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Passivation-based Control Reconfiguration with Virtual Actuators

IFAC-PapersOnLine 2022年第6期55卷 698-703页

作者： Iury Bessa Vicenç Puig Reinaldo Martínez Palhares Federal University of Amazonas Department of Electricity Manaus Brazil Federal University of Minas Gerais Graduate Program in Electrical Engineering Brazil Supervision Safety and Automatic Control Research Center (CS2AC) Institut de Robótica i Informática Industrial (CSIC-UPC) Universitat Politécnica de Catalunya Barcelona Spain Federal University of Minas Gerais Department of Electronics Engineering Belo Horizonte Brazil

This paper presents a novel approach for designing reconfiguration blocks for fault hiding of linear systems subject to actuator faults based on the passivity/dissipativity theory. For this purpose, the concept of passivation block is used to design virtual actuators (VAs) which guarantee that the faulty plant achieves the desired passivity indices and consequently the stability. Linear matrix inequalities (LMI)-based conditions are provided for designing the proposed VAs for ensuring the stability recovery for linear systems. Finally, a numerical example is used for assessing the proposed approach.

关键词： Fault tolerant control Reconfiguration Block Fault hiding Dissipativity Passivity

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Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter

Social Media Analysis using Sentiment Analysis on COVID-19 f...

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International Conference on Information Technology, Information systems and electrical engineering (ICITISEE)

作者： Fairuz Iqbal Maulana Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter.

关键词： COVID-19 Sentiment analysis Analytical models Data analysis Social networking (online) Blogs Data visualization

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Water management and conductivity studies in novel polymer zirconium-based membranes for PEM fuel cells

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Case Studies in Chemical and Environmental engineering 2025年 11.0卷

作者： Ahmed, Tallah Magdi Al Othman, Amani Shamayleh, Abdulrahim Tawalbeh, Muhammad Engineering Systems Management Graduate Program American University of Sharjah PO. Box 26666 Sharjah United Arab Emirates Department of Chemical and Biological Engineering American University of Sharjah PO. Box 26666 Sharjah United Arab Emirates Energy Water and Sustainable Environment Research Center American University of Sharjah PO. Box 26666 Sharjah United Arab Emirates Department of Industrial Engineering American University of Sharjah PO. Box 26666 Sharjah United Arab Emirates Sustainable and Renewable Energy Engineering Department University of Sharjah PO. Box 27272 Sharjah United Arab Emirates Sustainable Energy & Power Systems Research Centre RISE University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates

Proton exchange membrane fuel cells (PEMFCs) are among the promising alternatives for clean energy generation, especially when hydrogen is the fuel used. Their operation is still faced with several challenges, and one of them is water accumulation in the cathode compartment. If PEMFCs are operated at temperatures above the boiling point of water, a significant obstacle would be eliminated due to the formation of water in the vapor phase. This prevents water from accumulating in the liquid phase which causes flooding. Therefore, this work aims to develop a novel composite membrane based on zirconium silicate (ZrSiO₄) and Polyvinylidene fluoride (PVDF) for such high-temperature operation. The composite membrane was synthesized using solution casting, and its structural and chemical properties were characterized using thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) techniques. The ZrSiO₄/PVDF membrane demonstrated a maximum proton conductivity of 2.14 × 10⁻³ S/cm at room temperature with 8 wt% ZrSiO₄, along with a water uptake value of 43.85 %. Consequently, when tested above the boiling point of water, the membrane exhibited a high conductivity of 2.13 × 10⁻³ S/cm at 105 °C. The results suggest the suitability of such zirconium silicate-based composite membranes for fuel cell applications operating above the boiling point of water. © 2024 The Author(s)

关键词： Composite membranes PEM fuel cells Proton conduction PVDF Water management Zirconium silicate

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Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

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npj Flexible Electronics 2020年第1期4卷 243-254页

作者： Zixuan Zhang Tianyiyi He Minglu Zhu Zhongda Sun Qiongfeng Shi Jianxiong Zhu Bowei Dong Mehmet Rasit Yuce Chengkuo Lee Department of Electrical&Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore Centre for Intelligent Sensors and MEMS(CISM) National University of Singapore4 Engineering Drive 3Singapore 117576Singapore Hybrid Integrated Flexible Electronic Systems(HIFES) 5 Engineering Drive 1Singapore 117608Singapore Smart Systems Institute National University of Singapore3 Research LinkSingapore 117602Singapore National University of Singapore Suzhou Research Institute(NUSRI) Suzhou Industrial Park215123 SuzhouChina Department of Electrical and Computer Systems Engineering Monash UniversityMelbourneVIC 3800Australia NUS Graduate School for Integrative Science and Engineering(NGS) National University of SingaporeSingapore 119077Singapore

The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable *** reveals sensory information in daily life containing personal information,regarding identification and *** wearable electronics of gait analysis are mainly limited by high fabrication cost,operation energy consumption,or inferior analysis methods,which barely involve machine learning or implement nonoptimal models that require massive datasets for ***,we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory *** sock equipped with self-powered functionality also can be used as wearable sensors to deliver information,regarding the identity,health status,and activity of the *** further address the issue of ineffective analysis methods,an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed,which produces a 93.54%identification accuracy of 13 participants and detects five different human activities with 96.67%*** practical application,we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring,healthcare,identification,and future smart home applications.

关键词： Deep smart IoT

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Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration

arXiv

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arXiv 2021年

作者： Zhou, Yuqi Zhu, Hao Hanasusanto, Grani A. The Department of Electrical & Computer Engineering The University of Texas at Austin AustinTX78712 United States The Graduate Program in Operations Research & Industrial Engineering The University of Texas at Austin AustinTX78712 United States

Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a robust optimal transmission switching (OTS) framework that can effectively relieve grid congestion and mitigate renewable curtailment. We formulate a two-stage distributionally robust chance-constrained (DRCC) problem that assures limited constraint violations for any uncertainty distribution within an ambiguity set. Here, the second-stage recourse variables are represented as linear functions of uncertainty, yielding an equivalent reformulation involving linear constraints only. We utilize moment-based (mean-mean absolute deviation) and distance-based (∞-Wasserstein distance) ambiguity sets that lead to scalable mixed-integer linear program (MILP) formulations. Numerical experiments on the IEEE 14-bus and 118-bus systems have demonstrated the performance improvements of the proposed DRCC-OTS approaches in terms of guaranteed constraint violations and reduced renewable curtailment. In particular, the computational efficiency of the moment-based MILP approach, which is scenario-free with fixed problem dimensions, has been confirmed, making it suitable for real-time grid operations. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational efficiency

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A weblab control experiment using the ball and beam system and multiobjective optimization 5

A weblab control experiment using the ball and beam system a...

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5th Experiment at International Conference, exp.at 2019

作者： Kagami, Ricardo Massao Kovalski Da Costa, Guinther Mendes, Luciano Antonio Freire, Roberto Zanetti Industrial and Systems Engineering Graduate Program-PPGEPS Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil Department of Control and Automation Engineering Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil

ISBN: (纸本)9781728136370

y associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. Control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant. © 2019 IEEE.

关键词： Multiobjective optimization

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Multi-step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods

Multi-step ahead Bitcoin Price Forecasting Based on VMD and ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ramon Gomes da Silva Matheus Henrique Dal Molin Ribeiro Naylene Fraccanabbia Viviana Cocco Mariani Leandro dos Santos Coelho Industrial & Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Electrical Engineering Federal University of Parana (UFPR) Curitiba Parana Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bitcoin is the leading currency in the cryptocurrency market capturing attention worldwide. Forecasting the Bitcoin price as accurate as possible is essential, but due to its high volatility this task is challenging. Many researchers try, through the years, to develop efficient models for predicting the Bitcoin price using several different data-driven approaches. The objective of this paper is to develop a novel decomposition-ensemble learning model that combines Variational Mode Decomposition (VMD) and Stacking-ensemble learning (STACK) with machine learning algorithms to forecast the Bitcoin price multi-step ahead. The algorithms are k-Nearest Neighbors, Support Vector Regression with Linear kernel, Feed-forward Artificial Neural Network with single-layer perceptron, Generalized Linear Model, and Cubist. Correlation matrix (CORR), principal component analysis (PCA), and Box-Cox transformation (BOXCOX) were used as data preprocessing techniques. Estimating the performance of the proposed models (namely VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) using relative root mean square error, symmetric mean absolute percentage error, and absolute percentage error measures, defined that for one-day-ahead forecast VMD-STAK-BOXCOX model presented the better performance, and for two and three-days-ahead forecast VMD-STACK-CORR model was chosen, compared to VMD, STACK, and machine learning algorithms models' performance. Diebold-Mariano statistical test was conducted to evaluate a reduction in forecasting errors. Therefore, the proposed models (VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) indeed forecast accurately Bitcoin price and outperformed the compared models (VMD, STACK, and machine learning models).

关键词： Predictive models Biological system modeling Data models Bitcoin Training Machine learning algorithms

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Proposal of a framework for a disaster management system

Proposal of a framework for a disaster management system

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2016 industrial and systems engineering Research Conference, ISERC 2016

作者： Foggiatto, Anderson Avanzi, Daniel de Freitas Rocha Loures, Eduardo Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Instituto Curitiba de Informática Curitiba Brazil Federal University of Technology - Parana Curitiba Brazil Department of Mechanical Engineering Federal University of Parana Curitiba Brazil

During a disaster, the more coordinated the response, the lesser its impacts are. One of the factors that has a major influence in response coordination is the interoperability among involved actors - public and private entities that interact to exchange information through the communication of people and systems. This factor is critical because it allows an efficient and effective mobilization of available resources towards reducing the effects caused by an event. This work proposes a framework for the development of Disaster Management systems (DMS) that promote real time communication (information flow and process coordination) among different involved entities, primarily focusing on interoperability in its different levels. This framework is based both on a literature review of scientific works and a survey of worldwide initiatives related to improving disaster response. The main components of the related framework architecture are an SDK (software development kit), a common relational database, multiple communication platforms (e.g., a Web portal and a mobile app), and low-cost hardware to better collect and make needed information available quickly and accurately. © 2016 Proceedings of the 2016 industrial and systems engineering Research Conference, ISERC 2016. All rights reserved.

关键词： Interoperability

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

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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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Determining Factors Affecting Perceived Quality among Shoe Manufacturing Workers towards Shoe Quality: A Structural Equation Modeling Approach

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

作者： Chuenyindee, Thanatorn Torres, Robimarl B. Prasetyo, Yogi Tri Nadlifatin, Reny Persada, Satria Fadil Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok 10220 Thailand School of Industrial Engineering and Engineering Management Mapúa University Manila 1002 Philippines School of Graduate Studies Mapúa University Manila 1002 Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Malang 65154 Indonesia

The shoe industry has been continuously growing in recent years. The goal of this study was to identify factors affecting perceived shoe quality among shoe manufacturing workers. A sample of 350 shoe manufacturing workers participated in answering a survey that was distributed using a purposive sampling approach. Structural Equation Modeling (SEM) indicated that quality training was the most significant factor on perceived shoe quality, indicating that quality training should be prioritized and included in company programs to sustain quality products. Interestingly, perceived tooling/machinery conditions, teamwork and cooperation, the operator’s technical skills, and the operator’s quality mindset, also had significant effects on perceived shoe quality. This study is one of the first studies to explore perceived shoe quality among shoe manufacturing workers. The results may convey information for future research on perceived product quality, particularly for the shoe industry. Finally, our framework can be utilized for the enhancement of perceived product quality in shoe manufacturing industries worldwide. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： perceived quality shoe quality Structural Equation Modeling

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