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

共 37467 条记录，以下是821-830 订阅

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Research on Process Recommendation Algorithm Based on User Intent Perception

Research on Process Recommendation Algorithm Based on User I...

引用

2023 international conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Wang, Yajun Jin, Bo School of Mechanical Engineering Dalian University of Technology Dalian China School of Innovation and Entrepreneurship Dalian University of Technology Dalian China

ISBN: (纸本)9781510671881

With the rapid development of the manufacturing industry, the design of manufacturing processes has become increasingly complex, and there is a growing demand from companies and users for efficient process recommendation systems. However, existing research on process recommendation often overlooks the importance of user intent. In reality, user intent often consists of multiple facets, each with different emphases, collectively driving users to choose different processes. For example, an intent to meet tight deadlines may favor less time-consuming processes, while an intent for safety requirements may prefer more mature processes. Disregarding the notion of multiple intents from users would confine the modeling of interactions between users and processes. Therefore, to enhance the efficiency and quality of process design, this paper proposes an intent-aware manufacturing process recommendation algorithm. This algorithm combines process knowledge activity analysis with intent awareness, and establishes a mapping between process knowledge and process knowledge graph through graph representation learning and attention mechanisms, enabling perception of user intent and corresponding process knowledge recommendation. Through empirical validation and application, this paper demonstrates the effectiveness and practicality of the algorithm. Experimental results show that the intent-aware process knowledge recommendation algorithm significantly improves the efficiency and accuracy of process design, providing strong support for the development of the manufacturing industry. © 2023 SPIE. All rights reserved.

关键词： Efficiency

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Tuning Process Parameters to Control the Porosity of Parts Produced with Directed Energy Deposition 32nd

Tuning Process Parameters to Control the Porosity of Parts P...

引用

32nd international conference on Flexible Automation and Intelligent manufacturing

作者： Piscopo, Gabriele Atzeni, Eleonora Iuliano, Luca Salmi, Alessandro Politecn Torino Dept Management & Prod Engn Turin Italy

ISBN: (纸本)9783031382406;9783031382413

Laser powder directed energy deposition (LP-DED) is an exponentially growing additive manufacturing process. The process is mainly used for repairing/remanufacturing operations, for the production of large components, or for the production of components with tailored characteristics. Despite the many benefits in the adoption of the LP-DED process, the applications are nowadays still limited. The reasons lie partly in a limited knowledge of the relationship between process parameters and the properties achieved in the final product. Specifically, one of the critical factors influencing the mechanical properties is the porosity of the part. The purpose of the present work is to investigate the effect of the main process parameters, such as laser power, travel speed and powder feed rate, on the porosity of AISI 316L samples produced by the LP-DED process. Results showed that by tuning process parameters, it is possible to produce samples with a density up to 99.9%. In the range of process parameters investigated in this work, porosities are mainly reduced by increasing the laser power;moreover, at lower power values, the increase in powder feed rate significantly improves the part density.

关键词： Additive manufacturing Directed Energy Deposition Laser Powder Directed Energy Deposition Porosity Analysis 316L Stainless Steel

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Integration of Computer Vision systems in Robotics and Industry 4.0 2

Integration of Computer Vision Systems in Robotics and Indus...

引用

2nd IEEE international conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Vikas Prasad, Rajesh Upadhaya, Santosh Kumar Kumar, Amit Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350368109

Computer vision is the field that focuses on automating and combining various processes and representations used for visual perception. The subject encompasses numerous approaches that provide individual use, such as image processing. Image processing is converting, transforming and transferring an image. This method is the statistical categorization of patterns based on applying statistical decision theory. Computer vision systems are deployed in various environments, from the shop floor and hospital suite to Mars and surgical suits. Incorporating computer vision in manufacturing is a groundbreaking step towards elevating Industry 4.0 by enhancing efficiency, accuracy and connectivity. This is driven by the compelling benefits realized when computer vision can be used in a factory to create a safe environment for all stakeholders and operators, affording machines with visual perception and enabling them to process and act on the high-richness of environmental data given input by a camera. Computer vision integrated into manufacturing is a drastic step towards more intelligent, efficient and connected production methods as part of Industry 4.0. This paper discusses different features of the Industrial Revolution, the role of computer vision in Industrial Revolution 4.0, and how computer vision affected the Industrial Revolution in future industry development. As computer vision debuts firsthand in commercial use in manufacturing, so is the industry about to explode with unprecedented creativity and productivity. © 2024 IEEE.

关键词： Computer integrated manufacturing

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A data-Driven Two-Stage Dynamometer Diagram Inference Approach for Variable Operational Conditions in Pumping Machines 7

A Data-Driven Two-Stage Dynamometer Diagram Inference Approa...

引用

IEEE 7th international conference on Industrial Cyber-Physical systems (ICPS)

作者： Zhang, Jiusi Luo, Hao Tian, Jilun Qiao, Xinyu Xu, Xiaoyi Jiang, Yuchen Yin, Shen Kaynak, Okyay Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China Norwegian Univ Sci & Technol Fac Engn N-7034 Trondheim Norway Bogazici Univ Dept Elect & Elect Engn TR-34342 Istanbul Turkiye

ISBN: (纸本)9798350363029;9798350363012

In recent years, the soft measurement and state estimation of key parameters of industrial control systems and critical infrastructure have become crucial. The acquisition of dynamometer diagrams for pumping machines holds great significance in monitoring the operational status of drilling industrial control systems. To achieve the accurate inference of dynamometer diagrams from electrical parameters under variable operational conditions, this paper proposes a data-driven two-stage approach. Specifically, without prior knowledge, this paper proposes an electrical parameter derivation diagram approach based on extreme gradient boosting (XGBoost), which can accurately online suspended load and displacement estimation, thereby enabling efficient dynamometer diagram deduction. Furthermore, facing the variable operational conditions in actual engineering, this paper proposes a two-stage strategy. From a fine-grained perspective, the XGBoost algorithm is constructed separately for the operational condition categories and dynamometer diagram elements. This paper verifies the proposed dynamometer diagram approach under variable operational conditions through the real-world data provided by Daqing Oilfield Research Institute. The experimental result demonstrates the advantages of the proposed approach.

关键词： Industrial control systems Pumping machines Dynamometer diagram data-driven Variable operational conditions

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Multi-Label Classification for Fashion data: Zero-Shot Classifiers via Few-Shot Learning on Large Language Models 16

Multi-Label Classification for Fashion Data: Zero-Shot Class...

引用

16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Jiang, Dongming Shah, Abhishek Yeung, Stanley Zhu, Jessica Singh, Karan Goldenberg, George CaaStle Inc. United States

ISBN: (纸本)9789897587160

Multi-Label classification is essential in the fashion industry due to the complexity of fashion items, which often have multiple attributes such as style, material, and occasion. Traditional machine-learning approaches face challenges like data imbalance, high dimensionality, and the constant emergence of new styles and labels. To address these issues, we propose a novel approach that leverages Large Language Models (LLMs) by integrating few-shot and zero-shot learning. Our methodology utilizes LLMs to perform few-shot learning on a small, labeled dataset, generating precise descriptions of new fashion classes. These descriptions guide the zero-shot learning process, allowing for the classification of new items and categories with minimal labeled data. We demonstrate this approach using OpenAI’s GPT-4, a state-of-the-art LLM. Experiments on a dataset from CaaStle Inc., containing 2,480 unique styles with multiple labels, show significant improvements in classification performance. Few-shot learning enhances the quality of zero-shot classifiers, leading to superior results. GPT-4’s multi-modal capabilities further improve the system’s effectiveness. Our approach provides a scalable, flexible, and accurate solution for fashion classification, adapting to dynamic trends with minimal data requirements, thereby improving operational efficiency and customer experience. Additionally, this method is highly generalizable and can be applied beyond the fashion industry. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Zero-shot learning

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Human Affect Recognition Using Deep Learning Approaches

Human Affect Recognition Using Deep Learning Approaches

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

作者： Seshaiah, M. Bhargavi, D.S. Hema, H. Mallikashree, N. Manuja, C.R. S J C Institute of Technology Department of Computer Science and Engineering Chickballapur India

ISBN: (纸本)9798350359688

An important advance towards more characteristic human-computer interaction is the automatic detection of human impact. Present-day developments entail the integration of physiological and audiovisual sensors to identify emotions in real-world situations, posing a challenge to standard machine learning algorithms. A thorough assessment of creative writing on human affect recognition from 2010 to 2017 is done in this research, with a centre on deep neural network-based approaches. A distinct trend toward the promotion of deep learning (DL) methods is visible in the classification of 950 studies based on whether they used shallow or deep learning architectures. An extensive assessment of deep neural network appeal in this sector is possible through a more concentrated investigation of a subset consisting of 233 papers that use them. The development of spatial, temporal, and joint feature representations for multi- modal sensor data requires DL. To demonstrate the significant advancements made in this area, the study presents estimable state-of-the-art architectures. For academics working on similar applications, the outcome is identified, and the significance of frameworks in the field of human affect is recognized. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring Depression Through Social Media: A Textual Analysis

Exploring Depression Through Social Media: A Textual Analysi...

引用

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

作者： Bhavya, R. Ramani, Roja Royal, G.Leela Gowtham Manideep, G. Sridhar, Gautham New Horizon College of Engineering Department of Cse Bangalore India

ISBN: (纸本)9798350359688

Depression, a widespread mental health affliction, profoundly impacts individuals and societies globally. Timely intervention demands early detection, and the burgeoning realm of user-generated social media text presents a valuable avenue for understanding mental health. This thesis aims to harness machine learning techniques to establish a dependable system for identifying depression within social media text. Gathering a diverse corpus of posts from individuals with and without depression, the developed system demonstrates consistent and accurate performance across various evaluation metrics. The thesis culminates in an in-depth exploration of depression diagnosis using machine learning and natural language processing algorithms applied to social media data. Specifically, the study scrutinizes sentiment analysis and artificial intelligence methodologies for predicting emotional distress from a dataset of 20,000 English tweets obtained via the Twitter API. The analysis employs the text blob library alongside classification techniques encompassing Random Forest, KNN, Decision Tree, and Naïve Bayes model. © 2024 IEEE.

关键词： Tweets

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Application of System Operation and Maintenance knowledge Base based on Machine Learning in Automobile Industry 8

Application of System Operation and Maintenance Knowledge Ba...

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8th international conference on Information systems engineering, ICISE 2023

作者： Cheng, Bin Hao, Qiong Chen, Zhiwen Wuhan Railway Vocational College of Technology College of Computer and Information Engineering Wuhan430000 China

ISBN: (纸本)9798350307009

With the increasingly fierce competition in the automobile industry, the demand for digital transformation is increasingly strong, and the requirements for system operation and maintenance are also increasingly high. In the face of the current situation of system operation and maintenance faced by the automobile industry, such as passive fault detection, repetitive work, and reliance on experience, this paper innovatively proposes a system operation and maintenance knowledge base based on machine learning applicable to the automobile industry. It uses the Scrap crawler to obtain the system history data, uses the decision tree algorithm to extract the value contained in the data, and finally visualizes the results to improve the service quality of operation and maintenance and optimize the operation and maintenance management process. © 2023 IEEE.

关键词： Automotive industry

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Observer-Based Event- Triggered Reinforcement Learning Control for Discrete-time MASs 6

Observer-Based Event- Triggered Reinforcement Learning Contr...

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6th international conference on Electronic engineering and Informatics, EEI 2024

作者： Ma, Linmei Huang, Longwang Gao, Zilin Shi, Haotian School of intelligent manufacturing Chongqing Institute of Engineering Chongqing China School of Automation Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Electronics and Communication Engineering Guangzhou University Guangzhou China

ISBN: (纸本)9798350353594

This paper studies the event-triggered output optimal control for discrete-time multi-agent systems (MASs) with unmeasured system states. A state estimator is initially developed to monitor the agent's states, and subsequently, a new event-triggered reinforcement learning control method is devised using the state observer. To avoid the interdependence of agent control inputs, the hierarchy decomposition method is applied to assign priorities for each agent in the control design. A distributed event-triggered condition is designed for the sensor-to-controller channel of each agent, such that the data transmission among the agents can be greatly reduced. Lastly, stability analysis and numerical simulation are presented to demonstrate availability of the scheme. © 2024 IEEE.

关键词： Multi agent systems

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Validating Production Test Scenarios with Cyber-Physical System Design Models 25

Validating Production Test Scenarios with Cyber-Physical Sys...

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25th IEEE international conference on Business Informatics (CBI)

作者： Biffl, Stefan Hoffmann, David Kiesling, Elmar Meixner, Kristof Lueder, Arndt Winkler, Dietmar CDL SQI Vienna Austria TU Wien Inst Informat Syst Engn Vienna Austria CDP Vienna Austria Otto von Guericke Univ Inst Factory Automat Magdeburg Germany WU Wien Inst Data Proc & Knowledge Management Vienna Austria SBA Res gGmbH Vienna Austria

ISBN: (纸本)9798350315158

Information systems are key facilitators for production planning, configuration, and monitoring in increasingly flexible Cyber-Physical Production systems (CPPSs), such as automated welding lines for joining car parts. The guideline VDI 2206 provides a V-model to organize the engineering disciplines. It emphasizes the importance of validation in CPPS engineering but provides limited guidance on the up-front specification and validation of test scenarios. This paper introduces the Production Test Scenario Validation (PTSV) approach to validate to what extent a multi-view CPPS design model and associated data sources from production engineering and operation represent the concepts used in test scenarios. The PTSV (i) elicits concepts in pre- and post-conditions of production test scenarios, (ii) maps these concepts to a multi-view CPPS design model, such as a production asset network, and (iii) identifies mismatches between production test scenarios and CPPS design models, such as missing model elements or data sources required for validation. We evaluated the effectiveness and efficiency of the approach in a feasibility study on a real-world welding line for joining car parts. The results indicate that the PTSV is feasible and effective for improving the capability to measure and analyze outcomes of test scenarios in CPPS engineering according to the VDI 2206.

关键词： Industry 4.0 cyber-physical production system VDI 2206 behavior-driven development knowledge graph

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