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Electrics and computer (INTCEC), Interdisciplinary Conference on

作者： Ezequiel Muxito Cristiano Akamine Postgraduate Program in Electrical and Computer Engineering Mackenzie Presbyterian University Sao Paulo Brazil

ISBN: (数字)9798350349450

ISBN: (纸本)9798350349467

The integration of technology into the healthcare system is emerging as a tool to support professionals in the treatment of Heart Failure (HF), and is based on an analysis of the scientific and technological progress of the Internet of Things, applied as an innovative perspective in Health 4.0. This article presents a study that proposes to develop an innovative prototype Left Ventricular Assist Device (artificial heart). The main focus is on advanced sensing, connectivity via mobile network and remote supervision support, aimed at the operation and diagnosis of patients affected by HF. The project incorporates advanced data collection, processing, and analysis tools using Big Data and Data Analytics concepts. The motivation for this work is the search for technological innovation as support for medical science and the contribution to the Unified Health System. The prototype includes a microcontroller, a heartbeat sensor, an accelerometer sensor to collect movement, acceleration and fall detection data, a mobile network module to transmit the data and create an electronic medical record. This article highlights the initial phase, which includes preliminary results such as configuring the module and components, collecting data using heartbeat and movement sensors, storing the data in MongoDB and visualizing it in Jupyter Notebook.

关键词： Artificial heart Technological innovation Heart beat Prototypes Sensors Cardiovascular diseases Public healthcare

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Electronic Control System for the Left Ventricular Assistance Device

Electronic Control System for the Left Ventricular Assistanc...

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Electrics and computer (INTCEC), Interdisciplinary Conference on

作者： Mohd Hasan Cristiano Akamine Postgraduate Program in Electrical and Computer Engineering Mackenzie Presbyterian University Sao Paulo Brazil

ISBN: (数字)9798350349450

ISBN: (纸本)9798350349467

The number of heart transplants is constantly increasing. Each year, it grows by around 60% due to the health problems plaguing the world's population. Furthermore, the lack of available transplanted organs and the waiting list greatly increases and can last for years. All these reasons make it increasingly difficult to get a heart transplant. Solutions such as artificial organs are gaining more and more prominence and are in great demand. Considering the advancement of science and technology, new research shows great hope in prolonging and improving the quality of life for many patients. Among these researches is the Left Ventricular Assist Device (LVAD), which uses auxiliary pumps along with the natural heart of the person, allowing a longer waiting time in the transplant queue and giving a quality of life. This work aims to implement autonomous electronic control over this device, allowing autonomous speed control over the pump motor. This method gives patients better control and greater freedom to practice their daily lives. Another important reason is the economic factor, which aims to reduce costs and allow greater popular reach. In this work, the tests were carried out on only the Brushless Direct Current Motor (BLDC). The results are demonstrated in numerical and graphic form for better illustration.

关键词： Graphics Costs Brushless DC motors Velocity control Sociology Heart transplantation Motors

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Statistical Device Simulation and Machine Learning of Process Variation Effects of Vertically Stacked Gate-All-Around Si Nanosheet CFETs

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IEEE Transactions on Nanotechnology 2024年 23卷 386-392页

作者： Kola, Sekhar Reddy Li, Yiming Butola, Rajat National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

In this study, we report the process variation effect (PVE) including the work function fluctuation (WKF) on the DC/AC characteristic fluctuation of stacked gate-all-around silicon complementary field-effect transistors (CFETs). The PVE affects characteristic fluctuation significantly;in particular, for the variability of off-state current. Owing to the bottom channel of a fin-type, the P-FET suffers from the worst off-state current fluctuation (more than 200% variation) compared to the N-FET. The device variability induced by the WKF is marginal because of amorphous-type metal grains. As input features to an artificial neural network (ANN) model, low and high work function values, as well as parameters of PVE that have prevalent effects on CEFT transfer characteristics are further considered and modeled. The estimated values of R2-score prove that the ANN model properly grasps information from the dataset successfully;thus, it can be used to model emerging CFETs for circuit simulation. © 2002-2012 IEEE.

关键词： Neural networks

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Residential Power Satisfaction as a Demand Side Management Approach in Puerto Rico

Residential Power Satisfaction as a Demand Side Management A...

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IEEE Central America and Panama Convention (CONCAPAN)

作者： Carlos A. Peña Becerra Willian Pacheco Cano Marcel Castro Sitiriche Samira Ortiz Department of Electrical and Computer Engineering University of Puerto Rico Mayaguez PR Electrical and Computer Engineering Program Universidad Ana G. Méndez – Recinto de Gurabo

ISBN: (数字)9798350366723

ISBN: (纸本)9798350366730

Puerto Rico's electrical infrastructure powers over three million residents, yet energy consumption overcomes generation and continues to rise. This imbalance results in an unreliable power supply and frequent blackouts across the island. One solution to this issue is to increase generation capacity by adding new generating units and enhancing transmission networks, though this is expensive. Another approach is to promote the adoption of rooftop solar generation with storage systems through microgrids. Alternatively, demand-side management (DSM) offers a more cost-effective solution by regulating demand and reducing energy use at the consumer level. This paper explores the unique integration of power satisfaction within DSM strategies to reduce energy consumption while preserving user satisfaction. Utilizing an agent-based model to simulate household appliance behavior, we demonstrate how power satisfaction can reduce energy demand. This innovative approach offers a promising pathway toward a more resilient and sustainable energy future for Puerto Rico.

关键词： Energy consumption Renewable energy sources Demand side management Power system stability Agent-based modeling Stability analysis Solar power generation Security Sustainable development Resilience

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty 6

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Andriadi, Kus Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Yogyakarta55281 Indonesia

ISBN: (纸本)9798350358346

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large. © 2023 IEEE.

关键词： Generative adversarial networks

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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Ultra-Thin CMOS-Compatible Artificial Magnetic Conductor for Millimeter-Wave Antenna-on-Chip

Ultra-Thin CMOS-Compatible Artificial Magnetic Conductor for...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Yiyang Yu Atif Shamim Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal KSA

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

Antenna-on-chip (AoC) is one of the promising solutions in the 6G era. However, it suffers from poor radiation due to the lossy Si substrate. Placing an artificial magnetic conductor (AMC) under the AoC cannot only isolate the AoC from the lossy Si substrate but also provide in-phase reflection. Nonetheless, the ultra-thin stack-up of the mainstream semiconductor technology makes the realization of AMC challenging at the millimeter-wave spectrum. To make the AMC electrically thicker, this paper presents four kinds of thickness reduction techniques (TRT): slot, notch, metallic post, and embedded guiding structure. With TRTs, an ultra-thin AMC ( $15\ \mu \mathrm{m}$ thickness) is realized. With the proposed AMC, the AoC demonstrated a gain of 3.2 dB at 72 GHz. Compared to the standalone antenna, the AMC enhances the gain by 7 dB.

关键词： 6G mobile communication Conferences Millimeter wave technology Conductors Silicon Reflection Gain

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Impact Analysis of Time-of-Use pricing enabled Electric Vehicle charging to the uncoordinated charging on a Distribution Network

Impact Analysis of Time-of-Use pricing enabled Electric Vehi...

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IEEE General Meeting Power& Energy Society

作者： Muhammad Abeer Sohail Rashna Khan Syed Hammad Mukhtar Ahmad Usman Electrical and Computer Engineering Program Habib University Karachi Pakistan

Increased adoption of Electric Vehicles (EVs) result in appearance of high energy demand clusters in a distribution network. This has adverse effects on the distribution network resulting in voltage drop, increased losses, overloading of distribution feeders, transformers, and critical components. This results in degradation of power quality and network stability. In this work, a modified IEEE 13 bus system has been used to model and simulate 11 kV distribution grid using the actual network parameters, network topology, and an identified cluster region, based on the information from a distribution company in Pakistan. A scheduling strategy for EV charging using the the time-of-use pricing (ToUP) mechanism is devised and compared to the uncoordinated EV charging approach. The performance evaluation of these strategies for various degrees of EV penetration has been conducted and new challenges are identified.

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Performance Evaluation of Deep Feature in Cross Spectral Face Recognition

Performance Evaluation of Deep Feature in Cross Spectral Fac...

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International Conference on Information Technology, computer and electrical engineering (ICITACEE)

作者： Ernita Dewi Meutia Fitri Arnia Nasaruddin Rusdha Muharar Maulisa Oktiana Khairun Saddami Department of Electrical and Computer Engineering Doctoral Program School of Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Cross-spectral face recognition (CSFR), particularly in the Near Infra-Red (NIR) and visual (VIS) spectrum, presents challenges due to differences in illumination and sensor characteristics. NIR sensors are commonly used in practical applications, such as in nighttime outdoor surveillance systems, due to their ability to capture images in low light conditions and at various standoff distances. This study proposes a simple CSFR method that combines deep learning techniques with photometric normalization using Homomorphic Filtering (HF) to identify subjects in NIR images across different standoff distances. Both NIR and VIS images are filtered to mitigate illumination discrepancies. A deep convolutional neural network (CNN) model is employed to automatically extract discriminative deep features from the normalized images of both spectrums. Four deep CNN models of ResNet-50, DenseNet, EfficientNet, and MobileNetV2 are tested. The performance of the models in feature extraction for CSFR is evaluated on the LDHF (Long Distance Heterogeneous Face) database. The Hamming distance metric is employed to match NIR probe images with VIS gallery images. The experimental findings, expressed in recognition rate RR, demonstrate the effectiveness of the approach in enhancing CSFR accuracy, with MobileNetV2 achieving the highest accuracy of 90% for a short standoff. In addition, HF improves image quality, enhancing the deep learning model's ability to discern image characteristics.

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ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PERSPECTIVE OF DISENTANGLED FACTORS 12

ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Jonghyun Jung, Dahuin Lee, Saehyung Park, Junsung Shin, Juhyeon Hwang, Uiwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Division of Digital Healthcare Yonsei University Korea Republic of AIIS ASRI INMC ISRC Seoul National University Korea Republic of

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error. Through experimental studies, however, we observed the unreliability of entropy as a confidence metric for TTA under biased scenarios and theoretically revealed that it stems from the neglect of the influence of latent disentangled factors of data on predictions. Building upon these findings, we introduce a novel TTA method named Destroy Your Object (DeYO), which leverages a newly proposed confidence metric named Pseudo-Label Probability Difference (PLPD). PLPD quantifies the influence of the shape of an object on prediction by measuring the difference between predictions before and after applying an object-destructive transformation. DeYO consists of sample selection and sample weighting, which employ entropy and PLPD concurrently. For robust adaptation, DeYO prioritizes samples that dominantly incorporate shape information when making predictions. Our extensive experiments demonstrate the consistent superiority of DeYO over baseline methods across various scenarios, including biased and wild. Project page is publicly available at https://***/DeYO/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Entropy

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