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28th ISTE International Conference on Transdisciplinary engineering, TE 2021

作者： Zanon, Gabriel Nogueira Szejka, Anderson Luis De Freitas Rocha Loures, Eduardo Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Paraná - PUCPR Curitiba Brazil

ISBN: (纸本)9781643682082

Digital transformation is now widely discussed and applied in different enterprises and under various organizational aspects. Traditional industries recognize the need to innovate and digitalize their business processes and models. Among these industries, the railway sector presents a vast space for digital transformation. The railway has several operational, regulatory, regional and technological specificities that must be taken into account in the digitalization processes. Given the multiple aspects involved and the transdisciplinarity, the digital transformation strategy and roadmap of companies must be defined in a way that fits it to the corporate strategy, allowing digital transformation program to be implemented effectively, prioritizing internal sectors with the greatest strategic impact, generating greater productivity, business models improvement, security and, above all, competitiveness for the sector. This research aims to propose a model for the definition of the digital transformation strategy in the railway companies using the Balanced Scorecard (BSC) as a tool for strategic organizational analysis and multi-criteria decision-making (MCDM) to define the drivers for digital transformation in the railway. Finally, to demonstrate the proposal, a case study will be carried out at a Brazilian railway company. © 2021 The authors and IOS Press.

关键词： Decision making

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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Towards a smart reconfiguration process for complex product manufacturing based on industrial robot cells

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IFAC-PapersOnLine 2022年第2期55卷 108-113页

作者： Murillo Skrzek Leandro L. da Silva Anderson L. Szejka Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil

The cost for companies that can custom their production line with precision and quality, through machining centres and laser cutting machines, is expensive, as the degree of machining freedom increases. Furthermore, the greater the complexity of the product to be manufactured, the more processes and equipment are needed to manufacture it. In this context, this research proposes a smart reconfiguration process for complex product manufacturing, based on industrial robotic manipulators, with 6 or more degrees of freedom, superior to conventional methods. With the integration of SolidWorks and PowerMill software, it was possible to apply the solution in an experimental case of blisk manufacturing milling and laser cutting of an engine head gasket. With CAD, CAM and robot simulation by the PowerMill tool, it was possible to validate the proposed solution, obtaining process data and information of milling and laser cutting. With the analysis of the simulation results, it was possible to identify the limitations of the project, such as (i) restriction in the precision of the machining of the product; (ii) amount of real-time supervision and monitoring information; and (iii) work area limit. However, the project can integrate two manufacturing processes to machine and cut complex parts with flexibility.

关键词： Robotic manipulator Milling Laser Cutting Reconfigurable Manufacturing systems Complex Product Computer-Aided Manufacturing

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A Mixed Integer Linear programming for COVID-19 Related Medical Waste Reverse Logistics Network Design 22

A Mixed Integer Linear Programming for COVID-19 Related Medi...

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4th International Conference on Management Science and industrial engineering, MSIE 2022

作者： Santos, Aira Ronalie P. Maghfiroh, Meilinda Fitriani Nur Sapiter, John Rayam Prasetyo, Yogi Tri Redi, Anak Agung Ngurah Perwira Persada, Satria Fadil Young, Michael Nayat Nadlifatin, Reny Ardiansyahmiraja, Bobby School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Industrial Engineering Universitas Islam Indonesia Yogyakarta Indonesia Industrial Engineering Department Binus Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Faculty of Business and Economics Universitas Surabaya Surabaya Indonesia

ISBN: (纸本)9781450395816

Medical wastes bring probable hazards and public risks if not handled correctly, especially during the covid-19 pandemic. Waste management costs are rising due to continues devastation of covid-19 virus. Increased care and handling processes have been implemented to avoid further spreading the infection while ensuring the proper disposal. Given this, medical waste related to covid-19, including other wastes generated by medical facilities, requires delicate integration into the waste management process thru a reverse logistics network for cost-efficient collection, processing, and transport style. Using AMPL software as solving tool, this paper was designed to utilize a mixed-integer linear programming model of a reverse logistics network to improve medical waste management by identifying the total minimum cost of the waste disposal cycle from Waste generating facilities, treatment facilities, and to the disposal factory. © 2022 ACM.

关键词： COVID-19

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Portfolio Selection Using Mean Variance Theory Based on the 30-year Historical Returns of Selected Subsectors of the Philippine industrial Sector

Portfolio Selection Using Mean Variance Theory Based on the ...

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2024 IEEE International Conference on industrial engineering and engineering Management, IEEM 2024

作者： Yamanaka, A.D. Young, M.N. Chuahay, T.N. Pratseyo, Y. Persada, S. Nadlifatin, R. School of Graduate Studies Mapua University Manila Philippines School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Finance Chung Yuan University Taoyuan City Taiwan Department of Industrial Engineering and Management Yuan Ze University Taoyuan City Taiwan International Bachelor Program in Engineering Yuan Ze University Taoyuan City Taiwan Entrepreneurship Business Creation Business School Binus University West Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798350386097

Portfolio selection is an important part of investment management that aims to select the right combination of assets to achieve an optimal risk-return ratio. This study integrates the Mean-Variance Theory utilizing the 30-year historical returns of the Philippine Stock Exchange. Three subsectors of the industrial Sector were used as the investment pool. The back-test results showed that the Mean-Variance portfolios of selected subsectors can outperform the industrial sector and not the market. Stakeholders must be cautious when investing in these three subsectors, considering the values obtained from the simulations. There are recommended portfolio selections wherein companies have a specific allocation that investors and financial managers can use whenever they decide to invest in these subsectors. Overall, this study offers a framework for investors to use as a guide in diversifying their stakes in the Philippine Stock Exchange. © 2024 IEEE.

关键词： Decentralized finance

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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

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IEEE Transactions on Services computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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PPM-HC: A method for helping project portfolio management based on topic hierarchy learnings

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International Journal of Business Intelligence and Data Mining 2021年第3期18卷 364-382页

作者： Pinto, Ricardo A.M. Bernardini, Flavia Marcacini, Ricardo M. Graduate Program in Industrial Engineering and Computing Systems Fluminense Federal University Jardim Bela Vista Rio das OstrasRJ Brazil Computing Institute Fluminense Federal University São Domingos NiteróiRJ Brazil Information Systems Federal University of Mato Grosso do Sul P.O. Box 210 Três LagoasMS Brazil

The projects categorisation is a crucial step in the project portfolio management (PPM). Categorising projects allows the organisation to identify categories with a lack or excess of projects, according to its strategic objectives. In this work, we present a new method for project portfolio management based on hierarchical clustering (PPM-HC) to organise the projects at several levels of abstraction. In the PPM-HC, similar projects are allocated to the same clusters and subclusters. PPM-HC automatically learns an understandable topic hierarchy from the project portfolio dataset, thereby facilitating the (human) task of exploring, analysing and prioritising the projects of the organisation. We also proposed a card sorting-based technique which allows the evaluation of the projects categorisation using an intuitive visual map. We carried out an experimental evaluation based on a benchmark dataset and we also presented a real-world case study. The results show that the proposed PPM-HC method is promising. © 2021 Inderscience Enterprises Ltd.

关键词： Investments

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Selection of industry 4.0 Technologies: A research agenda

Selection of industry 4.0 Technologies: A research agenda

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IISE Annual Conference and Expo 2021

作者： Santos, Reginaldo Carreiro de Lima, Edson Pinheiro da Costa, Sérgio Eduardo Gouvêa Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná PR Curitiba Brazil

ISBN: (纸本)9781713838470

The technological paradigm shift is supporting the digital transformation of manufacturing, characterized by emerging industry 4.0 technologies, and the inherent complexity in selecting appropriate technologies. This increasing complexity of technologies, the multiple and conflicting decision criteria, and the abundance of technology alternatives demand a structured process for managers to select technologies to be acquired and implemented. This work derived from the observation of a real problem in a recognized world class automaker company. The identified problem consists in a lack of a consolidated process (procedure) to support managers and specialists in selecting industry 4.0 technologies. This paper aims at extending theory concerning technology management and selection, by proposing a research agenda regarding the solution for the defined problem. A systematic literature review (SLR) is performed to support it, comprising the analysis and synthesis of a review portfolio. The construction of a structured procedure for industry 4.0 technology selection would serve two purposes: (i) to identify the most suitable technology to solve a specific problem, (ii) to support the creation of a technology roadmap to ground a long-term investment plan. The procedure should be able to deal with both purposes to support firms in a digital transformation journey, enabling an objective, rational and traceable decision-making process. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

关键词： Industry 4.0

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Experiences with Postdoctoral Training in Human Factors: Mentor and Trainee Perspectives 67th

Experiences with Postdoctoral Training in Human Factors: Men...

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67th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2023

作者： Caine, Kelly Catchpole, Ken Sasangohar, Farzan Chen, Karen B. Hegde, Sudeep Souders, Dustin McDonald, Anthony McLaurin, Elease J. Clemson University School of Computing ClemsonSC United States Medical University of South Carolina Clinical Practice and Human Factors CharlestonSC United States Texas A&M University Industrial and Systems Engineering CentralSC United States North Carolina State University Edward P. Fitts Department of Industrial and Systems Engineering RaleighNC United States Clemson University Department of Industrial Engineering CentralSC United States Clemson University Psychology Department ClemsonSC United States University of Wisconsin-Madison Department of Industrial and Systems Engineering MadisonWI United States Oak Ridge Institute for Science & Education Fellowship Program Centers for Disease Control & Prevention PhiladelphiaPA United States

Postdoctoral training positions are becoming more common in the human factors and ergonomics (HFE) discipline. However, conversations related to training in the HFE discipline have largely focused on undergraduate and graduate education. This panel assembles both postdoctoral mentors and former trainees who collectively have a diverse set of HFE-related postdoctoral experiences. By panelists discussing their experiences, observations, and recommendations related to postdoctoral training with the audience, the panel session will support HFE faculty and students in making more informed decisions about if and how a postdoctoral experience (either as a mentor or trainee) could be a part of their career development in HFE. © 2023 Human Factors and Ergonomics Society.

关键词： education human factors mentorship postdoctoral training

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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