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26th International Joint Conference on Industrial engineering and Operations Management, IJCIEOM 2020

作者： Francisco, Rosemary de Freitas Rocha Loures, Eduardo Santos, Eduardo Alves Portela Deschamps, Fernando UNISINOS University São LeopoldoRS Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná CuritibaPR Brazil

ISBN: (纸本)9783030569198

Industry 4.0 is creating a radically more dynamic work environment, which calls for considerable changes in the role of industry professionals. As industries need to respond to evolving trends quickly, as well professionals need to be able to work and constantly learning in this new work environment. Besides, due to the continuous advances in technology and the adoption of these technologies in the industries processes, the level of complexity in the workplace is increasing, which results in the need for a new professional role. However, the main challenge is how to prepare and qualify these professionals to acquire these competences. In order to provide a better understanding of the new professional role and their relationship with the industry 4.0 working environment, this paper introduces an ontology for Competence Management in the Fourth Industrial Revolution. Considering the recent scientific and business developments for preparing the industries and workers for this new working era, competences and skills needed for professionals in industry 4.0 were reviewed, as well as challenges and opportunities. Based on a systematic literature review in scientific sources and grey literature, this work identified what technologies are being adopted and what the professional competences are for working in this working environment. Since Industry 4.0 is spreading all over the world, this study contributes to this understanding and also provides a starting point for further research regarding workforce competence management for Industry 4.0. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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A Method to Gaze Following Detection by Computer Vision Applied to Production Environments 17th

A Method to Gaze Following Detection by Computer Vision Appl...

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17th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2020

作者： Cenzi, Emannuell Dartora Rudek, Marcelo Pontifícia Universidade Católica do Paraná PUCPR Industrial and Systems Engineering Graduate Program – PPGEPS CuritibaPR80215-901 Brazil

ISBN: (纸本)9783030628062

The humans have the natural ability of following objects with the head and eyes and identify the relationship between those objects. This daily activity represents a challenge for computer vision systems. The procedure to identify the relationship between human eye gaze and the trackable objects is complex and demands several details. In this current paper we proposed a review of the main gazing following methods, identified the respective performance of them and also proposed an AI based method to estimate the gaze from 2D images based on head pose estimation. The main important details to be recovered from images are scene depth, head position and alignment and ocular rotation. In this approach we perform a track estimation of the gaze direction without the use of the eye position, and also, the face partial occlusion is considered in the analysis. The proposed approach allows low cost in processing with considerable accuracy at low complexity sceneries, because we don’t need to extract the facial features. Gaze tracking is important to evaluate employees’ attention to specific tasks in order to prevent accidents and improve work quality. The presented method improves the current knowing workflow by applying the head pose estimation instead face detection for training and inference. The promisors results are presented and open points are also discussed. © 2020, IFIP International Federation for Information Processing.

关键词： Neural networks

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Structural health monitoring: Frequency domain analysis of beam with breathing crack 2

Structural health monitoring: Frequency domain analysis of b...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Gunawan, F.E. Kanto, Y. Kamil, I. Sutikno Tran, H.N. Industrial Engineering Department BINUS Graduate Program-Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Mechanical Systems Engineering College of Engineering Ibaraki University Ibaraki Japan Department of Industrial Faculty of Engineering Andalas University Padang Indonesia Department of Mechanical Engineering Department Faculty of Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Automotive Engineering Faculty of Transportation Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam

Structural Health Monitoring (SHM) system can be considered as an Internet-of-Things (IoT) system implemented to engineering structure to provide real-time information on the integrity of the structure. In doing so, the system acquires data from various sensors distributed across the structure and infers damage sensitive quantities. In the research, we proposed a damage index derived from the governing dynamics of the structure in the frequency domain. The proposal was empirically verified by applying the index to analyze the case of a cracked cantilever beam subjected to a harmonic load. The value of the damage index was computed at some points surrounding the crack to understand the effect of the distance of the monitoring point to the crack to the index sensitivity. The results suggest that the index is sensitive to the crack particularly when the observation point is near the crack. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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Optimal Tank Sizing, Reliability, and Financial Feasibility Analysis of Domestic Rainwater Harvesting systems for Semi-Arid Climate: A Daily Linear programming Model

SSRN

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SSRN 2022年

作者： Ruso, Mustafa Akıntuğ, Bertuğ Kentel, Elcin Sustainable Environment and Energy Systems Graduate Program Middle East Technical University Northern Cyprus Campus Kalkanli Guzelyurt Mersin Turkey Civil Engineering Program Middle East Technical University Northern Cyprus Campus Kalkanli Guzelyurt Mersin Turkey Department of Civil Engineering Middle East Technical University Ankara Turkey

Rainwater harvesting (RWH) has proven to be an alternative water supply scheme for sustainable water management of regions with limited water resources. In this paper, a linear programming (LP) model with daily time steps, which minimizes the cost of the rooftop rainwater harvesting system (RWHS), is developed and used to calculate the optimum RWH tank size. The semi-arid island of Cyprus in the Eastern Mediterranean is selected as the case study area. The analysis is carried out at 33 sites with average annual rainfalls ranging between 292 mm and 548 mm to evaluate the effect of rainfall characteristic on the financial feasibility, reliability, resilience, vulnerability, and water savings of the RWHS. The optimum tank size obtained using the LP model for one of the sites is validated using the widely-used water balance simulation model. At 29 of the sites, RWHS investments are found to be financially feasible with a discounted payback period range between 12 and 28 years. The optimum RWH tank sizes determined to be between 2 m 3 and 6 m 3 result in up to 20% reliability with more than 50 m 3 of average annual water saving per single house. It is observed that in addition to rainfall characteristic, the water cost is another critical factor that affects the financial feasibility and water savings of a RWHS especially at regions with low rainfall. The developed LP model is also run using monthly time steps and it is demonstrated that financial feasibility and optimum tank sizes can only be assessed realistically when daily time steps are used. Finally, sensitivity analysis is carried out for the collector area and the average daily water consumption per capita. It is found that the discounted payback period of the RWHS is highly sensitive to the collector area. © 2022, The Authors. All rights reserved.

关键词： Rain

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An Analysis of Flexible Manufacturing on the Support of the Development of Smart Product-Service systems 17th

An Analysis of Flexible Manufacturing on the Support of the ...

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17th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2020

作者： de M. Leite, Athon F. C. S. Canciglieri, Matheus B. Szejka, Anderson L. Goh, Yee Mey Monfared, Radmehr P. de F. R. Loures, Eduardo Junior, Osiris Canciglieri Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná CuritibaParaná Brazil School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough Leicestershire United Kingdom

ISBN: (纸本)9783030628062

Currently, changes in the customer’s mindset and increased flexibility in manufacturing shifted competition between companies to focus on the product’s value rather than its cost. In this context, Product-Service systems (PSSs) are an alternative to traditional value propositions (i.e. pure products, pure services) by combining services with products and providing an experience to customers. In recent decades, PSSs were expanded from solely business aspects to engineering aspects and technical considerations in their scope, such as the paradigms of Technical PSS, Industrial PSS, Digitalized PSS and Cyber-Physical PSS. More recently, due to the application of Industry 4.0 concepts, there is a significant increase of "smart technologies", i.e. sensors;connectivity;mobile application, in PSSs, naming it a new paradigm on the field, known as Smart PSSs. In this context, this research aims to explore and discuss current issues related to the integration of flexible manufacturing processes and Smart PSSs through a literature review and analysis, regarding information sharing, traceability and system reconfiguration based on customer’s requirements. Found literature points to gaps in the theme related to semantic issues and design issues regarding the dynamic reconfiguration of the manufacturing process and the Smart PSS. The modern approaches to the theme, as found in literature, are still not able to cope with the semantic barriers and high volume of heterogeneous information in a holistic manner. Conclusions point that further investigation of the cited issues is required in a systematic manner, as well as the development of models that can cope with the presented issues. © 2020, IFIP International Federation for Information Processing.

关键词： Semantics

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Drivetrain simulation using variational autoencoders

arXiv

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arXiv 2025年

作者： Sharma, Pallavi Urrea-Quintero, Jorge-Humberto Bogdan, Bogdan Ciotec, Adrian-Dumitru Vasilie, Laura Wessels, Henning Skull, Matteo Porsche Engineering Etzelstraße 1 Bietigheim-Bissingen74321 Germany Graduate Program Computational Sciences in Engineering Technische Universität Braunschweig Braunschweig Germany Fraunhofer Institute for Energy Economics and Energy System Technology Joseph-Beuys-Straße 8 Kassel34117 Germany Institute of Applied Mechanics Division Data-Driven Modeling of Mechanical Systems Technische Universität Braunschweig Pockelsstraße 3 Braunschweig38106 Germany

This work proposes variational autoencoders (VAEs) to predict a vehicle’s jerk from a given torque demand, addressing the limitations of sparse real-world datasets. Specifically, we implement unconditional and conditional VAEs to generate jerk signals that integrate features from different drivetrain scenarios. The VAEs are trained on experimental data collected from two variants of a fully electric SUV, which differ in maximum torque delivery and drivetrain configuration. New meaningful jerk signals are generated within an engineering context through the interpretation of the VAE’s latent space. A performance comparison with baseline physics-based and hybrid models confirms the effectiveness of the VAEs. We show that VAEs bypass the need for exhaustive manual system parametrization while maintaining physical plausibility by conditioning data generation on specific inputs. © 2025, CC BY.

关键词： Signal encoding

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Occurrence of antibiotics, hormones and PFAs in surface water from a Nile tilapia aquaculture facility in a Brazilian hydroelectric reservoir

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Chemosphere 2024年 352卷 141444页

作者： Rodrigues, Daniel Aparecido da S. Starling, Maria Clara V.M. Barros, André Luiz C. de Santos, Mônica C. da Silva, Eloísa Stéphanie Viana, Guenther Carlos C. Ribeiro, Lara Fabian da S. Simcik, Matt F. Amorim, Camila C. GruPOA - Research Group on Environmental Applications of Advanced Oxidation Processes Department of Sanitary and Environmental Engineering Universidade Federal de Minas Gerais MG Belo Horizonte Brazil SIMOA - Intelligent Systems for Environmental Monitoring Department of Sanitary and Environmental Engineering Universidade Federal de Minas Gerais MG Belo Horizonte Brazil ProAmb - Graduate Program in Environmental Engineering Department of Environmental Engineering Universidade Federal de Ouro Preto in Memoriam MG Ouro Preto Brazil University of Minnesota School of Public Health 420 Delaware St SE MMC 807 MinneapolisMN55455 United States

This study assessed the occurrence of five antibiotics, three hormones, caffeine, and long and short-chain perfluoroalkyl and polyfluoroalkyl substances (PFASs) in surface water and feedstuff samples obtained from aquaculture cages in Três Marias reservoir in Brazil. This is the first work to evaluate the presence of PFAS in surface water used for aquaculture in Brazil. Solid-phase extraction and low temperature partitioning extraction followed by liquid chromatography coupled to mass spectrometry (LC-MS) were performed to process and analyze surface water samples and feedstuff, respectively. The ecotoxicological risk quotient was calculated for target compounds detected in water. Ciprofloxacin and caffeine were detected in all surface water samples. Pharmaceutical drugs ranged from 0.7 ng L−1 (trimethoprim) to 389.2 ng L −1 (β-estradiol). Estrone (10.24 ng g−1) and β-estradiol (66.20 ng g−1) were also found in feedstuff. Four PFASs (PFOA, PFDoA, PFTeDA, and PFBS) were detected (9.40–15.2 μg L−1) at levels higher than reported in studies conducted worldwide. Ecotoxicological risk assessment indicated high risks for caffeine and PFOA, PFDoA, and PFTeDA with RQ values from 10 to 103. These findings reveal risks to biodiversity, ecosystem integrity and human health considering possible intake of these contaminants by fish consumption due to potential bioaccumulation of these substances. Hence, it is critical to conduct more studies in this direction in Brazil and other low and middle-low-income countries. © 2024

关键词： Water quality

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 912-927页

作者： Alexandra J.Lee Dallas L.Mould Jake Crawford Dongbo Hu Rani K.Powers Georgia Doing James C.Costello Deborah A.Hogan Casey S.Greene Genomics and Computational Biology Graduate Program University of PennsylvaniaPhiladelphiaPA 19104USA Department of Microbiology and Immunology Geisel School of Medicine at DartmouthHanoverNH 03755USA Department of Systems Pharmacology and Translational Therapeutics University of PennsylvaniaPhiladelphiaPA 19104USA Wyss Institute for Biologically Inspired Engineering Harvard UniversityBostonMA 02115USA Department of Pharmacology University of Colorado School of MedicineDenverCO 80045USA Center for Health AI University of Colorado School of MedicineDenverCO 80045USA Department of Biochemistry and Molecular Genetics University of Colorado School of MedicineDenverCO 80045USA

Genome-wide transcriptome profiling identifies genes that are prone to differential expression(DE)across contexts,as well as genes with changes specific to the experimental *** genes that are specifically changed in a context of interest from common differentially expressed genes(DEGs)allows more efficient prediction of which genes are specific to a given biological process under ***,common DEGs or pathways can only be identified through the laborious manual curation of experiments,an inordinately time-consuming *** we pioneer an approach,Specific cOntext Pattern Highlighting In Expression data(SOPHIE),for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be *** apply SOPHIE to diverse datasets including those from human,human cancer,and bacterial pathogen Pseudomonas *** identifies common DEGs in concordance with previously described,manually and systematically determined common *** molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional ***’s measure of specificity can complement log2 fold change values generated from traditional DE *** example,by filtering the set of DEGs,one can identify genes that are specifically relevant to the experimental condition of ***,these results can inform future research *** scripts used in these analyses are available at https://***/greenelab/*** can access https://***/greenelab/sophie to run SOPHIE on their own data.

关键词： Neural network Transcriptomics Software Differential expression analysis Machine learning

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