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Knowledge-Data Driven for Cyber-Physical Production systems in the Aerospace Industry: Current Issues and Emergent Technologies

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IFAC-PapersOnLine 2024年第19期58卷 1162-1167页

作者： Murillo Skrzek Anderson L. Szejka Fernando Mas Maria Jose Escalona Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil University of Seville (US) Seville Spain CT Engineering Group Spain

The aerospace manufacturing industry exhibits significant complexity in all its tasks. For instance, the aircraft’s main body comprises several components with multiple dimensions, geometries, and materials. This kind of manufacturing system is specialised in creating aerospace parts characterised by advanced technology, limited production quantities, and a high degree of customisation. The aerospace products and the corresponding manufacturing systems have extensive life cycles spanning decades and are repurposed to accommodate product variations. Any disturbance during the project progression has the potential to result in escalated expenses and time investments, leaving economic and environmental drivers. In this way, Cyber-Physical Production systems (CPPS) are emerging to reduce misinterpretation and mistakes across all stages of the manufacturing process. Therefore, the main aim of this paper is to discuss the current issues and emergent technologies across the literature review to address the following Research Issue 1 (RI1): What are the current issues and emergent technologies in CPPS and KDD for the Aerospace Industry? Research Issue 2 (RI2): What is the gap in CPPS and KDD for the aerospace industry? This initial literature review is concentrated on the Knowledge data-driven (KDD) to aid in developing CPPS for Aerospace Sheet Metal (ASM) parts manufacturing and examining the use of CPPS in the aerospace sector. Finally, this research contributes to the research community with an initial overview of research trends in the domain of KDD for CPPS in the aerospace industry and finds the main research gaps in this area.

关键词： Knowledge data-driven Cyber-Physical Production System Aerospace Industry Content Analysis

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Framework for Automated Synthetic Image Generation for Vehicle Detection 23

Framework for Automated Synthetic Image Generation for Vehic...

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Proceedings of the 2023 6th International Conference on Sensors, Signal and Image Processing

作者： Vitória Biz Cecchetti Bruno José Souza Roberto Zanetti Freire Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Brazil and Pumatronix Electronic Equipment Ltd. Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Universidade Tecnológica Federal do Paraná (UTFPR) Brazil

ISBN: (纸本)9798400707995

The use of synthetic images has been highlighted within the computer vision field as a more practical option that helps reduce the time and cost spent on vehicle detection tasks in urban environments. This paper presents a framework for automated synthetic image generation, aiming at the detection of vehicles in urban environments. The creation of the 3D scenario is carried out through the Blender software. Some adverse conditions of the scenes were developed, including: rain, fog, occlusion by tree branches and electrical power lines, and low lighting. Then the algorithm for the automated generation of images was developed based on parameters chosen by the user to generate the scene and to generate the annotations of the images. Additionally, a convolutional neural network with YOLOv5 architecture along with a cross-validation technique was applied to carry out vehicle detections on the developed synthetic dataset, on both real and mixed datasets. The proposed framework generated a consistent dataset that can be used in vehicle detection networks, obtaining a mAP 0.5 above 96% and mAP 0.5:0.95 above 82%, also the precision values remained above 92%, and recall above 95%. It was also observed that the combination of synthetic images with real images can improve the performance of the model.

关键词： Computer vision Convolutional neural networks Object detection Synthetic images Vehicle detection.

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Influence of cognitively distracting activity on driver’s eye movement during preparation of changing lanes

Influence of cognitively distracting activity on driver’s e...

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SICE Annual Conference

作者： Huiping Zhou Makoto Itoh Toshiyuki Inagaki Doctoral Program in Risk Engineering Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan Department of Risk Engineering Faculty of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (纸本)9784907764302

This paper investigates driverpsilas behavior of checking a traffic situation by analyzing driverpsilas eye movement when the driver is cognitively distracted during preparation of changing lanes. We made an experiment with a motion-based driving simulator under conditions of several types of secondary tasks. Collected data resulted in lower detection rates of intent of changing lanes, less checking behaviors and longer time length of checking behavior. These facts clarified that there are discrepancies between secondary tasks and no secondary one. It showed an influence of cognitively distracting activity on driverpsilas behavior.

关键词： Eye movement Distraction Driver behavior Intent inference Changing lanes Eye Movements Driver Behavior distractability Lanes Drivers detection rate (DR) activity driver circuits

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： Deep learning

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Patient-Centric Healthcare Service systems: Evidence-Based Medicine as Architecturally Significant Requirement

Patient-Centric Healthcare Service Systems: Evidence-Based M...

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IEEE/ACM International Workshop on Software engineering in Healthcare systems (SEHS)

作者： Patrick Letouze Valeria Martins da Silva Jose I.M. de Souza Jùnior Graduate Program of Computational Modeling of Systems Universidade Federal do Tocantins - UFT Palmas Brazil Graduate Program of Systems' Engineering and Computation - PESC Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9781509022076

Healthcare Service systems may use and support evidence-based medicine, and to accomplish that, their design has to be patient-centered focusing on quality of evidence and strength of recommendation. Hence, this paper intends to promote the debate on the design of Healthcare Service systems. We propose a framework that attempts to systematize the design and development of Healthcare Service systems, especially in terms of software engineering. The framework is intended to support the design of Healthcare Service systems by understanding a complex health treatment as a project, which would provide naturally patient-centeredness, and by considering Evidence-Based Medicine guidelines and recommendations as architecturally significant requirements in the design of Healthcare Service systems, which would provide focus on quality of evidence and strength of recommendation. Therefore, we would have a SOA user-centric approach and a systematic service design.

关键词： Guidelines Medical services Medical diagnostic imaging Protocols Systematics Standards Electrocardiography

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Comparing Classification Algorithms to Analyze Twitter Sentiment on Public Opinion on Fuel Oil 24

Comparing Classification Algorithms to Analyze Twitter Senti...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Widarma, Gita Novita, Rice Mustakim Rozanda, Nesdi Evrilyan Universitas Islam Negeri Sultan Syarif Kasim Riau Graduate Program Information Systems Department Pekanbaru Indonesia Postgraduate Programs Universitas Negeri Padang Information Systems Pekanbaru Indonesia Postgraduate Programs Institut Pertanian Bogor Master of Informatics Engineering Pekanbaru Indonesia Postgraduate Programs Universiti Teknologi Malaysia Master of Information Systems Management Pekanbaru Indonesia

ISBN: (纸本)9798350313956

Twitter is currently used as one of the applications that is often used to convey opinions or unrest by the public such as the current issue that is currently rife, namely regarding the increase in fuel oil prices (BBM) in Indonesia. With so much data scattered in a tweet that only consists of a few fragments of words, there must be valuable information or meaning that Twitter users want to convey. Text mining is a very appropriate method to do the analysis because in text mining there is a method that focuses on an opinion which will be concluded into positive and negative opinions. There are several algorithms that are most often used in data mining, namely K-Nearest Neighbor, Decision Tree, and Naive Bayes. The three algorithms used in this study were also compared with the use of feature selection Particle Swarm Optimization (PSO) which is well known for improving the performance of algorithms. There are 2165 negative opinion data and 2835 positive sentiment data. The data will be tried based on distinctive sums of data namely 1000 data, 3000 data, and 5000 data. The classic K-Nearest Neighbor algorithm obtained an accuracy result of 75.60% and made it the best when tested with 1000 data. The use of Particle Swarm Optimization (PSO) is proven to improve accuracy as when tested using 3000 data with an accuracy value of 78.21% on the K-Nearest Neighbor algorithm. In the mean time, in testing 5000 Data, the classic K-Nearest Neighbor once more gotten the most elevated exactness with a esteem of 74.02%. © 2023 IEEE.

关键词： Decision trees

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Asymptotic behavior of the indicator function in the inverse problem of the wave equation for media with multiple types of cavities

arXiv

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arXiv 2024年

作者： Kawashita, Mishio Kawashita, Wakako Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima739-8526 Japan Electrical Systems and Control Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima739-8527 Japan

In this paper, the inverse problem of the wave equation by the enclosure method for a medium with multiple types of cavities is discussed. In the case considered here, the sign of the indicator function of the enclosure method is not determined and sign cancellation may occur, resulting in loss of information. By examining the top terms of the indicator function in detail, we show that the shortest distance to the cavities can be obtained even in such a *** Codes 35R30, 35L05, 35B40, 78A46 Copyright © 2024, The Authors. All rights reserved.

关键词： Wave equations

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Controller tuning with multi-objective optimization: benchmark definitions for SISO linear systems: A PI controller case ⁎

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IFAC-PapersOnLine 2024年第7期58卷 126-131页

作者： Gilberto Reynoso-Meza Ricardo Massao Kagami Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Control Systems Optimization Laboratory (LOSC) Pontifícia Universidade Católica do Paraná (PUCPR)

Multi-objective optimization techniques are practical techniques for controller tuning purposes. A wide variety of papers use such an approach or propose new algorithms to approximate the Pareto front. Nevertheless, despite the expressive volume of works dealing with it, there is no standard benchmark testbed that could be used as a baseline comparison among different techniques. This work addresses such a gap, with a first step proposing a linear-SISO testbed with guidelines and rules and providing a basic example for a PI controller. Such a benchmark is expected to promote further research on the topic.

关键词： Multi-objective optimization Parametric optimization Evolutionary Algorithms

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The shortest lengths and the enclosure method for time dependent problems

arXiv

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arXiv 2023年

In this article, the role of "shortest length" is discussed concerning the inverse problems with the time dependent enclosure method. In the beginning, the inclusions embedded in a non-layered medium are handled. The argument for the above case reveals why the "shortest length" is important. Next, the case where the inclusions are embedded in a two-layered medium is explained in view of the previous works by Ikehata and the authors. In addition, the regularity assumptions for the boundaries of the inclusions are *** Codes 35R30, 35L05, 35B40, 78A46 Copyright © 2023, The Authors. All rights reserved.

关键词： Enclosures

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A Comparative Hardware Implementation of Histogram of Oriented Gradients as a Descriptor in Embedded Tracking of Swarm

SSRN

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SSRN 2023年

作者： Legarda, Diego Antonio Pérez, Karen Viviana Muñoz, Daniel Mauricio Department of Mechanical Engineering Mechatronics Systems Graduate Program University of Brasilia DF Brasilia70910-900 Brazil

The Histogram of Oriented Gradients (HOG) algorithm is widely utilized in image processing for tasks such as detection, classification, and tracking. However, several challenges arise when implementing this algorithm on computing platforms with limited memory and low power consumption, such as mobile robots and drones. This work presents an in-depth analysis and implementation of three innovative hardware architectures for HOG, specifically designed for real-time processing using Field programmable Gate Arrays (FPGAs) in the context of mobile robot localization. The primary focus of these architectures is to simplify the processing operations involved in gradient magnitude and orientation calculation, histogram generation, and normalization. These simplifications lead to a reduction in resource utilization and energy consumption. Experimental results conducted on a Zynq 7020 device demonstrated minimal relative error values throughout the en-Email addresseslegarda8027@*** (Diego Legarda), kperezlujan@*** (Karen Ṕerez), damuz@*** (Daniel M. Muñoz) 1Corresponding authorPreprint submitted to Journal of Parallel and Distributed Computing. July 31, 2023tire process, along with a significant execution time improvement of over 1000 times when compared to the software-based solution. © 2023, The Authors. All rights reserved.

关键词： Mobile robots

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