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

共 36972 条记录，以下是4771-4780 订阅

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CHARACTERISATION OF DEFECTS IN STRAND-BASED THERMOPLASTIC COMPOSITE PARTS USING ULTRASONIC INSPECTION

CHARACTERISATION OF DEFECTS IN STRAND-BASED THERMOPLASTIC CO...

引用

SAMPE 2024 conference and Exhibition

作者： Pattery, Hanae Smith, Adam Pik, Arthur Canart, Jean-Philippe Tabiai, Ilyass Dubé, Martine Department of Mechanical Engineering École de technologie supérieure Montréal Canada Québec Canada Polytech Orléans Orléans France Teijin Carbon America Inc Rockwood United States

ISBN: (纸本)9781934551455

Strand-based compression moulding compounds are increasingly used in both the aerospace and automotive industries, as they offer an interesting compromise between processability and performance. Non-destructive inspection via ultrasonic scan is often used to assess the internal quality of composite structures, revealing the size and location of defects such as voids, delamination and foreign body contamination. However, the heterogeneous structure of strand-based composite components makes it difficult to obtain meaningful results using this method. Therefore, ultrasonic inspection is complemented using optical microscopy and photographs. The aim of this paper is to correlate c-scan data to manufacturing defects. During the production of pre-impregnated thermoplastic composite textiles known as prepregs, the edges exhibit unacceptable variations in thickness and fibre content. These edges are trimmed and these scraps of prepregs carbon fibre with polyetheretherketone are landfilled. This waste, referred to here as tape edge trim (TET), resembles traditional strands but comes in a variety of shapes and lengths. Compression-moulded panels were made from TET strands of similar length obtained through sieving. The panels underwent the analyses mentioned above. It appears that resin concentration areas and out-of-plane TET strands orientation prevail in panels made with smaller strands. © 2024 Soc. for the Advancement of Material and Process engineering. All rights reserved.

关键词： Automotive industry

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Application of Computer data Algorithms in Enterprise Intelligent manufacturing Operation Management System

Application of Computer Data Algorithms in Enterprise Intell...

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data Science and Network Security (ICDSNS), international conference on

作者： Jingyan Yuan Feng Yuan School of Arts and Sciences University of Rochester Rochester New York USA School of Economics and Management Jilin Engineering Normal University Changchun Jilin China

With the development of the times, the use of intelligent manufacturing operation management systems has gradually emerged in enterprises. Nowadays, intelligent manufacturing operation management systems have achieved significant results, but the system is not perfect and there are still some problems in operation. Therefore, this article studied the application of computer (AC) data algorithms in enterprise intelligent manufacturing operation management systems, which optimized intelligent manufacturing operation management systems through computer data algorithms. This article tested the improvement rate of manufacturing output of enterprises after using computer data algorithms through experiments. The highest improvement rate in the experimental data was 24%, and the lowest was 16%. From this experimental data, it can be seen that computer data algorithms have played a good role in the intelligent manufacturing operation management system of enterprises, in order to improve the manufacturing output of enterprises.

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Classification of Arrhythmia using Electrocardiogram based Image Features with Deep Learning 1

Classification of Arrhythmia using Electrocardiogram based I...

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1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Reddy, K. Naga Sankar Coumar, S. Oudaya Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Electrical and Communication Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350342796

Digital health technology leverages artificial intelligence for real-time monitoring of patients' arrhythmia conditions. Heartbeat data are captured via an electrocardiogram (ECG) and processed to extract pivotal features of each heartbeat. These extracted characteristics serve as the foundation for the training of data-driven models, facilitating the detection and measurement of arrhythmias. The success and reliability of these algorithmic decision-making systems heavily depend on the quality of features extracted, which typically necessitate significant empirical and domain-specific knowledge. To address these challenges, the current work introduces a novel method for arrhythmia classification that hinges on image features, derived using deep learning techniques, with the aim of augmenting the accuracy of the decision-making algorithms. Denoised one-dimensional (1D) ECG signals corresponding to various arrhythmia conditions are transformed into scalograms via the continuous wavelet transform (CWT) process. These scalograms are then classified with the help of a convolutional neural network (CNN), a powerful deep learning model. The proposed methodology exhibits an impressive classification accuracy of 91.52%, underscoring its potential effectiveness. This approach opens avenues for the establishment of trustworthy healthcare systems, enabling precise monitoring of patients' cardiac health, thereby facilitating timely and appropriate intervention. © 2023 IEEE.

关键词： arrhythmia classification continuous wavelet transform convolutional neural networks image features

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Detecting the Change in Representation in Election data by applying Kullback-Leibler Divergence to Attributes data 28th

Detecting the Change in Representation in Election Data by a...

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28th international conference on knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Morihara, Haruka Sophia Tajima, Ren Nagoh, Yumiko Sekiguchi, Kaira Ohsawa, Yukio The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8654 Japan

The diversity of members of the National Diet in Japan has remained to be low in Japan and the lack of representation has been pointed out. In this study, we propose a model that mathematically measures descriptive representation in the multiple steps of undergoing an election, using the attribute data of members of the parliaments in three democratic countries. First, we modeled the three groups that undergo an election process from a general public group to a candidate group, then to an elected group. Next, by quantifying representation from the distribution of attributes in each group using Kullback-Leibler divergence (KLD) and comparing among the groups, we examined the change in the degree of representation. As a result, we found some consistent results with the current discourse in political science research and proposed further research questions regarding the representation in the candidate group. The results have double implications (1) modelling an election as a multiple steps process enables us to examine in which steps the degree of representation changes and (2) the KLD is useful in measuring and comparing the representation between the candidate group and the elected group. © 2024 The Authors.

关键词： data assimilation

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ICISE 2022 - 2022 7th international conference on Information systems engineering

ICISE 2022 - 2022 7th International Conference on Informatio...

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7th international conference on Information systems engineering, ICISE 2022

ISBN: (纸本)9781450397889

The proceedings contain 13 papers. The topics discussed include: a proposed approach to crowd selection in crowdsourced requirements engineering for mobile apps;swift search an open-source search engine;canvas mobile application for English language learning through corrective feedback;ant colony optimization algorithm in the design of university subject teaching platform;a systemic big data framework for the charging pile business;gain property and data analysis for diagnosing failures in a high-efficiency induction motor;crisis impact on use of technology. evidence from omnichannel restaurant sales during COVID-19 pandemic;point cloud scene reconstruction based on multi-planar fitting;research on the competency framework of management accounting profession in the age of ‘great wisdom moving cloud’ of China based on knowledge graph;and understanding intention to use Netflix in Taiwan: integrating perceived value into the technology acceptance model.

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Anomaly Ranking of Failure Causes in manufacturing Process Using Causal Model

Anomaly Ranking of Failure Causes in Manufacturing Process U...

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IEEE international conference on systems, Man, and Cybernetics (SMC)

作者： Hattori, Reiko Ota, Yuya Fujii, Toru Nakajima, Hiroshi OMRON Corp Technol & Intellectual Property HQ Kyoto Japan

ISBN: (纸本)9781665442077

Identifying causes of unknown failures during the manufacturing process is quite difficult because of the uncertainty nature of the failures. As the manufacturing process becomes more complicated, failure-cause identification becomes more time consuming due to the increased number of causal candidates to be checked when addressing anomalies, and the complicatedly intertwined factors are difficult to discern. To solve these problems, various methods of anomaly detection and ranking have been developed using sensor data gathered during the manufacturing process. However, once an anomaly occurs at any point in the process, it propagates to other steps. Conventional anomaly detection methods are not sufficient against the complicated and unpredictable anomalies. In this study, we developed a novel anomaly ranking method based on a causal model. It employs a fusion model of human knowledge and sensor data to acquire a suitable causal model for accurate ranking. Experiments were conducted on a real packaging machine, the results of which showed a roughly 30% reduction of the ranking average compared with that of the conventional methods.

关键词： Anomaly detection

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Semantic Behaviour Tree Learning through Kinesthetic Demonstrations for Position-Force Controlled Robotic Applications

Semantic Behaviour Tree Learning through Kinesthetic Demonst...

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IEEE international conference on Automation Science and engineering (CASE)

作者： Lorenzo Fratini Niccolò Lucci Matteo Malavenda Andrea Maria Zanchettin Paolo Rocco Politecnico di Milano Department of Electronics Information and Bioengineering Milan MI Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Robots’ semantic understanding of their surroundings is critical to make them flexible and autonomous in decision-making. However, this semantic knowledge must be transferable to the robot, executable with commands that the robotic agent can understand, and adaptable in case of unexpected events. In this work, we propose a method that exploits Programming by Demonstration (PbD) to provide the robot with semantic Behaviour Trees (BTs). These encapsulate the Skill’s semantic knowledge through Predicates and its execution through position and force primitives. Each tree can be enhanced with additional demonstrations, creating new Modes to achieve the same action. Together with a knowledge base and a PDDL planner, BTs provide the robot autonomy, flexibility and reactiveness.

关键词： Computer aided software engineering Automation Semantics knowledge based systems Force Decision making Programming manufacturing Robots Monitoring

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Towards data-Driven Reliability Modeling for Cyber-Physical Production systems

Towards Data-Driven Reliability Modeling for Cyber-Physical ...

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12th international conference on Ambient systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Friederich, Jonas Lazarova-Molnar, Sanja Univ Southern Denmark Maersk Mc Kinney Moller Inst Odense Denmark

Reliability is one of the most important performance indicators in contemporary production facilities. Increasing reliability of manufacturing systems results in their prolonged lifetimes, and reduced maintenance and repair costs. Reliability modeling is a common technique for deriving reliability measurements and illustrating relevant fault-dependencies. There is a significant body of research focusing on hardware- and software reliability models, such as Fault Trees, Petri Nets and Markov Chains. Up until now, development of reliability models has been a labor-intensive and expert-knowledge-driven process. To remedy that, through the prevalence of data stemming from the new and technologically advanced manufacturing systems, we propose that data generated in modern manufacturing lines could be used to either automate or at least to support development of reliability models. In this paper, we elaborate on the details of our proposed framework for data-driven reliability assessment of cyber-physical production systems. We, furthermore, introduce a case study that will aid the development and testing of the proposed novel data-driven approach. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Cyber-Physical Production systems Reliability Analysis data-Driven Reliability Modeling

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Management of the Traffic Networks Transportation in Supplier-Customers Services by Strategic Games 11

Management of the Traffic Networks Transportation in Supplie...

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11th international conference on Information Technology and Quantitative Management, ITQM 2024

作者： Bîlbîie, Radu Popescu, Dumitru Dimon, Catalin Department of Automatic Control and Systems Engineering UPB Bucharest Romania Academy of Romanian Scientists Ilfov 3 Bucharest050044 Romania

This paper presents an innovative approach to optimize traffic networks in a supplier-customer system based on specific strategies of game theory. The traffic network is represented as a routing configuration in which the arcs represent various possible roads between supplier and customer entities, each route having an associated transport cost for the product unit. The game structure is defined by a matrix of transport costs between the players, supplier and customer, with their corresponding strategies. Based on these costs, the game matrix is built to which the existing quantities in warehouses are added, respectively the quantities requested by the final customers. By solving the game associated to the transportation traffic network, the different strategies are proposed for choosing the optimal solution. The simulation results confirm the effectiveness of the proposed approach and recommend some performant procedures for solving the game, such as Hurwicz, Laplace and Savage, close to optimal solution. This research opens new directions for management optimization in other domains such as comunication, electrical and administrativ networks. © 2024 The Authors.

关键词： Sales

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KSRR: knowledge Centric Semantically Driven Recipe Recommendation Framework Encompassing Collective Intelligence 15th

KSRR: Knowledge Centric Semantically Driven Recipe Recommend...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Shah, Haiya Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9783031889912

In the era of Web 3.0, there is a pressing need for a knowledge-centric model to recommend recipes, as the current structure of the worldwide Web lacks specialized recommendation frameworks for recipe recommendations, even though standard search engines offer some level of recipe recommendation. This paper aims to address this gap by proposing a dedicated recommendation channel. The model utilizes user historical activity on food apps as one of the criteria for personalization, leveraging the SimRank algorithm and Grey Wolf optimization to generate a comprehensive termset. Additionally, the model incorporates semantic similarity-based reasoning, employing techniques such as SimRank, Morisita overlap index, and normalized information distance. The Grey Wolf optimization framework further refines the solution set. The topic network comprises recipe ebooks, food blogs, and crawl labels from food blogs, with the inclusion of scientific ebooks to enrich the knowledge base. A restriction classifier is employed to control the features of the data center, ensuring computational feasibility and reducing complexity. Furthermore, a lightweight decision tree classifier demonstrates strong classification performance, characterized by high precision, a lower false discovery rate (FDR), and notable expressive percentage values. Extensive evaluations reveal that the proposed framework outperforms baseline models, establishing it as the leading model for recipe recommendations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Search engines

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