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Applications of multi-objective optimisation for PID-like controller tuning: a 2015-2019 review and analysis

IFAC-PapersOnLine 2020年第2期53卷 7933-7940页

作者： Tainara Marques Gilberto Reynoso-Meza Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) CuritibaPR Brazil

The first open invited track in multi-objective optimisation for control systems was organised in 2017 with the idea of exchanging ideas and research about how those techniques are valuable for control engineers. Given that control engineering problems are generally multi-objective problems, multi-objective optimisation offers an interesting approach via the simultaneous optimisation of all design objectives. Controller tuning is not except from this. In this paper we perform a review and analysis of the literature, limited to the IFAC environment, to appreciate and detect new tendencies in controller tuning applications via multi-objective optimisation. Time window under consideration is from 2015 to date, coinciding with a previous review on the topic, as well as the emigration of IFAC proceedings to Elsevier.

关键词： Multi-objective optimisation Parametric optimisation Evolutionary Algorithms Intelligent Control Process Control

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Improvement of corrosion resistance and heat dissipation of Mg-6%Al-4%Ca alloy by the addition of Mn, Mm, and the surface treatment

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Keikinzoku/Journal of Japan Institute of Light Metals 2023年第12期73卷 646-650页

作者： Ito, Mizuki Kondo, Natsuki Kirimoto, Yuichi 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 School of Sustainable Design Materials and Engineering University of Toyama Toyama Toyama-shi Japan Department of Nano and Functional Material Science Graduate School of Science and Engineering for Education University of Toyama Toyama Toyama-shi Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama Toyama-shi Japan CHUETSU METAL WORKS CO LTD.. Nakaniikawa-gun Toyama Japan Saitama Industrial Technology Center Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Toyama Imizu-shi Japan

With the recent digitization, electronic equipment housings are required to be thin-walled and have rigidity, heat dissipation, corrosion resistance, and electromagnetic shielding properties. Magnesium alloys are promising materials that satisfy these requirements. Mg-Al alloys have good castability and high strength, but their thermal conductivity decreases. In general, adding alloying elements to magnesium decreases thermal conductivity the effect of aluminum addition is significant. It was found that adding calcium to Mg-Al alloys to form Mg-Al-Ca (AX) alloys results in higher thermal conductivity than that of Mg-Al alloys. However, AX alloys have poor corrosion resistance and require post-treatment such as surface treatment. In this study, AX64, which has high thermal conductivity among the AX series alloys, was used as the basic composition, and corrosion resistance was improved by adding manganese and misch metal, and an attempt was made to improve corrosion resistance and emissivity through surface treatment. The change in thermal conductivity by heat treatment was also investigated. Experimental results showed that the addition of manganese and misch metal improved corrosion resistance and thermal conductivity. Plasma anodization also improved corrosion resistance and emissivity. Heat treatment of these alloys at 400°C improved the thermal conductivity with longer holding time. © 2023 The Japan Institute of Light Metals.

关键词： Corrosion resistance

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Competitive Advantage Model Through Knowledge Management systems Implementation to Optimize Business Sustainability 2021

Competitive Advantage Model Through Knowledge Management Sys...

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

作者： Wahyu Sardjono Erma Lusia Hargo Utomo Samudra Sukardi Aninda Rahmasari Yuri Delano Regent Montororing 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 Gajahmada 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.

关键词： sustainability knowledge management service recovery index business factor analysis

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Mine-to-client planning with Markov Decision Process 18

Mine-to-client planning with Markov Decision Process

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18th European Control Conference, ECC 2020

作者： Leal Gomes Leite, Joao Marcelo Arruda, Edilson F. Bahiense, Laura Marujo, Lino G. Federal University of Rio de Janeiro Industrial Engineering Program Alberto Luiz Coimbra Institute Graduate School and Research in Engineering Rio de Janeiro Brazil Federal University of Rio de Janeiro Systems Engineer and Computer Science Program Alberto Luiz Coimbra Institute Graduate School and Research in Engineering Rio de Janeiro Brazil

ISBN: (纸本)9783907144015

This works presents an innovative application of Markov Decision Process (MDP) to a medium-term mining logistics planning problem considering the mine-to-client supply chain. We implemented three distinct algorithms based on state-of-the-art approaches to solve large-scale problems, and compared their results. Furthermore, we combined all three variants in a single novel algorithm that attained fast convergence and may be an alternative to circumvent the curse of dimensionality underlying large scale problems. © 2020 EUCA.

关键词： Supply chains

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Using Online Grocery Applications during the COVID-19 Pandemic: Their Relationship with Open Innovation

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第2期8卷

作者： Gumasing, Ma. Janice J. Prasetyo, Yogi Tri Persada, Satria Fadil Ong, Ardvin Kester S. Young, Michael Nayat Nadlifatin, Reny Redi, Anak Agung Ngurah Perwira School of Industrial Engineering and Engineering Management Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Malang 65154 Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Industrial Engineering Department BINUS Graduate Program—Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

This present research examines the behavioral intentions of Filipinos to use online grocery applications during the novel COVID-19 pandemic. The study proposes an integration of the health belief model (HBM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) to identify the factors affecting the acceptance and usage of Filipinos of online grocery applications in terms of the impact of health risk for COVID-19. To accurately measure the factors and their relationship to behavioral intentions and usage behavior, a questionnaire was developed and distributed to 373 residents in the Philippines. Partial least squares structural equation modeling (PLS-SEM) was applied as an analytical method for this study. The results revealed that performance expectancy, perceived benefits, perceived severity, and cues to action significantly influenced the behavioral intentions and usage of online grocery apps during the COVID-19 pandemic. The study’s findings can be utilized as a theoretical framework for future researchers of consumer behavior;e-commerce developers;and grocery industry retailers, to enhance the innovation and services of online grocery applications. The results of this study may also be used and capitalized on by investors and managers to apply in strategizing when developing and marketing online grocery applications among con-sumers. Moreover, the framework of this study may be adopted and utilized by other online markets, even in different counties. Further theoretical and practical aspects are discussed in this paper. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： health belief model online grocery applications structural equation modeling UTAUT2

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PID control of hypnotic induction in anaesthesia employing multiobjective optimization design procedures

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IFAC-PapersOnLine 2021年第15期54卷 31-36页

作者： Ricardo Massao Kagami Renan Muniz Franco Gilberto Reynoso-Meza Roberto Zanetti Freire Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifícia Universidade Católica do Paraná (PUCPR) Brazil Department of Chemistry – Apucarana’s Campus Universidade Tecnológica Federal do Paraná (UTFPR) Brazil

General anaesthesia is a clinical procedure that involves the continuous monitoring of several parameters for the correct application of anaesthetics and associated drugs. Focusing on the automatic control in anaesthesia, this work presents a multiobjective optimization design of controllers based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem of drug delivery for induction of anaesthesia. Five Proportional-Integral-Derivative (PID) controllers in a decentralized scheme were tuned for one specific patient and tested in a total of 24 simulated patients. Acting over the infusions of Propofol, Remifentanil, Atracurium, Dobutamine, and Sodium Nitroprusside the proposed controllers could maintain the controlled variables in a safe range for surgical procedures.

关键词： Anaesthesia controller tuning multiobjective problem multivariable control PID controller

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Investigation of manufacturing conditions of single roll rapidly solidified ribbon for anode materials of magnesium rechargeable batteries

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Keikinzoku/Journal of Japan Institute of Light Metals 2023年第11期73卷 537-544页

作者： Kirimoto, Yuichi Fuke, Kousuke Tsukeda, Tadayoshi Aida, Tetsuo Nemoto, Yasunori Ikeda, Minoru Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Department of Nano and Functional Material Science Graduate School of Science and Engineering for Education University of Toyama 3190 Gofuku Toyama-shi-shiToyama Toyama930-8555 Japan Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama Toyama-shi-shiToyama Toyama Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama-shi-shiToyama Toyama Japan FLOW Science JAPAN Shizuoka Hamamatsu-shi Japan CHUETSU METAL WORKS CO. LTD Nakaniikawa-gun Toyama Japan Saitama Industrial Technology Center Saitama Kawaguchi-shi Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

Magnesium rechargeable batteries are attracting attention as next-generation rechargeable batteries because of their resource abundance, cost, safety, and high electrical capacity per volume compared to conventional lithium-ion rechargeable batteries. Our joint research team focused not only on the electrochemically active magnesium composition of ribbon for the negative electrode materials of magnesium rechargeable batteries but also on the production process, focused on the single roll type liquid quenching and rapid solidification method, which can be produced at a lower cost. In this study, the optimal processing conditions were examined by comparing CAE analysis with ribbon actually manufactured, with the aim of further improving manufacturing conditions using the same method, such as length discontinuities, thickness, and surface properties of ribbon. We specifically various process parameters such as injection pressure and roll peripheral speed for evaluation. As a result, several linear cracks were observed on the surface of the ribbon as the peripheral speed increased and as the Gap became wider, and the surface properties tended to deteriorate. Also, It was found that the dominant factor in the length of the ribbon was the peripheral speed. As the peripheral speed increased, the length of the ribbon tended to increase. © 2023 Japan Institute of Light Metals. All rights reserved.

关键词： Surface properties

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Machine learning techniques in bank credit analysis of companies: A case study of a Brazilian bank 49

Machine learning techniques in bank credit analysis of compa...

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49th International Conference on Computers and industrial engineering, CIE 2019

作者： Assef, F.M. Steiner, M.T.A. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Brazil

The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, not always did the best accuracy represent the best technique for the assessment of this problem. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study. © 2019, Computers and industrial engineering. All rights reserved.

关键词： Support vector machines

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Computer Vision with Cognitive Learning to Improve the Decision-Making During the Sales Process in Physical Stores 16th

Computer Vision with Cognitive Learning to Improve the Decis...

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16th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2019

作者： da Silva Ramalho, Vinícius Szejka, Anderson Luis Rudek, Marcelo Junior, Osiris Canciglieri Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil

ISBN: (纸本)9783030422493

In a world where the information is obtained faster than ever seem, new methods to process that high volume of data are being developed frequently. This is more notorious in a virtual ambient where the data is generated in a manner that is faster and easier to analyze than in the real world. This is very evident in the retail field, where virtual stores have easy access to all the advertisement a user visited and simple to obtain user profile, on the other hand physical stores are limited to basically create a register in a database when there is a purchase. In an attempt to improve the retailers experience from physical stores to manage their business this document has the objective to develop a computational tool that will analyze the people flux going in the establishment, trying to inform the retailer the amount of people and their gender to help the sales process in physical stores. To this end, computational vision methods and algorithms were raised, which after selection, theoretical conception and tool’s implementation it was tested with benchmarks to operate locally and in real time by accessing the cameras installed strategically in a real scenario. Two scenarios were tested: static ambient light and dynamic light. Two tests were conducted: YOLOv2 against background subtraction-based counter;gender classification using full body features. Even though the results were not as positive as needed for commercial use, the tool demonstrated potential and space for improvements. © 2019, IFIP International Federation for Information Processing.

关键词： Computer vision

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