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Evolutionary Multi-objective Optimization Design of a Butane Content Soft Sensor ⁎

IFAC-PapersOnLine 2020年第2期53卷 7915-7920页

作者： Victor Henrique Alves Ribeiro Matheus Henrique Reis Marchioro Giberto Reynoso-Meza Industrial and Systems Engineering Graduate Program (PPGEPS) Pontificia Universidade Católica do Paraná (PUCPR) Rua Imaculada Conceição 1155 Zip code 80215-901 Curitiba PR Brazil

industrial processes must be well equipped with a variety of sensors to maintain a desired quality. However, some variables cannot be easily measured due to different causes, such as acquisition and/or maintenance costs and slow acquisition time. This situation leads to a lack of real-time information in the process, which could lead to lower quality in the final product. One of such processes is the debutanizer column, where butane content measurement is highly delayed. To enable online prediction of such variables, available information from the process can be used to estimate predictive models, known as soft sensors. To this end, data-driven techniques can be used, such as statistical and machine learning. However, such techniques usually take into account a single metric when estimating the models, and there are multiple factors that play an important role when designing a soft sensor, such as stability and accuracy. To cope with such a situation, this paper proposes a multi-objective optimization design procedure, where feature selection and ensemble member combination are performed. Therefore, the multi-objective differential evolution algorithm with spherical pruning (spMODE-II) is initially employed for building a pool of non-dominated linear support vector regression (SVR) models. Subsequently, the same evolutionary algorithm is applied for selecting the weights of the previously generated models in a weighted combination ensemble. In a final multi-criteria decision making stage, a preferred ensemble is selected using the preference ranking organization method for enrichment of evaluations (PROMETHEE). Results indicate that the proposed approach is able to produce a highly stable and accurate butane content soft sensor for the debutanizer column.

关键词： Soft sensor Machine learning Ensemble learning Evolutionary algorithms Multi-objective optimization Chemical industry Chemical sensors Monitoring

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Multi-objective control engineering benchmark ⁎

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IFAC-PapersOnLine 2020年第2期53卷 7927-7932页

作者： Gilberto Reynoso-Meza Jesús Carrillo-Ahumada Victor Henrique Alves Ribeiro Tyene Zoraski Zanella Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Instituto de Química Aplicada. Universidad del Papaloapan Circuito Central 200 Colonia Parque Industrial Tuxtepec Oaxaca 68301 México

In this paper we are presenting the statement and evaluation guidelines of a control engineering benchmark, oriented for multi-objective optimisation design techniques. This is done with the aim of promoting new research on this field, by defining a benchmark to have reproducibility and comparability of the three steps involved in the multi-objective process: problem statement, optimisation process and multi-criteria decision making. The proposed benchmark is a single-input single-output process based on the Peltier effect. Rules and guidelines, merged with common practices in control systems engineering, are highlighted and disclosed in the multi-objective open invited track 2020.

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

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Drum-aware ensemble architecture for improved joint musical beat and downbeat tracking

arXiv

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arXiv 2021年

作者： Chiu, Ching-Yu Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals. The source separation module segregates the percussive and non-percussive components of the input signal, over which beat and downbeat tracking are performed separately and then the results are aggregated with a learnable fusion mechanism. This way, the system can adaptively determine how much the tracking result for an input signal should depend on the input's percussive or non-percussive components. Evaluation on four testing sets that feature different levels of presence of drum sounds shows that the new architecture consistently outperforms the widely-adopted baseline architecture that does not employ source separation. Copyright © 2021, The Authors. All rights reserved.

关键词： Blind source separation

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The Evaluation of the Local Beer Industry during the COVID-19 Pandemic and Its Relationship with Open Innovation

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第3期8卷

作者： Ong, Ardvin Kester S. Pequeña, Arianne R. Prasetyo, Yogi Tri Chuenyindee, Thanatorn Buaphiban, Thapanat Persada, Satria Fadil Nadlifatin, Reny School of Industrial Engineering and Engineering Management Mapua University 658 Muralla St. Intramuros Manila 1102 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Department of Industrial Engineering and Management Yuan Ze University 135 Yuan-Tung Road Chung-Li 32003 Taiwan Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok 10220 Thailand BINUS Business School Undergraduate Program Entrepreneurship Department Bina Nusantara University Jakarta 11480 Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia

The beer industry is one of the businesses affected during the COVID-19 pandemic. Despite the exponential growth of the beer industry throughout the years, this aspect of the beverage industry has gained limited attention and has been underexplored. This study aimed to provide a better and up-to-date understanding of Philippine-beer consumers to speed up its recovery. An online survey with 853 volunteer respondents was conducted to investigate Filipinos’ local beer consumption considering frequency, intake, expenses, and preference. A descriptive analysis of the consumers’ self-perceived evaluation of the changes in drinking showed a slight decrease in frequency, intake, and expenses and a minor change in preference. Somers’ d and the chi-squared test results indicated significant relationships between each demographic information (age, sex, and income) and frequency, intake, and expenses. In addition, a conjoint analysis with an orthogonal design indicated that price was the most important attribute (58.025%), followed by primary taste (12.452%), alcohol content (9.706%), mouthfeel (6.445%), aftertaste (6.355%), aroma (5.189%), and, lastly, color (1.827%). The findings of this study could be used as a baseline for improved product offerings, customized advertisements, and market segmentation. Moreover, the results of this study could be applied and extended by breweries to promote and create strategies. Lastly, this study could be extended and utilized by other beverage industries worldwide. © 2022 by the authors.

关键词： beer conjoint analysis consumer preference COVID-19 pandemic expenditure

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Efficient Hardware Implementation of Nonlinear Moving-horizon State Estimation with Artificial Neural Networks

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IFAC-PapersOnLine 2020年第2期53卷 7813-7818页

作者： Rafael Koji Vatanabe Brunello Renato Coral Sampaio Carlos H Llanos Leandro dos Santos Coelho Helon Vicente Hultmann Ayala Department of Mechanical Engineering UnB 70910-900 DF Brazil Industrial and Systems Engineering Graduate Program PUCPR 80215-910 PR Brazil Department of Electrical Engineering UFPR 81531-980 PR Brazil Department of Mechanical Engineering PUC-Rio 22231-180 RJ Brazil

In this contribution we investigate the application of radial basis functions artificial neural networks embedded in hardware for real-time moving-horizon state estimation. The solution of the optimal moving-horizon state estimation problem may be faced as the mapping from the inputs and outputs of the system to the state estimates according to the system model. This mapping may be solved offline with the optimal formulation and then approximated by any higher order function approximation algorithm, such as the ones from machine learning. An approximate version with radial basis functions neural networks is developed and implemented in a Field programmable Gate Array (FPGA) showing good results in terms of accuracy and computational time. We show that the state estimate using the approximate version of the moving-horizon algorithm can be run using a laboratory scale kit of approximately 500 kHz for an inverted pendulum at a clock rate of about 110 MHz. The latency to provide an estimate can be further reduced when FPGAs with higher clocks are used as the artificial neural network architecture is inherently parallel.

关键词： Estimation filtering Nonlinear observers filter design Nonlinear predictive control Neural networks Embedded computer architectures

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Corrigendum to “Techno-economic model to appraise the use of cattle manure in biodigesters in the generation of electrical energy and biofertilizer” [Biomass Bioenergy 150 (2021) 1–11]

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Biomass and Bioenergy 2023年 179卷

作者： Simone Andreia Guares José Donizetti de Lima Gilson Adamczuk Oliveira Industrial and Systems Engineering Graduate Program (PPGEPS) UTFPR Pato Branco Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Department of Mechanical Engineering Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil

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Improving the vocabulary learning by personalized proficiency 12

Improving the vocabulary learning by personalized proficienc...

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12th International Conference on Ubi-Media computing, Ubi-Media 2019

作者： Lin, Yi-Zhen Chen, Kai-Hsiang Huang, Jen-Wei Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan

ISBN: (纸本)9781728128207

To improve the vocabulary ability is very important in language learning. Thus, if we can learn and remember a word very effectively, then we will be able to master a language more quickly. Therefore, many scholars began to propose related research. Due to the learning mechanism of human brain, sometimes when people learn a new knowledge they may forgot at a short time. In order to make the consideration more complete, after analyzing Hermann Ebbinghaus's forgetting curve experiment, we added two variables, one is the acceptance of each word by the same person, and the other is the ability of different people to remember vocabulary. With the above two parameters, we want to design a system to help user to review the vocabulary which may be forgetting before. The forgetting curve can be personalized, and it is more accurate to calculate the best time for each user to review the vocabulary. © 2019 IEEE.

关键词： Game design

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Towards safety level definition based on the HRN approach for industrial robots in collaborative activities

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Procedia Manufacturing 2019年 38卷 1481-1490页

作者： Magno Paiva Hippertt Marcio Lazai Junior Anderson Luis Szejka Osiris Canciglieri Junior Eduardo Rocha Loures Eduardo Alves Portela Santos Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana Curitiba Brazil

Since the first industrial revolution, factories seek to increase their profits by improving their performance and reducing costs in their products. With each new industrial revolution, new ideas on how to reduce costs appear. Currently, in industry 4.0, the fourth industrial revolution, advanced robotics emerges as one of the principal pillars and with-it collaborative robotics. Collaborative robotics is the branch of robotics that has the security systems needed to perform joint activities with humans. However, the quantification of safety level varies depending on the location and more granularly from company to company. This work aims to assign levels of safety that allow a robot to perform a collaborative activity. To propose such values, the work was started with a bibliographical review focused on defining the robots that would be approached, reviewing the ISO (International Organization for Standardization) related to machinery safety and defining the term of collaboration. Then, the method of calculation of the safety indicator was evaluated, in the case covered was the Hazard Rating Number (HRN), and then defined the intervals that will be assigned to the robots that will perform collaborative activities. To validate the defined HRN intervals, a survey was developed and distributed for engineers from automation sector of an automotive company trained in safety, in this survey, categories of robots were placed for the engineers to assign HRN values to them. After the survey, the results were grouped and analyzed analytically. As a result, the proposed HRN intervals were validated because they fit the safety-level stipulated required for a robot to perform collaborative activity. Also, it was concluded that a robot can perform a collaborative activities even if it has not been acquired for this purpose, it is enough that it meets the safety requirements proposed in the work.

关键词： Collaborative Robots industrial Robots Safety Hazard Rating Number Introduction

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Control of a Refrigeration System Benchmark Problem: An Approach based on COR Metaheuristic Algorithm and TOPSIS Method

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IFAC-PapersOnLine 2019年第11期52卷 85-90页

作者： Ricardo Massao Kagami Gilberto Reynoso-Meza Eduardo A.P. Santos Roberto Zanetti Freire Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Brazil

The Proportional-Integral-Derivative (PID) controller is widely used in industrial applications. In order to evaluate the performance of distinct types of controllers, several benchmark systems are available in the specialized literature. In this work, an alternative approach to tune PID controllers based on the Competition Over Resources (COR) metaheuristic optimization algorithm and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method is proposed to solve a vapor compression refrigeration system problem, which is a benchmark control challenge. The proposed technique considered the normalization of multiple cost functions to simplify the selection of two PIDs’ parameters, reducing the need of understanding the influences of cost functions variations in complex problems. The proposed controllers outperformed the base line controllers, providing a performance index 68.39% better than the original approach. Finally, the proposed method was compared with other tuning methods.

关键词： Competition over resources algorithm controller tuning multi-objective problem multivariable control

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A dual-mode deep transfer learning (D2TL) system for breast cancer detection using contrast enhanced digital mammograms

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IISE Transactions on Healthcare systems engineering 2019年第4期9卷 357-370页

作者： Wang, Kun Patel, Bhavika K. Wang, Lujia Wu, Teresa Zheng, Bin Li, Jing Industrial Engineering Program at School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States Department of Radiology Mayo Clinic in Arizona Scottsdale United States School of Electrical and Computer Engineering College of Engineering University of Oklahoma Norman United States

Full-field digital mammography (FFDM) and magnetic resonance imaging (MRI) are gold-standard techniques for breast cancer detection. The newly contrast-enhanced digital mammography (CEDM) integrates the complementary strengths of FFDM and MRI, and is being incorporated into the practice of leading institutions. The current clinical practice using CEDM is sub-optimal because it is primarily based on clinicians' trained eyes. Automated diagnostic systems under the conventional machine learning paradigm suffer from drawbacks, such as the requirement for precise segmentation, extraction of shallow features that do not suffice for diagnostic images, and adoption of a sequential design without a global objective. We propose a deep learning (DL)-empowered diagnostic system using CEDM, the core of which is a novel dual-mode deep transfer learning (D2TL) model. The proposed system is innovative in several aspects, including (1) a dual-mode deep architecture design;(2) use of transfer learning to facilitate robust model estimation under small sample-size;(3) development of visualization techniques to help interpret the model results and facilitate inter- and intra-tumor malignancy quantification;and (4) minimization of human bias. We apply D2TL to classify benign vs. malignant tumors using the CEDM data collected from the Mayo Clinic in Arizona. D2TL outperforms competing models and approaches. © 2019, © 2019 “IISE”.

关键词： breast cancer Deep learning imaging-based diagnosis transfer learning

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