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IEEE Conference on Decision and Control

作者： Thomas Beckers Jacob Seidman Paris Perdikaris George J. Pappas Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA graduate program in Applied Mathematics and Computational Science University of Pennsylvania Philadelphia PA USA Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which determine the behavior of any real-world system. This omission is unfavorable in two ways: The models are not as data-efficient as they could be by incorporating physical prior knowledge, and the model itself might not be physically correct. We propose Gaussian Process Port-Hamiltonian systems (GPPHS) as a physics-informed Bayesian learning approach with uncertainty quantification. The Bayesian nature of GP-PHS uses collected data to form a distribution over all possible Hamiltonians instead of a single point estimate. Due to the underlying physics model, a GP-PHS generates passive systems with respect to designated inputs and outputs. Further, the proposed approach preserves the compositional nature of Port-Hamiltonian systems.

关键词： Uncertainty Gaussian processes Probabilistic logic Data models Bayes methods Behavioral sciences Kernel

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Relationship between temperature and magnet skew angle in PMSG for low-speed wind applications

Relationship between temperature and magnet skew angle in PM...

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2020 International Conference on Power, Energy and Innovations, ICPEI 2020

作者： Trongtorkarn, Mintra Theppaya, Thanansak Luengchavanon, Montri Prince of Songkla University Hatyai Energy Technology Program Faculty of Engineering Songkhla Thailand Interdisciplinary Graduate School of Energy Systems Songkhla Thailand

ISBN: (纸本)9781728172408

The Permanent Magnet Synchronous Generators (PMSG) was generally used for low speed wind turbines. A skew magnet (rotor) was practically utilized for reducing the starting torque of the PMSG operating at low wind speeds. However, the thermal conduction in the air-coil (stator) can be effected by the magnet skew angle. The thermal conduction was then measured at three different areas, in the middle, at the surface and between the air-coil. The higher thermal conduction is found out to be located in the middle of the air-coil. When the skew angle was zero, the temperature maintained at 41.2°C for 120 minutes at 250 rpm while a 20 degrees angle skew resulted in the lower temperature at 39.5°C for 120 minutes at the same speed. At the higher turbine speed, 500 rpm, the non-skew magnet generated the temperature of 48.8°C for 120 minutes while the temperatures of a 20 degree angle skew read 46°C for 120 minutes. Overall, the magnet skewed angle at 20 degrees decreased the temperature by approximately 4.13% and 5.74% for 120 minutes at 250 and 500 rpm respectively. Therefore, the skew magnet angle can practically reduce the temperature in PMSGs for applications based on low-speed wind turbines. © 2020 IEEE.

关键词： Wind turbines

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Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Vision-based Semantic Communications for Metaverse Services:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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Meta-EHR: A meta-learning approach for electronic health records with a high imbalanced ratio and missing rate

Meta-EHR: A meta-learning approach for electronic health rec...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Hsu-Hsiang Chang Te-Cheng Hsu Yi-Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Institute of Communications Engineering National Tsing Hua University (NTHU) Hsinchu Taiwan Department of Electrical Engineering National Taiwan University (NTU) Taipei Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

Data imbalance is a practical and crucial issue in deep learning. Moreover, real-world datasets, such as electronic health records (EHR), often suffer from high missing rates. Both issues can be understood as noises in data that may lead to bad generalization results for standard deep-learning algorithms. This paper introduces a novel meta-learning approach to deal with these noise issues in an EHR dataset for a binary classification task. This meta-learning approach leverages the information from a selected subset of balanced, low-missing rate data to automatically assign proper weight to each sample. Such weights would enhance the informative samples and suppress the opposites during training. Furthermore, the meta-learning approach is model-agnostic for deep learning-based architectures that simultaneously handle the high imbalanced ratio and high missing rate problems. Through experiments, we demonstrate that this meta-learning approach is better in extreme cases. In the most extreme one, with an imbalance ratio of 172 and a 74.6% missing rate, our method outperforms the original model without meta-learning by as much as 10.3% of the area under the receiver-operating characteristic curve (AUROC) and 3.2% of the area under the precision-recall curve (AUPRC). Our results mark the first step towards training a robust model for extremely noisy EHR datasets.

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Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation

arXiv

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arXiv 2022年

作者： Giammarino, Vittorio Queeney, James Carstensen, Lucas C. Hasselmo, Michael E. Paschalidis, Ioannis Ch. Division of Systems Engineering Boston University BostonMA02215 United States Center for Systems Neuroscience Department of Psychological and Brain Sciences Graduate Program for Neuroscience Boston University BostonMA02215 United States College of Engineering Hariri Institute for Computing & Computational Science & Engineering Boston University BostonMA02215 United States

We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a series of 2D navigation tasks. We show how our methods can leverage animal videos to improve performance over RL algorithms that do not leverage such observations. © 2022, CC BY.

关键词： Reinforcement learning

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A Classification Scheme in Evaluating Undergraduate Students' Feedback Towards Service Quality of Private University 22

A Classification Scheme in Evaluating Undergraduate Students...

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

作者： Prasetyo, Yogi Tri Regondola-Bolata, Lauren Nadlifatin, Reny Persada, Satria Fadil Chuenyindee, Thanatorn Ardiansyahmiraja, Bobby School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines Continuous Quality Improvement Office Mapúa University Manila Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Entrepreneurship Department Binus Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok10220 Thailand Faculty of Business and Economics Universitas Surabaya Surabaya Indonesia

ISBN: (纸本)9781450395816

Student feedback is an important part of service quality of a private university. The purpose of the study was to evaluate undergraduate students' feedback towards service quality of private university. A total of 1555 questionnaires and a total of 201 negative comments were collected from one private university in the Philippines during one academic year. For students' comments, classification schemes were utilized to organize the meaningful categories. It showed that negative personality traits of the employees and poor student accommodation were two most frequent negative comments. Classification schemes were found as a powerful tool to evaluate the students' feedback towards service quality. Finally, that this approach can be applied and extended to analyze students' feedback in different private universities worldwide. © 2022 ACM.

关键词： Quality of service

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Intelligent Phase Contrast Meta-Microscope System

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Nano Letters 2023年第24期23卷 11630-11637页

作者： Chu, Cheng Hung Chia, Yu-Hsin Hsu, Hung-Chuan Vyas, Sunil Tsai, Chen-Ming Yamaguchi, Takeshi Tanaka, Takuo Chen, Huei-Wen Luo, Yuan Yang, Pan-Chyr Tsai, Din Ping YongLin Institute of Health National Taiwan University Taipei10672 Taiwan Institute of Medical Device and Imaging National Taiwan University Taipei10051 Taiwan Department of Biomedical Engineering National Taiwan University Taipei10051 Taiwan Department of Mechanical Engineering National Taiwan University Taipei10617 Taiwan Innovative Photon Manipulation Research Team RIKEN Center for Advanced Photonics Saitama351-0198 Japan Graduate Institute of Toxicology College of Medicine National Taiwan University Taipei100 Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei100 Taiwan Program for Precision Health and Intelligent Medicine National Taiwan University Taipei106319 Taiwan Department of Internal Medicine National Taiwan University Hospital National Taiwan University Taipei10002 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei11529 Taiwan Department of Electrical Engineering City University of Hong Kong Kowloon999077 Hong Kong Centre for Biosystems Neuroscience and Nanotechnology City University of Hong Kong Kowloon999077 Hong Kong The State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Kowloon99907 Hong Kong

Phase contrast imaging techniques enable the visualization of disparities in the refractive index among various materials. However, these techniques usually come with a cost: the need for bulky, inflexible, and complicated configurations. Here, we propose and experimentally demonstrate an ultracompact meta-microscope, a novel imaging platform designed to accomplish both optical and digital phase contrast imaging. The optical phase contrast imaging system is composed of a pair of metalenses and an intermediate spiral phase metasurface located at the Fourier plane. The performance of the system in generating edge-enhanced images is validated by imaging a variety of human cells, including lung cell lines BEAS-2B, CLY1, and H1299 and other types. Additionally, we integrate the ResNet deep learning model into the meta-microscope to transform bright-field images into edge-enhanced images with high contrast accuracy. This technology promises to aid in the development of innovative miniature optical systems for biomedical and clinical applications. © 2023 American Chemical Society.

关键词： Microscopes

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Mrf-Former: A Multi-Scale Resolution Fusion Approach for Addressing Scale Discrepancies in Remote Sensing Image Change Detection

SSRN

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SSRN 2024年

作者： Yang, Hualin Ouyang, Jiarui Cheng, Junhao Zhang, Wen Li, Hui Li, Xiying Chen, Calvin Yu-Chian Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China - Preferred Program Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong Provincial Key Laboratory of Intelligent Transportation System Sun Yat-sen University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Institute of Artificial Intelligence Guangzhou University Guangzhou China

Dealing with resolution variations in dual-temporal remote sensing images is challenging due to differences in sensor capabilities, altitudes, footprints, resampling, and data fusion. Conventional methods for different-resolution change detection struggle to effectively address these challenges, primarily due to insufficient consideration of global and local feature interactions. In this paper, we propose a novel change detection model that leverages the power of multi-scale resolution fusion(MRF). By incorporating the MRF-Block, our approach extracts hierarchical multi-scale features from the input data, enabling effective capture of contextual information at various granularities. This facilitates improved handling of resolution disparities and promotes interactions between global and local features. Experimental evaluations conducted on diverse datasets, demonstrate the effectiveness of our proposed model in achieving accurate change detection in dual-temporal remote sensing imagery, even in scenarios characterized by varying resolutions. Code is available on-line at https://***/yanghl1914/MRF-Former. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Real engineering Problems in an Undergraduate Course: The learning methodologies and assessment tools

Real Engineering Problems in an Undergraduate Course: The le...

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World engineering Education Forum - Global engineering Deans Council (WEEF-GEDC)

作者： Ricardo A. Diogo André L.A.C. Venâncio Maria Angela M. Roveredo Santos Eduardo F.R. Loures Neri dos Santos Knowledge Engineering&Management Graduate Program Federal University of Santa Catarina Florianopolis Brazil Industrial&Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Control and Automation Engineering/Mechatronics Engineering Pontifical Catholic University of Parana Curitiba Brazil

ISBN: (纸本)9781665424899

This paper presents the context of the Ubiquitous Computing course carried out in an Industrial engineering undergraduate program throughout 2020 and the first semester of 2021. This course took advantage of the Institutional Modernization program led by the Control and Automation engineering Undergraduate program, which consists in the modernization of undergraduate engineering programs at the Pontifical Catholic University of Paraná. The course aimed to help develop competencies, i.e., a set of knowledge, skills, and attributes aligned with Student Outcomes suggested by ABET. Also, the course proposal brought the productive sector very close to the academic environment, proposing real engineering problems as challenges. However, what are the methods and assessment tools for practical learning in a course with modern elements? This paper proposed the flipped learning and the Challenge-Based Learning Framework with the support of the CDIO Framework as learning methodologies to answer such question. Additionally, quizzes, tests, presentations, rubrics, and peer evaluation were applied as assessment tools to measure the student's progress. Finally, students were listened to about their perceptions during the course. The results suggested that the learning methodologies and assessment tools are suitable for the course context, although some improvements are expected for the following course offers. Moreover, students approved the initiative to bring in real challenges proposed by companies, the mentoring hours, and the feedback about the projects.

关键词： Automation Companies Industrial engineering Ubiquitous computing Proposals Mentoring engineering education

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Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

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Journal of Geographic Information System 2021年第3期13卷 318-339页

作者： David Gabriel de Barros Franco Maria Teresinha Arns Steiner Undergraduate Program in Logistics Universidade Federal do Tocantins (UFT) Araguaína Brasil Graduate Program in Industrial Engineering and Systems Pontifícia Universidade Católica do Paraná (PUC-PR) Curitiba Brasil

The increasing demand for energy has intensified recently, requiring alternative sources to fossil fuels, which have become economically and environmentally unfeasible. On the other hand, the increasing land occupation in recent centuries is a growing problem, demanding greater efficiency, particularly in the reuse of abandoned areas, which has become an alternative. An interesting alternative would be installing energy facilities like solar, wind, biomass, and geothermal, in these areas. The objective of this paper is to develop a classification methodology, based on Artificial Intelligence (AI) and Quantum Theory (QT), to automatically carry out the classification of abandoned areas suitable for the settlement of these power plants. Artificial Neural Networks (ANNs) improved by the hybrid algorithm Quantum-behaved Particle Swarm Optimization (QPSO) together with the Levenberg-Marquardt Algorithm (LMA) were used for the classification task. In terms of Mean Squared Error (MSE), the QPSO-LMA approach achieved a decrease of 19.6% in relation to the classical LMA training with random initial weights. Moreover, the model’s accuracy showed an increase of 7.3% for the QPSO-LMA over the LMA. To validate this new approach, it was also tested on six different datasets available in the UCI Machine Learning Repository and seven classical techniques established in the literature. For the problem of installing photovoltaic plants in abandoned areas, the knowledge acquired with the solar dataset can be extrapolated to other regions.

关键词： Solar Energy Soil Reuse Artificial Neural Networks Quantum-Behaved Particle Swarm Optimization

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