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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Alan R. A. Pinto Filipe A. C. Bahia Reuben P. R. Souza José R. Pinheiro Fabiano F. Costa Post-Graduate Program in Electrical Engineering PPGEE Federal University of Bahia Salvador Brazil Dept. of Electrical and Computer Engineering DEEC Federal University of Bahia Salvador Brazil Dept. of Electrical Engineering DEE Federal University of Campina Grande Campina Grande Brazil

This paper proposes a reconfiguration for a single-phase, double conversion uninterruptible power supply (UPS). With the addition of two static switches to the original circuit, the UPS mitigates the ripple in the current, through battery discharge performed by two legs. Other than these switches, the circuit topology is preserved. The preliminary results show a satisfactory level of ripples for the inductor current and suitable power quality for the output voltage.

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Graph Laplacian Learning with Exponential Family Noise

arXiv

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arXiv 2023年

作者： Shi, Changhao Mishne, Gal The Electrical and Computer Engineering Department UC San Diego CA92093 United States The Halıcıoglu Data Science Institute The Neurosciences Graduate Program UC San Diego CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, these are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to a variational form to account for the inherent stochasticity of the latent smooth representation. Finally, since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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Synthesis, coating, and application of a novel cadmium-free red perovskite quantum dot for micro LED technology

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Materials science and engineering: B 2024年 304卷

作者： Chou, Hsin-Yu Lo, Chih-Wei Kumar Singh, Anoop Kumar Yadlapalli, Bharath Shi, Wei-Yi Chiang, Jung-Lung Wuu, Dong-Sing Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Graduate Institute Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung411030 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan

This study successfully creats cadmium-free red perovskite QDs through a chemical solution method. The CsPb(Br0.2/I0.8)3 QDs were synthesized by adjusting the bromine-to-iodine ratio and was customized for red emission wavelengths. The QDs were coated with silicon dioxide (SiO2) using a sol-gel method to protect their luminescent characteristics from moisture and oxygen and enhancing interface modification. Finally, pixelated red perovskite QDs were applied to a blue light display as a color conversion layer. The excess blue light penetration was addressed using a 23-layer distributed Bragg reflector (DBR), which redirected surplus blue light back to the QDs, amplifying red light by 30% with a reduced blue light penetration rate of 0.5%. Future research may involve achieving a regionally colored display by precisely aligning and bonding pixelated perovskite QDs, processed for red and previously studied green quantum dots, onto a pre-prepared blue light display. © 2024 Elsevier B.V.

关键词： Silica

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The research of factors influencing advanced medical robot use

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Quality and Quantity 2021年第2期55卷 385-393页

作者： Hung, Chin-Liang Department of Computer Science and Information Engineering Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

To understand the factors influencing medical robot use, this study developed a conceptual model and hypothesized that personal innovativeness in IT, perceived usefulness of IT and perceived ease of use of IT positively affect attitude toward medical robots, which in turn facilitates medical robot intention and medical robot use. By analyzing data from 276 surveys from Taiwan’s respondents who have employed medical robots, we demonstrate a positive effect of personal innovativeness in IT, perceived usefulness of IT and perceived ease of use of IT on attitude toward medical robots. In addition, attitude toward medical robots has a positive effect on medical robot intention and medical robot use. The main implications of study for medical robots firms are to conduct business activities to stress perceived usefulness of IT and perceived ease of use of IT, they can use Ads to describe the functions and advantages to draw user’ attitude toward medical robots. © 2020, Springer Nature B.V.

关键词： Attitude toward medical robots Medical robot intention Medical robot use Perceived ease of use of IT Perceived usefulness of IT Personal innovativeness in IT

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Provably Efficient Algorithm for Nonstationary Low-Rank MDPs

arXiv

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arXiv 2023年

作者： Cheng, Yuan Yang, Jing Liang, Yingbin Integrative Sciences and Engineering Programme NUS Graduate School Singapore School of Electrical Engineering and Computer Science The Pennsylvania State University University ParkPA16802 United States Department of Electrical and Computer Engineering The Ohio State University OH43210 United States

Reinforcement learning (RL) under changing environment models many real-world applications via nonstationary Markov Decision Processes (MDPs), and hence gains considerable interest. However, theoretical studies on nonstationary MDPs in the literature have mainly focused on tabular and linear (mixture) MDPs, which do not capture the nature of unknown representation in deep RL. In this paper, we make the first effort to investigate nonstationary RL under episodic low-rank MDPs, where both transition kernels and rewards may vary over time, and the low-rank model contains unknown representation in addition to the linear state embedding function. We first propose a parameter-dependent policy optimization algorithm called PORTAL, and further improve PORTAL to its parameter-free version of Ada-PORTAL, which is able to tune its hyper-parameters adaptively without any prior knowledge of nonstationarity. For both algorithms, we provide upper bounds on the average dynamic suboptimality gap, which show that as long as the nonstationarity is not significantly large, PORTAL and Ada-PORTAL are sample-efficient and can achieve arbitrarily small average dynamic suboptimality gap with polynomial sample complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Comparative Analysis of Normal and ASD EEG Data Using Hjorth Parameters

Comparative Analysis of Normal and ASD EEG Data Using Hjorth...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muliyadi Muliyadi Melinda Melinda Yuwaldi Away Syahrul Gazali Doctoral Program School of Engineering Post Graduate Program Universitas Syiah Kuala Banda Aceh Indonesia Electrical Engineering Department Politeknik Negeri Lhokseumawe Aceh Indonesia Dept. Electrical Engineering and Computer Engineering Faculty Universitas Syiah Kuala Banda Aceh Indonesia Dept. Neurology Universitas Syiah Kuala Banda Aceh Indonesia Mahar Mardjono National Brain Center Hospital Jakarta Indonesia

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

The increasing prevalence of autism spectrum disorder (ASD) in recent decades has underscored the need for more accurate and objective diagnostic methods. Electroencephalography (EEG) holds promise as a tool in ASD neurobiological research due to its ability to capture neural oscillatory activity with high temporal resolution. This study aims to analyze the neurophysiological differences between individuals with ASD and neurotypical populations by examining EEG data using Hjorth parameters. The preprocessing stage involved using a Butterworth filter to enhance EEG signal quality by eliminating noise in the frequency range of 4-40 Hz. The Hjorth parameters, including Activity, Mobility, and Complexity, were used to assess the statistical characteristics of the EEG signals. The analysis was conducted on EEG data collected from 5 typical individuals and 5 ASD ASD subjects. Our findings reveal that the Hjorth Activity parameter is notably lower in the ASD group across all regions, with the most significant differences observed in the left and right frontal areas, where average activity measures 0.236 μV 2 and 0.060 μV 2 in ASD individuals, compared to 0.512 μV 2 and 0.138 μV 2 in the neurotypical group. However, the Hjorth Complexity and Mobility values remained consistent between the two groups, implying that the fundamental structure of the EEG signals is relatively preserved. These findings offer insights into the neurobiological mechanisms of ASD and suggest the potential use of Hjorth parameters in developing more targeted interventions for individuals with ASD.

关键词： Human computer interaction Autism Frontal lobe Noise Neural activity Electroencephalography Complexity theory Signal resolution Robots Optimization

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Analysis of the Brazilian Climate Data Influence on Soybean Pricing by Using Long Short-Term Memory Models

SSRN

引用

SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Dal Molin, Viviane Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical and Computer Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

This work uses machine learning to verify whether the Brazilian climate influences international soybean price prediction. For this, climatic data, dollar quotes, inflation, and annual soybean production were collected from different data sources. Then, the collected data were pre-processed and grouped by Brazilian state. Afterward, data from the five most productive Brazilian states are chosen, such as Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS). Afterward, predictive models are built using the long short-term memory (LSTM) method, which is applied in each chosen state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest values of root mean squared error (RMSE) and mean absolute error (MAE). Regarding predictors trained without climate data, the RS and PR obtained the lowest values of RMSE and MAE, respectively. After conducting statistical hypothesis tests, no significant difference was found among models, stating, with 95% certainty, that, in this study, the Brazilian climatic data did not influence the model performance for predicting soybean prices. On the other hand, one of the main contributions of work is conducting an unprecedented study about soybean price prediction using Brazilian climatic data. © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

引用

IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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A Machine Learning-Based Digital Twin Model for Pressure Prediction in the Fuel Injection System 48

A Machine Learning-Based Digital Twin Model for Pressure Pre...

引用

48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Duarte, Edwin P. Viegas, Eduardo K. Santin, Altair O. Graduate Program in Computer Science PPGIa Pontificia Universidade Catolica Do Parana-Pucpr Brazil Robert Bosch GmbH Engineering Systems Curitiba Brazil Secure Systems Research Center Abu Dhabi United Arab Emirates

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Over the last years, the engine calibration task has mostly been conducted based on the engineers' knowledge. As a result, considering the complexity of modern engines, finding the most suitable configuration for each situation has become an impractical and expensive task. Apart from causing engines to be produced with inadequate calibration configuration, it can also decrease the lifespan of their components, degrading their efficiency. This paper proposes a machine learning-based digital twin model for pressure prediction in a fuel injection system, split into two steps. First, we extract statistical engine features based on a predefined time window to represent the engine behavior over time. Second, a digital twin implemented through a machine learning model is used to predict pressure levels in the fuel injection system. As a result, the predicted values can be used to assist the engine common rail system module in avoiding undesired engine states. Experiments performed on a new dataset, built over a real diesel-based engine, consisting of 208 features and over 1.3 million instances, have shown the feasibility of our proposal. The proposed scheme can predict in an advance time of 0.1 seconds the pressure levels for a fuel injection system with only 0.057 RMSE. Moreover, it increases its error rate by only 10.6% if a 0.5-second time advance is required. © 2022 IEEE.

关键词： Machine learning

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The Michigan Robotics Undergraduate Curriculum: Defining the Discipline of Robotics for Equity and Excellence

arXiv

引用

arXiv 2023年

作者： Jenkins, Odest Chadwicke Grizzle, Jessy Atkins, Ella Stirling, Leia Rouse, Elliott Guzdial, Mark Provost, Damen Mann, Kimberly Millunchick, Joanna Robotics and Electrical Engineering & Computer Science United States Undergraduate Education Robotics Institute United States Robotics Institute United States Graduate Programs Robotics Institute United States Robotics and Industrial Engineering United States Robotics and Mechanical Engineering United States Electrical Engineering & Computer Science United States Engineering Education Research Program United States Undergraduate Education College of Engineering United States University of Michigan United States

The Robotics Major at the University of Michigan was successfully launched inthe 2022-23 academic year as an innovative step forward to better servestudents, our communities, and our society. Building on our guiding principleof "Robotics with Respect" and our larger Robotics Pathways model, the MichiganRobotics Major was designed to define robotics as a true academic disciplinewith both equity and excellence as our highest priorities. Understanding thattalent is equally distributed but opportunity is not, the Michigan RoboticsMajor has embraced an adaptable curriculum that is accessible through adiversity of student pathways and enables successful and sustained career-longparticipation in robotics, AI, and automation professions. The results afterour planning efforts (2019-22) and first academic year (2022-23) have beenhighly encouraging: more than 100 students declared Robotics as their major,completion of the Robotics major by our first two graduates, soaringenrollments in our Robotics classes, thriving partnerships with HistoricallyBlack Colleges and Universities. This document provides our original curricularproposal for the Robotics Undergraduate program at the University of Michigan,submitted to the Michigan Association of State Universities in April 2022 andapproved in June 2022. The dissemination of our program design is in the spiritof continued growth for higher education towards realizing equity andexcellence. The most recent version of this document is also available on Google Docsthrough this link: https://***/robotics_majorMSC Codes 97 © 2023, CC BY.

关键词： Curricula

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