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Multi-Scale Analysis Combined Operando Elemental/Spectroscopic Measurement Techniques in Oxide-Type All-Solid-State Na Batteries

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Energy & Environmental Materials 2025年第2期8卷 38-47页

作者： Koji Hiraoka Kazuo Yamamoto Takeshi Kobayashi Tetsuo Sakamoto Shiro Seki Graduate School of Engineering Applied Chemistry and Chemical Engineering Program Kogakuin University Nanostructures Research Laboratory Japan Fine Ceramics Center Energy Transformation Research Laboratory Central Research Institute of Electric Power Industry Graduate School of Engineering Electrical Engineering and Electronics Program Kogakuin University

Understanding the charge/discharge mechanism of batteries plays an important role in the development of high-performance systems,but extremely complicated reactions are *** these complex phenomena are also bottlenecks for the establishment of all-sol id-state batteries(ASSB),we conducted multi-scale analysis using combined multi-measurement techniques,to directly observe charge/discharge reactions at hierarchical scales for the oxide-type ASSB using Na as the carrier *** particular,all of measurement techniques are applied to cross-section ASSB in the same cell,to complementarily evaluate the elemental distributions and structural *** Operando scanning electron microscopy-energy-dispersive X-ray spectroscopy,the Na concentration in the electrode layers changes on the micrometer scale under charge/discharge reactions in the first ***,Operando Raman spectroscopy reveal changes in the bonding states at the atomic scale in the active material,including changes in reversible structural *** cycling the ASSB,the elemental distributions are clearly observed along with the particle shapes and can reveal the Na migration mechanism at the nanometer scale,by time-of-flight secondary ion mass ***,this study can provide a fundamental and comprehensive understanding of the charge/discharge mechanism by observing reaction processes at multiple scales.

关键词： Operando oxide-type all-solid-state battery Raman SEM-EDS sodium battery TOF-SIMS

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

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

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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VidyaMedic: Smart Alert System For Continous Vital Sign Monitoring

VidyaMedic: Smart Alert System For Continous Vital Sign Moni...

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2023 International Conference on electrical engineering and informatics, ICEEI 2023

作者： Ikawati, Vidya Prihatmanto, Ary Setijadi School of Electrical Engineering and Informatics Bandung Institute of Technology Doctoral Program of Electrical Engineering and Informatics Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350381290

The proposed smart system namely VidyaMedic utilizes continuous heart rate monitoring through sensors, with data transmission to the cloud via an IoT platform. Testing was conducted on a 40-year-old male participant who wore the Polar H10 heart rate monitor for 24 days. The heart rate profile captures the circadian rhythm, the natural internal biological clock governing cardiovascular and behavioral processes over a 24-hour cycle, influencing morning, daytime, and nighttime activities. Health conditions can be assessed using an algorithm which calculates the running sum of standard deviation for heart rate data over this period. Initially, standard deviation is computed for each data point, quantifying data dispersion from the mean. Subsequently, the running sum of standard deviation enables the detection of significant changes over time, abrupt fluctuations indicate outliers or extreme values, potentially impacting overall standard deviation and signaling significant data changes. Deviations from the expected or previous standard deviation values, marked by a regression coefficient less than or greater than 1, may suggest cardiac abnormalities or heart rhythm disturbances. © 2023 IEEE.

关键词： Heart

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Interfacial-Layer-Free Ge0.95Si0.05Nanosheet FeFETs

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IEEE Transactions on Electron Devices 2024年第3期71卷 1758-1763页

作者： Hsieh, Wan-Hsuan Chen, Yu-Rui Liu, Yi-Chun Zhao, Zefu Lee, Jia-Yang Tu, Chien-Te Huang, Bo-Wei Wang, Jer-Fu Lee, M.H. Liu, C.W. Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Institute and Undergraduate Program of Electro-Optical Engineering Graduate School of Advance Technology National Taiwan University Taipei10617 Taiwan National Taiwan University Graduate School of Advance Technology Taipei10617 Taiwan Graduate Institute of Electronics Engineering Graduate Institute of Photonics and Optoelectronics Graduate School of Advanced Technology National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan

The substantial memory window (MW) of 1.8 V is achieved by utilizing stacked two nanosheet (NS) gate-all-around (GAA) Ge0.95 Si0.05 ferroelectric field-effect transistors (FeFETs), employing a low write voltage of 2 V. The channel phosphorus concentration 1E18 cm-3 is used to erase the absence of interfacial layer (IL) can reduce the operation voltage. An isotropic wet etching process is employed for channel release. The stacked two NSs offer the advantages of reducing cell-to-cell variation and doubling the read current. Additionally, the stability of data storage is demonstrated with the data retention of >1E4 s, linearly extrapolated to a span of 10 years, and exhibiting high endurance >1E11 cycles. The thermal budget is as low as 400 °C. © 1963-2012 IEEE.

关键词： III-V semiconductors

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Enhancing dynamic ensemble selection: combining self-generating prototypes and meta-classifier for data classification

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Neural Computing and Applications 2024年第32期36卷 20295-20320页

作者： Manastarla, Alberto Silva, Leandro A. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil Computing and Informatics Faculty and Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (***) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of *** supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the *** framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that *** outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. © The Author(s), under exclusive licence to Springer-Ver

关键词： Nearest neighbor search

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Multi-Step-Ahead Prediction of User Movement on 5G Networks Using the Long Short-Term Memory Method 18

Multi-Step-Ahead Prediction of User Movement on 5G Networks ...

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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Timoteo, Adriel Benyamin, Fabian Rafinanda Anargyarahman, Naufal Bhanu Iskandar Yuliana, Hajiar School of Electrical Engineering and Informatics Telecommunications Engineering Program Bandung Institute of Technology Bandung Indonesia Jenderal Achmad Yani University Faculty of Engineering Department of Electrical Engineering Cimahi Indonesia

ISBN: (纸本)9798331527686

Since the initial launch of 5G networks in 2018, 5G technology has rapidly expanded to cover about 40% of the world's population, offering higher bandwidth, faster connectivity, and lower latency than 4G. However, due to its higher frequency operation, 5G necessitates more frequent handovers, making efficient handover performance crucial. This research focuses on enhancing handover efficiency by predicting user movement using Long Short-Term Memory (LSTM) models for multi-step-ahead prediction. The model created will utilize 60 seconds of historical data to predict 10 seconds ahead, with a target to achieve an average prediction accuracy of less than 20 meters with a latency under 100 ms. The study employs TensorFlow for model training, leveraging the Grab-Posisi dataset, and utilizes Plotly and TensorBoard for result visualization. Six model variations were trained, with the 16-unit model trained for 3106 epochs performing best, achieving 16.99 meters accuracy and 31 ms latency. The findings suggest that LSTM is effective for predicting user movement in 5G networks, with potential accuracy improvements through extended training epochs. © 2024 IEEE.

关键词： Prediction models

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Robot and Human Interaction using Natural Language Understanding in Indonesian based on Mental Image Based Understanding 11

Robot and Human Interaction using Natural Language Understan...

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11th International Conference on Information and Communication Technology, ICoICT 2023

作者： Abrari, Muhammad Giriarda Hindersah, Hilwadi Rusmin, Pranoto Hidaya Bandung Institute of Technology Master Program in Electrical Engineering Bandung Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350321982

Robots, in order to understand human commands, could be possible if there is an interaction between them. Possible interaction including the Natural Language, that humans have used to communicate with each other. The method used in this research for robots to understand Natural Language is Mental Image Directed Semantic Theory (MIDST) which uses unique Lmd formula as a basis for computing the Natural Language Understanding (NLU) processes. Equipped with support modules such as Speech to Text, Text to Speech, and Synonym Search Algorithm the robot successfully to receive, understand, and issue responses according to the Natural Language expressions given in Indonesian Language. The movement of the robot is also simulated in a two-dimensional plane to demonstrate its ability to serve humans. © 2023 IEEE.

关键词： Semantics

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EfficientSpeech: An On-Device Text to Speech Model 48

EfficientSpeech: An On-Device Text to Speech Model

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Atienza, Rowel University of the Philippines Electrical and Electronics Engineering Institute Ai Graduate Program Philippines

ISBN: (纸本)9781728163277

State of the art (SOTA) neural text to speech (TTS) models can generate natural-sounding synthetic voices. These models are characterized by large memory footprints and substantial number of operations due to the long-standing focus on speech quality with cloud inference in mind. Neural TTS models are generally not designed to perform standalone speech syntheses on resource-constrained and no Internet access edge devices. In this work, an efficient neural TTS called EfficientSpeech that synthesizes speech on an ARM CPU in real-time is proposed. EfficientSpeech uses a shallow non-autoregressive pyramid-structure transformer forming a U-Network. EfficientSpeech has 266k parameters and consumes 90 MFLOPS only or about 1% of the size and amount of computation in modern compact models such as Mixer-TTS. EfficientSpeech achieves an average mel generation real-time factor of 104.3 on an RPi4. Human evaluation shows only a slight degradation in audio quality as compared to FastSpeech2. © 2023 IEEE.

关键词： CMOS FLOPS parameters RTF TTS

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Security Authentication on the Edge: A Step Further to Defense in Depth for Internet of Battle Things 11

Security Authentication on the Edge: A Step Further to Defen...

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11th IEEE International Conference on Software Defined Systems, SDS 2024

作者： Lawisch, Marcio Antonio Dos Anjos, Julio Cesar S. De Freitas, Edison Pignaton Institute of Informatics RS Brazil Federal University of Ceara Campus Itapaje Graduate Program in Teleinformatics Engineering PPGETI CE Brazil

ISBN: (纸本)9798331518325

Currently, the demand for the security of network assets in companies grows dramatically because of several factors, including the growth of the attack surface combined with the variety of connection standards and correlated to the emerging Internet of Things (IoT) related devices and services. This reality is also evident regarding nodes connected to military networks, especially Tactical Edge (TE) devices. Due to the wide variety of equipment connected to the networks (from small sensors to powerful radars) and their possible limitations in resources and transmission means, mainly caused by the terrain and operational conditions, these networks called the Internet of Battle Things (IoBT), also face important security challenges. In this context full of many intentional and unintentional security challenges, this work proposes using blockchain technology as a network access security solution within an in-depth defense concept at three levels: hardware, network, and application. Tests for attacks against the network layer resulted in high resistance rates without consuming significant resources. © 2024 IEEE.

关键词： Blockchain

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Deep Convolutional Neural Networks for Accurate Solar Module Classification in Thermal Images 12

Deep Convolutional Neural Networks for Accurate Solar Module...

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12th International electrical engineering Congress, iEECON 2024

作者： Phungket, Chutinun Nangthin, Prasit Pathumwan Institute Of Technology The Graduate Program Faculty Of Engineering Faculty Of Engineering Bangkok Thailand Pathumwan Institute Of Technology Faculty Of Engineering Department Of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

The rapid and continuous technological advancements in computer and internet technologies, combined with data management techniques, find applications in various domains. For example, they aid in predictive maintenance systems, evaluate the performance of solar modules, and differentiate efficiently performing solar modules from less efficient ones using thermal imagery. The accuracy of solar module classification significantly impacts the subsequent assessment. In this study, we employ advanced techniques, particularly deep learning through Convolutional Neural Networks (CNN), to classify thermal images of solar modules. The practical aspects of applying CNN to the dataset of thermal images are explored, including transfer learning from pretrained CNN architectures and custom CNN architecture training. Experiments are conducted with three prominent types of solar module conditions: normal, cracked, and dusty. The results reveal that the most effective approach involves learning and fine-tuning pre-trained architectures on detailed studies, achieving a classification accuracy of up to 94.5% © 2024 IEEE.

关键词： Convolutional neural networks

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