanying safety problems of parking equipment such as trapped car accidents have gradually attracted attention. The research on safety guarantee of parking equipment needs to be carried out urgently, and the work of safety assessment and fault prevention of parking equipment is constantly carried out. This paper presents a new fault diagnosis model for the track of mechanical parking equipment. The vibration signal data of the running track of the mechanical parking equipment under different operating conditions were collected, and the time-frequency diagram of the vibration signal was obtained by using wavelet transform. The time-frequency diagram of the fault samples was expanded by SinGAN, and the BiLSTM-CNN neural network fault diagnosis model was built for training. The example shows that the track fault diagnosis model based on BiLSTM-CNN is suitable for practical application and can accurately diagnose the track fault of mechanical parking equipment. © 2024 SPIE.

关键词： Wavelet transforms

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knowledge and data Management: The Cornerstone of Effective Organizational Strategy 8

Knowledge and Data Management: The Cornerstone of Effective ...

引用

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

作者： Rajic, Milena Milosavljevic, Peda Kostic, Zorana University of Niš Niš Faculty of Mechanical Engineering Department of Management in Mechanical Engineering Serbia

ISBN: (纸本)9798350313246

In the modern business landscape, organizations operate in highly complex and dynamic environments, facing constant challenges in maximizing efficiency, sustainability, and profitability. To achieve success, businesses must harness and leverage knowledge and data effectively. knowledge and data Management are important in shaping an organization's strategy. By maintaining a knowledge repository and implementing collaborative platforms, businesses can avoid duplicating efforts, reduce errors, and develop strategies with a long-term vision. The seamless transfer of knowledge from experienced employees to new hires also ensures the preservation of critical expertise, further improving an organization's competitive advantage. The effective utilization of data is crucial to optimize energy consumption, reduce costs, make processes more sustainable, and consequently minimize environmental impact. Energy-intensive industries must carefully monitor and analyse data related to energy consumption, production processes, and equipment efficiency to identify opportunities for improvement, energy management, and industrial management, serving as the base for informed decision-making and sustainable growth. This paper explores the interconnectedness of these key areas and how they can be improved through effective knowledge and data management practices. © 2023 IEEE.

关键词： Energy management

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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

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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Research on the Impact of Intelligent manufacturing on Total Factor Productivity in China Based on Machine Learning Methods 14

Research on the Impact of Intelligent Manufacturing on Total...

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14th international conference on Logistics and systems engineering

作者： Guan, Heshan Bao, Qin Teng, Mingyan Wang, Qian School of Economics Management and Law University of South China Hunan Hengyang421001 China

ISBN: (纸本)9798331314347

Intelligent manufacturing is the main direction of the construction of manufacturing power, the improvement of China's manufacturing quality level is closely related to the degree of development of intelligent manufacturing. This paper selects data from 31 provinces in China, excluding Hong Kong, Macao and Taiwan, for the period 2014-2022, and empirically examines how the level of intelligent manufacturing development affects total factor productivity in each province through regression models. The results of the study show that, in general, the level of development of intelligent manufacturing has a significant positive effect on total factor productivity, i.e., an increase in the level of intelligent manufacturing can significantly contribute to the overall economic growth of the country;The results of the heterogeneity analysis show that, from the perspective of coastal and non-coastal areas, the level of intelligent manufacturing development has a certain degree of heterogeneity on economic development, and has a significant positive impact on the high-quality development of the coastal region's economy, while the impact on the economic development of the non-coastal region is weak, mainly because the coastal region has a strong technological and human resources advantage, while the non-coastal region lacks a sufficient economic base and innovative capacity that cannot support the development of intelligent manufacturing for the time being;As non-coastal areas strengthen the deep docking with the Beijing-Tianjin-Hebei Region, the Yangtze River Delta, the Guangdong-Hong Kong-Macao Greater Bay Area, undertake the gradient transfer of industries in an orderly manner, and optimise the industrial layout, non-coastal areas are expected to better carry the development of intelligent manufacturing and promote the high-quality development of the economy;By analysing the mechanism with R&D capability as a mediating variable, it is verified that the improvement of the leve

关键词： Smart manufacturing

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DIGITAL engineering PLATFORM FOR SYNERGISTIC DECISION-MAKING IN manufacturing PLANT OPERATIONS: RESEARCH QUESTIONS

DIGITAL ENGINEERING PLATFORM FOR SYNERGISTIC DECISION-MAKING...

引用

ASME international Design engineering Technical conferences / 42nd Annual Computers and Information in engineering conference (IDETC-CIE) / 48th Design Automation conference (DAC)

作者： Swaminathan, N. Gautham, B. P. Shukla, Rishabh Malhotra, Chetan Gaduparthi, Trinath Tata Consultancy Serv TCS Res Pune Maharashtra India

ISBN: (纸本)9780791886236

Decision makers in the manufacturing industry ranging from plant operators to senior management make decisions based on a combination of defined procedures and rules, expert inputs and analysis, and their own knowledge and understanding of the problem context. Decision spaces are getting more complex, with the business paradigms shifting towards autonomous plants and servitization of products. With the advent of Internet of Things (IoT) and technologies such as machine learning and digital twins, the resources and capabilities available to decision-makers are expanding vastly. The gamut of concerns to be addressed is also expanding, with new challenges such as sustainability and its concomitant regulations and the pressure to make businesses more socially aware. Further, it would be ideal if decision-makers could easily draw upon the relevant knowledge, intuition, and experience of human experts, as well as knowledge currently buried in documents and data, and synthesize all the diverse inputs towards informed decision-making by integrating cyber, physical, and social systems. This motivates the question, "How do we create platforms that synergize these diverse knowledge sources and capabilities to facilitate better decision-making?" In this paper, we try to delve into identification of few key research questions and discuss opportunities and requirements around the same, that can aid in creating a digital platform to synergize all these diverse inputs and support decision-making. While this paper uses decision-making in manufacturing plant operations to explore the challenges and discuss one possible approach, the problem of enabling seamless synergy between the knowledge and capabilities of diverse human, IT and physical elements applies to all Cyber Physical Social systems (CPSS).

关键词： Cyber-physical social systems decision-making viewpoints ontological models synergistic decision-making

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Artificial Intelligence Tools for Early Detection of Product Defects in the Pharmaceutical Industry 18

Artificial Intelligence Tools for Early Detection of Product...

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

作者： Tshehla-Nkuna, Makope Mulongo, Ndala Yves University of Johannesburg Faculty of Engineering and the Built Enviroment South Africa

ISBN: (纸本)9798350318401

Current in-process inspection system used at a local pharmaceutical company is manual, outdated and not equipped to perform 100% non-destructive testing to identify critical production defects prior to product release to market. This has resulted in high customer complaints. To address this issue pharmaceutical industry, need to streamline processes and reduce manual intervention. Operator error have been found to be the main contributors to product defect and quality problem. The pharmaceutical industry has also found itself not immune to challenges caused by humans, manual intervention and changes related to the manufacturing environment. Hence, the necessity to be agile and relevant in a dynamic environment. This emphasis has caused industry to advocate for the adoption of technological advancement tools for continuous improvement in quality and process efficiencies. This study aimed at establishing, the most modern and innovative advance technological tools, preferable Artificial Intelligence tools to identify and reduce production defects in real time, with less risk to product. Artificial Intelligence functionality tools. The research methodology executed for this study is the PRISMA approach. The successful implementation of AI is dependent on the accuracy of the input date. The challenges of AI adoption include initial investment cost, data security and AI regulatory framework. © 2023 IEEE.

关键词： Defects

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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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RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models 24

RecPrompt: A Self-tuning Prompting Framework for News Recomm...

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33rd ACM international conference on Information and knowledge Management (CIKM)

作者： Liu, Dairui Yang, Boming Du, Honghui Greene, Derek Hurley, Neil Lawlor, Aonghus Dong, Ruihai Li, Irene Univ Coll Dublin Dublin Ireland Univ Tokyo Tokyo Japan

ISBN: (纸本)9798400704369

News recommendations heavily rely on Natural Language Processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user interests and reading behaviors. Large Language Models (LLMs) like GPT-4 have shown promising performance in understanding natural language. However, the extent of their applicability to news recommendation systems remains to be validated. This paper introduces RecPrompt(1), the first self-tuning prompting framework for news recommendation, leveraging the capabilities of LLMs to perform complex news recommendation tasks. This framework incorporates a news recommender and a prompt optimizer that applies an iterative bootstrapping process to enhance recommendations through automatic prompt engineering. Extensive experimental results with 400 users show that RecPrompt can achieve an improvement of 3.36% in AUC, 10.49% in MRR, 9.64% in nDCG@5, and 6.20% in nDCG@10 compared to deep neural models. Additionally, we introduce Topic-Score, a novel metric to assess explainability by evaluating LLM's ability to summarize topics of interest for users. The results show LLM's effectiveness in accurately identifying topics of interest and delivering comprehensive topic-based explanations.

关键词： News Recommendation Automatic Prompt engineering Large Language Models

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