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IEEE International Conference on Micro Electro Mechanical Systems

作者： Xiang Liu Simon Sanchez Yan Gong Zebin Jiang Trevor Stevens Hasan Banan Debajit Saha Wen Li Department of Physiology Neuroscience Program Michigan State University East Lansing MI USA Institute for Quantitative Health Science and Engineering East Lansing MI USA Department of Biomedical Engineering Michigan State University East Lansing MI USA Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

Despite advances in modern microelectrode technologies, designing high-density flexible microelectrode arrays (MEAs) for small insect brains remains challenging, especially when aiming for dense microelectrode configurations on flexible substrates in restricted areas (e.g., 250×100 μm 2 ). To overcome these challenges, we developed a novel dual-sided design that doubles the channel count and density of MEAs within the same spatial footprint in comparison with the single-sided counterparts. A unique folding-annealing method is introduced to craft Parylene-based flexible dual-sided density MEAs. These MEAs, with overall dimensions of 20 μm in thickness and 120-230 μm in width, are specifically tailored for the locust antenna lobe, which is the central odor processing circuitry in insects. The efficacy of the dual-sided MEA for neurophysiological sensing of odors was validated in vivo using a neural circuitry (antennal lobe) of the locust brain.

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Gramian Angular Field and Recurrence Plots as Feature engineering Techniques on Residential Appliances Labeling: A Comparative Analysis

Gramian Angular Field and Recurrence Plots as Feature Engine...

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IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America)

作者： J. S. Corrêa D. L. Cavalca R. A. S. Fernandes Department of Electrical Engineering Federal University of São Carlos São Carlos Brazil Graduate Program in Computer Science Federal University of São Carlos São Carlos Brazil

The demand for electricity has increased rapidly and, for this reason, there is a need to efficiently use it. In this way, the identification of residential appliances enables such use for consumers and is crucial for demand response programs. Due to the variety of appliances in homes and their dynamic behavior, the search for patterns that explain and allow the correct labeling of temporal windows becomes a challenging task, since a window may contain more than one appliance. In this sense, the present paper proposes the transformation of time-series into images, using Gramian angular field and recurrence plots. The dataset composed of images was submitted to the labeling process, considering the use of convolutional neural networks. A comparative analysis was performed using the UK-DALE dataset. The results demonstrated the effectiveness of the proposed feature engineering stage, since the labeling task reached F1-scores until 94 %.

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Performance Analysis of Interleaved Bidirectional DC-DC Converters for Battery Control in Islanded Microgrid

Performance Analysis of Interleaved Bidirectional DC-DC Conv...

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IEEE General Meeting Power& Energy Society

作者： Lindemberg Roberto de Lima Pedro A. de Alcântara Luana C. S. Soares Camila M. V. Barros Luciano S. Barros Mathematics and Computational Modeling Graduate Program João Pessoa Brazil Electrical Engineering Graduate Program Federal University of Paraíba João Pessoa Brazil Computer Systems Department Federal University of Paraíba João Pessoa Brazil

In this paper, considering photovoltaic/battery sourced microgrids with grid-forming droop control in the load interface converter for power management, two alternative converter topologies for the battery are tested: dual boost interleaved converter and three-phase interleaved converter. These topologies are based on the conventional bidirectional buckboost converter and their employment is to allow a reduction in component sizing, improvement in power quality, and higher output voltage. Analysis of the operation of these converters in different operation modes of the microgrid is carried out, including battery normal charging, battery normal discharging, battery charging limit, and maximum state-of-charge, through simulations in MATLAB/Simulink environment. Results suggest advantages such as reduced stress on switches and battery charging current ripple for the two interleaved converters tested and the potential implementation of higher output voltage for the dual boost converter.

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Implementation of Genetic Algorithm Optimization based Artificial Neural Network on Raspberry Pi for Fault Diagnosis

Implementation of Genetic Algorithm Optimization based Artif...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Amal Hichri Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.

关键词： Fault diagnosis Photovoltaic systems Embedded systems Accuracy Fault detection Decision making Artificial neural networks Hyperparameter optimization Transient analysis Genetic algorithms

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Fabric-substrated capacitive biopotential sensors enhanced by dielectric nanoparticles

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Nano Research 2021年第9期14卷 3248-3252页

作者： Xiangjun Chen Xiaoxiang Gao Akihiro Nomoto Keren Shi Muyang Lin Hongjie Hu Yue Gu Yangzhi Zhu Zhuohong Wu Xue Chen Xinyu Wang Baiyan Qi Sai Zhou Hong Ding Sheng Xu Materials Science and Engineering Program University of California San DiegoLa JollaCA 92093USA Department of NanoEngineering University of California San DiegoLa JollaCA 92093USA Department of Electrical and Computer Engineering University of California San DiegoLa JollaCA 92093USA Department of Bioengineering University of California San DiegoLa JollaCA 92093USA

Wearable biopotential sensing devices are essential to long-term and real-time monitoring of human ***-contact,capacitive sensing electrodes prevent potential skin iritations,and are thus beneficial for long-term *** capacitive electrodes are either connected to a separate control circuit via external wires or have limited sensing capacitances,which leads to low signal *** study demonstrates a stretchable capacitive sensing device with integrated electrodes and control electronics,with enhanced signal *** electrodes and the control electronics are fabricated on a common fabric substrate for breathability and strain-limiting *** stretchable electrodes are based on an island-bridge design with a stretchability as high as-100%,and an area ratio as high as-80%.By using a dielectric calcium copper titanate(CCTO)composite as the adhesive layer,the electrode capacitance can be increased,yielding an enhanced signal-to-noise ratio(SNR)in the acquired *** device offers a convenient and comfortable approach for long-term non-contact monitoring of biopotential signals.

关键词： capacitive sensing biopotential stretchable calcium copper titanate(CCTO) signal-to-noise ratio(SNR)

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Downstream Task Performance of Pre-Trained Model For Outdoor Audio Event Classification

Downstream Task Performance of Pre-Trained Model For Outdoor...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Theresia Herlina Rochadiani Yaya Heryadi Ilvico Sonata Lukas Wawan Suparta BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Our surroundings’ auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models—three, four, and five classes—with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively.

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CVO: Curriculum Vitae Optimization by Recommending Keywords to Undergraduate Students 11th

CVO: Curriculum Vitae Optimization by Recommending Keywords...

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11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022

作者： Santos, Cibele Góes, Fabrício Martins, Carlos da Cunha, Felipe Post-Graduation Program in Electrical Engineering Pontifícia Universidade Católica de Minas Gerais MG Belo Horizonte30535-901 Brazil Informatics Department University of Leicester Leicester United Kingdom Post-Graduation Program in Informatics Pontifícia Universidade Católica de Minas Gerais MG Belo Horizonte30535-901 Brazil Department of Computer Science Pontifícia Universidade Católica de Minas Gerais MG Belo Horizonte30535-901 Brazil

ISBN: (纸本)9783031336133

Candidate selection platforms have been widely used in companies that seek agility in the process of hiring. Candidates who do not meet the requirements of a job vacancy are disqualified in the first step, called screening. This stage has been automated due to the large volume of curriculum vitae (CV) of candidates per vacancy, particularly for internship vacancies. As a consequence, candidates receive little to none feedback and do not know how to improve/optimize their CVs for new applications. The goal of this paper is to realize the curriculum vitae optimization (CVO) process for internship vacancies by implementing a recommendation system that given an undergraduate student CV, it suggests the addition of relevant keywords, taking into account the student’s undergraduate course. This system is implemented based on the clustering of CVs keywords, from an internship recruitment private company database, into profile groups which are linked to internship vacancies. The experimental results showed that recommendations improved students CVs similarity (competitiveness within a specific field) from 18.83%, with 3 keywords recommendation, up to 50.67%, with 10 words. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Students

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DeepCat: A Deep Learning Approach to Understand Your Cat's Body Language

DeepCat: A Deep Learning Approach to Understand Your Cat's B...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Arwa Fawzy Ahmed Hassan Ahmed Gad Ahmed B. Zaky Haitham El-Hussieny Computer Science and Information Technology (CSIT) program Egypt-Japan University of Science and Technology Alexandria Egypt Department of Computer Science Engineering (CSE) Egypt-Japan University of Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Egypt Department of Mechatronics and Robotics Engineering Egypt-Japan University of Science and Technology Alexandria Egypt

Pet owners experience difficulty in understanding their pets' body language and its implications for animal welfare, given that animals cannot utilize human speech to communicate their emotions and health conditions. However, previous experiments for analyzing cat behavior have demonstrated that cats are precisely expressive. DeepCat, a deep-learning approach developed in this study, translates cats' body language signals, enabling owners to discern their feline companions' intended messages and emotional states. Our DeepCat model was trained on a dataset comprising 10,000 cat images, implementing automatic labeling to track key features, including the tail, eyes, and mouth. Presented as a Flutter application, DeepCat can function everywhere, allowing owners to easily monitor their cats and make informed decisions in situations that require caution. This paper discusses the potential benefits and limitations of DeepCat and provides suggestions for future research in this domain.

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Solar Charging Dock for Personal Electric Vehicles

Solar Charging Dock for Personal Electric Vehicles

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Zachary Bleam N. Durham Adel El-Shahat Hala M. Abdel Mageed Samu Blitch Zain Mirza Dept. of Electrical and Computer Engineering Georgia Southern University Statesboro GA USA Energy Program School of Engineering Technology Purdue University West Lafayette IN USA High Voltage Department National Institute of Standards (NIS) Giza Egypt

Electric Vehicles (EV s) such as e-bikes, e-scooters, and e-skateboards become the most popular Eco-friendly personal modes of transportation in the United States. These EVs are mostly recharged via the power grid's stations. Grid power is generated through multiple means including hydroelectric, thermal, solar, and wind. Even with using EVs to reduce the dangerous effects of fuel burning, there is a real need to take major steps towards green charging approaches. Charging EVs through renewable energy resources is maximizing the ecologically friendly potentials. The aim of this work is to provide a green and sustainable charging system for personal electric vehicles. Hence, a solar-powered charging dock has been built and controlled by an Arduino Mega 2560 adding to a Raspberry Pi 4. Subsequently, solar panel tracker linear actuator has been applied to be integrated with the installed dock solar panel tracking system for delivering maximum precision while operating. To get economic and reliable dock, it has been constructed from wood.

关键词： Renewable energy sources Wind Green products Transportation Wind power generation Electric vehicles Software

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Bambang Sulistio Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%.

关键词： Deep learning Sentiment analysis Analytical models Social networking (online) Blogs Decision making Text categorization Bidirectional control Transformers Encoding

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