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Time Stacking Decision Tree for Oscillatory Failure Case Detection in a Flight Control System

IFAC-PapersOnLine 2023年第2期56卷 354-359页

作者： Giberto Reynoso-Meza Victor Henrique Alves Ribeiro Marcelo Feliciano Filho Bianca Zavadisk de Abreu Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifícia Universidade Católica do Paraná (PUCPR) Rua Imaculada Conceição 1155 Zip code 80215-901 Curitiba PR Brazil Silex Curitiba PR Brazil Colégio Estadual da Polícia Militar Curitiba PR Brazil

Flight control systems are an essential part of aircraft, consisting of all elements between the pilot and the movable parts, such as actuators, controllers, and sensors, to control the aircraft's attitude, trajectory and speed. In this context, oscillatory failure cases are problems that can occur in any part of the flight control system, causing undesired oscillatory behavior that affects the aircraft's structural load. On the one hand, correctly detecting such failures enables weight saving to improve aircraft performance in different aspects; on the other hand, such detection enables reconfiguring the control law to keep the aircraft in its path. This work describes a data-driven oscillatory fault detection model and the control law reconfiguration for a fight control system for the Aerospace industrial Benchmark. Preliminary results indicate that the developed system can accurately detect faults in several scenarios and properly reconfigure the control loop.

关键词： Aerospace Guidance, navigation and control of vehicles Fault detection and diagnosis Machine learning

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Mean Variance Portfolio Selection Utilizing Services Subsector’s (Media, Telecommunications, and Information Technology) 30-Year Philippine Stock Exchange Trading Data

Mean Variance Portfolio Selection Utilizing Services Subsect...

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IEEE International Conference on industrial engineering and engineering Management

作者： E.D. Salazar M.N. Young T.N. Chuahay Y. Pratseyo S. Persada R. Nadilfatin Myra F. Contreras School of Industrial Engineering and Engineering Management Mapúa University Intramuros School of Graduate Studies Mapúa University Intramuros Department of Finance Chung Yuan Christian 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 Institur Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

The Philippine Stock Exchange (PSE), established in 1927, has significantly evolved, especially since the 1980s with major reforms and technological advancements. This paper analyzes the performance of portfolios in the Media, Telecommunications, and Information Technology sectors listed on the PSE over a 30year span. Using mean-variance analysis, it evaluates portfolio optimization by balancing risk and return. Drawing on data from 1993 to 2022, the study examines portfolios across various risk-return frontiers (RRFs) and compares the service sector’s performance. The results highlight varying portfolio metrics across RRFs, with some showing outperformance and higher RRFs leading to diminishing returns. The study also identifies key companies with significant stock allocations, providing strategic insights for portfolio construction. These findings offer investors guidance on optimizing portfolio strategies within the PSE for better decision-making and investment outcomes.

关键词： Measurement Decision making Companies Media Telecommunications Resource management Stock markets Portfolios Optimization Investment

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Effect of Cu addition on electrochemical activity of ribbons of Mg-Al-Ca anode materials for magnesium rechargeable batteries manufactured by rapid solidification method

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Keikinzoku/Journal of Japan Institute of Light Metals 2024年第1期74卷 14-17页

作者： Fukui, Shunsaku Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama Japan CHUETSU METAL WORKS CO. LTD. Nakaniikawa-gun Toyama Japan Saitama Industrial Technology Center Kawaguchi Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

The potential for magnesium storage batteries to become the next generation of rechargeable batteries is high. This is largely due to the abundance of magnesium metal resources and their high safety and electric capacity per volume. Our research team has been studying magnesium alloys as anode materials for these batteries. Specifically, we have been investigating the electrochemical activity and mechanism of adding copper to Mg-9mass%Al and Mg-9mass%Al-3mass%Ca. Our findings indicate that the electrochemical activity increased with the addition of 1mass%Cu, but decreased with the addition of 3mass%Cu. In addition, changes in lattice constant and basal orientation were also observed. These changes decreased with 1mass%Cu and increased with 3mass%Cu. SEM observations showed that the cell delimitation was indistinct for the 1%Cu addition. © 2024 The Japan Institute of Light Metals.

关键词： Secondary batteries

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

arXiv

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arXiv 2022年

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. Coelho, Leandro dos 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

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, CC BY-NC-SA.

关键词： Machine learning

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Portfolio Selection and Comparative Portfolio Analysis of Transportation Services, Hotel and Leisure, and Education Subsectors Against Service Sector in the Philippine Market Using Mean-variance Model

Portfolio Selection and Comparative Portfolio Analysis of Tr...

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IEEE International Conference on industrial engineering and engineering Management

作者： E. L. Cruz M. N. Young T. N. Chuahay Y. Prasetyo S. Persada R. Nadilfatin M. Contreras School of Industrial Engineering and Engineering Management Mapua University Manila Philippines School of Graduate Studies Mapua University Manila Philippines Department of Finance Chung Yuan Christian 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: (数字)9798350386097

ISBN: (纸本)9798350386103

This paper presents a framework for portfolio selection in the transportation services, hotel and leisure, and education subsectors of the Philippine market using the mean-variance model. Using the service sector and the broader market as benchmarks, this study spans 7,670 test days from January 1, 1993, to December 31, 2022, identifying optimal risk-return factors (RRFs) of $\mathbf{0. 4}$ for transportation, 0.2 for hotel and leisure, and 0.3 for education. Furthermore, the higher percentage allocations the investors are recommended to invest in are International Container Terminal Services, Inc., Waterfront Philippines, Incorporated, and Far Eastern University, Incorporated. Paired t-test results, with p-values below 0.01 and 0.05, indicate these subsectors can outperform the service sector. The study also conducted a comparative analysis with 30-year historical data to crisis periods like the Global Financial Crisis (GFC) and the COVID-19 pandemic, exhibiting distinct performance trends and emphasizing the need for tailored strategies during economic disruptions. The findings of this research may provide an alternative portfolio selection model for investors seeking to optimize their investment portfolios.

关键词： COVID-19 Analytical models Pandemics Education Transportation Stability analysis Resource management Stock markets Portfolios Resilience

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Space and Time Efficiency Analysis of Data-Driven Methods Applied to Embedded systems

Space and Time Efficiency Analysis of Data-Driven Methods Ap...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Iron Tessaro Roberto Zanetti Freire Viviana Cocco Mariani Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil

ISBN: (纸本)9781728190495

One of the applications of data-driven methods in the industry is the creation of real-time, embedded measurements, whether to monitor or replace sensor signals. As the number of embedded systems in products raises over time, the energy efficiency of such systems must be considered in the design. The time (processor) efficiency of the embedded software is directly related to the energy efficiency of the embedded system. Therefore, when considering some embedded software solutions, such as data-driven methods, time efficiency must be taken into account to improve energy efficiency. In this work, the energy efficiency of three data-driven methods: the Sparse Identification of Nonlinear Dynamics (SINDy), the Extreme Learning Machine (ELM), and the Random-Vector Functional Link (RVFL) network were assessed by using the creation of a real-time in-cylinder pressure sensor for diesel engines as a task. The three methods were kept with equivalent performances, whereas their relative execution time was tested and classified by their statistical rankings. Additionally, the space (memory) efficiency of the methods was assessed. The contribution of this work is to provide a guide to choose the best data-driven method to be used in an embedded system in terms of efficiency.

关键词： Support vector machines Ranking (statistics) Radio frequency Embedded systems Neurons Energy efficiency Real-time systems

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Formability of Third Generation Advanced High Strength Steel: Experimental and Theoretical Approach

SSRN

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SSRN 2024年

作者： Butuc, Marilena C. Vincze, Gabriela Santos, Rafael Pereira, António Santos, Abel D. Amaral, Rui L. Barlat, Frederic Centre for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Portugal LASI - Intelligent Systems Associate Laboratory Guimarães Portugal Graduate Program in Metallurgical Engineering Universidade Federal Fluminense Rio de Janeiro Volta Redonda Brazil Faculty of Engineering University of Porto R. Dr. Roberto Frias s/n Porto4200-465 Portugal INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering Portugal Graduate Institute of Ferrous Technology Pohang University of Science and Technology Korea Republic of

The aim of this paper is to evaluate, predict and analyze the plastic properties and formability of third-generation advanced high-strength steel (3genAHSS), with a specific focus on USS CR980XG3™️ AHSS as the reference material. Forming limit diagrams were determined, utilizing a range of experimental methods, including Nakajima tests, Erichsen tests, uniaxial tensile tests, and the Bulge test. A pivotal aspect is the application of an advanced sheet metal forming limit model based on Marciniak-Kuczynski analysis to simulate localized necking onset. Various constitutive equations, such as von Mises, Hill 48, and Yld2000-2d yield conditions, were used in simulation, alongside strain hardening laws like Swift and Voce. Comparative analyses were conducted to assess the accuracy of different models in predicting forming limits, with a particular emphasis on the influence of strain rate sensitivity. The study underscores the significant impact of the experimental method on forming limit values, highlighting that Yld2000-2d and the Swift hardening law with strain rate sensitivity provide optimal reproducibility of experimental data. Furthermore, the research emphasizes the necessity of considering strain rate sensitivity for accurate numerical simulations, stressing the effectiveness of the Marciniak-Kuczynski model when combined with proper constitutive equations in predicting forming limits for USS CR980XG3™️ AHSS. © 2024, The Authors. All rights reserved.

关键词： Strain hardening

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COMPETITIVE ADVANTAGE MODEL THROUGH KNOWLEDGE MANAGEMENT systems IMPLEMENTATION TO OPTIMIZE BUSINESS SUSTAINABILITY 9

COMPETITIVE ADVANTAGE MODEL THROUGH KNOWLEDGE MANAGEMENT SYS...

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9th International Conference on Information Technology: IoT and Smart City, ICIT 2021

作者： Sardjono, Wahyu Lusia, Erma Utomo, Hargo Sukardi, Samudra Rahmasari, Aninda Regent Montororing, Yuri Delano Information Systems Management Department Binus Graduate Program Bina Nusantara University Indonesia Research Capacity Building Research & Development Bina Nusantara University Indonesia Department of Management Faculty of Economic and Business Gajah Mada University Indonesia Graduate Program Faculty of Economy and Business University of Indonesia Indonesia International Relations Department Faculty of Humanities Bina Nusantara University Indonesia Industrial Engineering Study Program Bhayangkara University Indonesia

ISBN: (纸本)9781450384971

Complaint resolution that arise due to internal and external factors in a company can be monitored through the Service Recovery Index (SRI), and SRI is developed through a number of factors that influence it. Meanwhile, the SRI model needs to be developed to see the business sustainability through implementation knowledge management system in the organization. The data collection method was carried out, starting with building a research instrument based on knowledge management theory and continued with observation and distributing questionnaires to respondents who were involved in the business process of managing SRI. The data analysis method was used to process the data from the questionnaire, factor analysis method was used and the method to build the model used was the regression analysis method. The results obtained from this study are the discovery of 4 (four) factors with a number of indicators that are grouped on each formed factor, which can then be made and built a readiness model that reflects current conditions, so that it can be used to build predictions in the future related to readiness. implementation of knowledge management system development in order to support increased understanding of the service recovery index in the organization. © 2021 ACM.

关键词： Knowledge management

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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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Working in the 4.0 Era: An Ontology for Competence Management in the Fourth industrial Revolution 26th

Working in the 4.0 Era: An Ontology for Competence Managemen...

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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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