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共 37034 条记录，以下是571-580 订阅

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Towards Model-Based Assembly System Configuration Supported by SysML and AutomationML 32nd

Towards Model-Based Assembly System Configuration Supported ...

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32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Rath, Jan-Erik Koch, Julian Schueppstuhl, Thorsten Hamburg Univ Technol Inst Aircraft Prod Technol Denickestr 17 D-20173 Hamburg Germany

ISBN: (纸本)9783031382406;9783031382413

The demands of product individualization and reduced development times are an increasing challenge for current product and production system engineering, as customization processes are still a mostly manual task involving different software tools and paperwork. Due to their manual nature, they are error-prone, leading to long lead times and costly rework. Thus, efficient co-customization of products and their production systems must be enabled by common data models and continuously linked software tools. Complex mechatronic products such as aircraft cabins are more and more likely to be developed via a model-based systems engineering (MBSE) approach using systems Modeling Language (SysML). In assembly system design, model-based development often relies on AutomationML, a modeling and data exchange format for plant engineering information. To combine these concepts, an approach for assembly system configuration based on a SysML-representation of the product and an AutomationML-represantation of the plant is proposed. After the customer configures a unique product version, the corresponding SysML model will be analyzed by the outlined configuration algorithm to determine necessary changes to the assembly system. Based upon an AutomationML library with digital representations of the available assembly system components and their behavior, these logical and parametric changes will be implemented in the instantiated assembly system model. Using AutomationML, configuration can not only take place on the top-level logical model, but also on specific robot or PLC programs. These can be then used for simulations generating information about assemblability and assembly times, allowing improved design decisions once fed back to the product engineers.

关键词： Model-Based engineering SysML AutomationML Assembly System Planning Assembly System Reconfiguration

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Proceedings of the 26th international conference on Enterprise Information systems, ICEIS 2024

Proceedings of the 26th International Conference on Enterpri...

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26th international conference on Enterprise Information systems, ICEIS 2024

ISBN: (纸本)9789897586927

The proceedings contain 185 papers. The topics discussed include: the evolution of original ERP customization: a systematic literature review of technical possibilities;data-driven process analysis of logistics systems: implementation process of a knowledge-based approach;a performance analysis for efficient schema design in cloud-based distributed data warehouses;quantitative analysis of the relationship between master data quality and process quality;analyzing sepsis treatment variations in subpopulations with process mining;balancing autonomy and control: an adaptive approach for security governance in large-scale agile development;project management in large-scale with international settings: challenges faced with multiculturalism;on the integration of privacy-enhancing technologies in the process of software engineering;and software engineers engagement and job satisfaction: a survey with practitioners working remotely in a public organization.

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HACS: An Enhancement Framework for Deep Code Search Benefiting from Hard Negative Samples 36

HACS: An Enhancement Framework for Deep Code Search Benefiti...

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

作者： Song, Qihong Liu, Jianxun Hu, Haize School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)1891706594

Code search aims to retrieve relevant code snippets from large code repositories based on query, promoting code reuse and enhancing software development efficiency. Deep Learning is a powerful approach for code search, in which the hard negative samples within training batches critically impact model performance. However, most existing deep code search models only randomly sample negative samples, resulting in a paucity or complete lack of hard negative samples. To address this limitation, we introduce a novel enhancement framework named HACS to optimize the composition of negative samples within batches, thus enhancing the training effectiveness of deep code search models. The core idea is to increase the count of hard negative samples within the negative samples corresponding to each query in the training batch. Specifically, HACS utilizes deep reinforcement learning techniques for sampling hard negative samples and implements vector-level mixed data augmentation strategy to generate hard negative samples. We evaluated our framework on a public dataset covering six programming languages. Experimental results reveal that HACS significantly improves the code search performance of existing models. © 2024 knowledge systems Institute Graduate School. All rights reserved.

关键词： Deep reinforcement learning

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Machine Learning in Workflow Optimization 2

Machine Learning in Workflow Optimization

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2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Reddy, V Srinivasa Pavithra, P. Mishra, Rashmi Agrawal, Shital Sharma, M.K. Lakshmikanth, R. Kg Reddy College of Engineering and Technology Department of Civil Engineering Hyderabad India Department of Information Technology Visakhapatnam India G L Bajaj Institute Of Technology & Management Department of Applied Science & Humanities Greater Noida India Shivajirao S. Jondhle College of Engineering & Technology Asangaon India Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut250004 India Adithya Institute of Technology Department of Management Studies Coimbatore India

ISBN: (纸本)9798350364699

This paper delves into the integration of machine literacy algorithms to enhance effectiveness, productivity, and decision-making in workflow operations. By using prophetic analytics and real-time data processing, machine literacy models can identify patterns and prognosticate implicit backups, enabling visionary adaptations and resource allocation. This exploration focuses on the operation of supervised and unsupervised literacy ways to streamline complex workflows, reduce functional costs, and ameliorate overall performance. crucial case studies from manufacturing, healthcare, and logistics demonstrate the practical benefits of these technologies. The findings punctuate the transformative eventuality of machine literacy in creating adaptive and intelligent workflow systems that can stoutly respond to changing conditions and demands. This study contributes to the growing body of knowledge on the crossroads of machine literacy and workflow optimization, offering perceptivity and strategies for perpetration in different functional surroundings. © 2024 IEEE.

关键词： Adversarial machine learning

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A data Mesh Adaptable Oil and Gas Ontology Based on Open Subsurface data Universe (OSDU) 15

A Data Mesh Adaptable Oil and Gas Ontology Based on Open Sub...

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15th international conference on knowledge engineering and Ontology Development, KEOD 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Abolhassani, Neda Tudor, Ana Paul, Sanjoy Accenture Labs 415 Mission St Floor 35 San FranciscoCA94105 United States

ISBN: (纸本)9789897586712

Incompatible models, heterogeneous data, and siloed data present challenges for the Oil & Gas industry. knowledge graphs provide efficient consolidation, improved quality, and universal access to data, addressing these challenges. Developed by major global Oil & Gas and cloud organizations, the Open Subsurface data Universe (OSDU) platform provides subsurface energy data ingestion, enrichment, and consumption services, as well as metadata storage, indexing, and search services. OSDU data supply chain aligns with the main concepts of the new trending data architecture, data Mesh, such as federated data governance, decoupling data from applications, and domain specific data products. data integration in subsurface data industry can be achieved by building a domain knowledge graph based on standard and enriched OSDU framework schemas. A knowledge graph-based solution begins with building a domain ontology. The purpose of this article is to introduce the OSDU ontology, which is publicly available on GitHub under the Apache 2.0 license. This paper discusses OSDU ontology design, development, applications, and evaluation. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0).

关键词： Graphic methods

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Proceedings of the 26th international conference on Enterprise Information systems, ICEIS 2024

Proceedings of the 26th International Conference on Enterpri...

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26th international conference on Enterprise Information systems, ICEIS 2024

ISBN: (纸本)9789897586927

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Low-cost irrigation system based on multi-tenant and WSN technologies(SCA) 36

Low-cost irrigation system based on multi-tenant and WSN tec...

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

作者： Ma, Jinwei Li, Meian Chao, Lumeng Computer and Information Engineering College Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Agriculture University Hohhot China

ISBN: (纸本)1891706594

The increase in irrigated crop areas lead to the requirements for freshwater will in fact rise significantly. Considering this kind of need, this study aims to develop a low cost irrigation system that provide the monitoring and control of irrigated agriculture using Cloud Computing, Internet of Things(IoT) to improve the efficiency of water use in irrigated agriculture and reduce the cost of the solution. In this paper, an irrigation system driven by Internet of Things and cloud based web application is developed. The web application uses a multi-tenant web framework (MWF) that can provide cost-effective services for scenarios where a large number of small tenants, only some of which are active at the same time. The system uses wireless sensor network(WSN) communication technology to integrate various sensors, solenoid valves and edge devices scattered throughout the crop area for monitoring and control. The solution significantly improves water use efficiency while reducing costs, making it attractive to users. © 2024 knowledge systems Institute Graduate School. All rights reserved.

关键词： Solenoid valves

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18th international Joint conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

18th International Joint Conference on Biomedical Engineerin...

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18th international Joint conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

The proceedings contain 256 papers. The special focus in this conference is on Biomedical engineering systems and Technologies. The topics include: Cross-Domain Transfer Learning for Domain Adaptation in Autism Spectrum Disorder Diagnosis;an Interpretable Machine Learning Model for Meningioma Grade Prediction;approximation of Inertial Measurement Unit data to Time Series Kinematic data Through Correlation Analysis and Machine Learning;Privacy-Preserving Mortality Prediction in ICUs Using Federated Learning;trends in Drug Prescriptions in the Outpatient Physician Sector in a German Federal State from 2014 to 2023 Using Morbidity Related Groups, Correlations and Partial Correlations;quality Clustering for Reducing the Search Space for Mobile Stroke Unit Allocation;leveraging Cross-Verification to Enhance Zero-Shot Prompting for Care Document data Extraction;Integrating Gait and Clinical data with Explainable Artificial Intelligence for Parkinson’s Prediction: The EDAM System;Unveiling Breast Cancer Causes Through knowledge Graph Analysis and BioBERT-Based Factuality Prediction;optimizing Blood Transfusions and Predicting Shortages in Resource-Constrained Areas;using Machine Learning to Assess the Impact of Harsh Violent Discipline on Children and Adolescents in Lowand Middle-Income Countries: A Comparative Analysis Focusing on Its Relationship with Disabilities;HealthAIDE: Developing an Audit Framework for AI-Generated Online Health Information;enhancing Diagnostic Accuracy of Drug-Resistant Tuberculosis on Chest X-Rays Using data-Efficient Image Transformers;CFC Annotator: A Cluster-Focused Combination Algorithm for Annotating Electronic Health Records by Referencing Interface Terminology;novel Approach to De-Identify Relational Healthcare databases at Rest: A De-Identification of Key data Approach;enhancing Fracture Aftercare Through a Human-Centered Mobile App Design;challenges of Generalizing Machine Learning Models in Healthcare.

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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...

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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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Classification of Table Cells Based on LLM Prompts

Classification of Table Cells Based on LLM Prompts

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

作者： Liu, Mengjie Bu, Chenyang Bai, Shengxing Dong, Bingbing Wu, Xindong China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665410205

Tables, as an important means of data storage, are widely used in spreadsheets, web tables, and PDFs. By integrating information from table data with knowledge re-trieved from an external knowledge base, and examining the correspondences between cell values in the table and instances in the knowledge base, we can extract knowledge from the table to augment and enrich the knowledge base. To achieve this goal, we first need to classify table cells based on their functions in the layout. Due to the diverse structures arising from the arrangements of rows and columns, as well as the complexity of content resulting from concise data storage, current automation techniques heavily rely on stylistic features of table cells, such as font or color. Moreover, these methods are rarely experimented with or validated on tables without style features. Recent literature indicates that large language models (LLMs) demonstrate an ability to understand the structure and content of tables in tasks such as table judgment reasoning. Even without extensive feature inputs or pre-training, LLMs still show comparable results to machine learning and deep learning in these tasks. Therefore, this paper attempts to apply LLMs to table cell classification without using other stylistic features. We have designed a 4-component prompt paradigm (Classification Definition, Instruction, Table, Com-pletion), representing respectively the classification definition, task instructions, table data, and result output. We conduct experiments on three datasets CIUS, SAUS, and DEEX for table cell classification with one-shot learning. Our experimental results show that with the assistance of LLMs, better results can be achieved without utilizing stylistic features. © 2024 IEEE.

关键词： knowledge based systems

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