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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(Science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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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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Frequency-Domain Modeling-Free Iterative Learning Control for Point-To-Point Motion 32

Frequency-Domain Modeling-Free Iterative Learning Control fo...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Maeda, Yoshihiro Iwasaki, Makoto Nagoya Institute of Technology Electrical and Mechanical Engineering Program Department of Engineering Nagoya Japan

ISBN: (纸本)9798350399714

The data-driven autonomous feedforward (FF) control design technique known as frequency-domain modelling-free iterative learning control (MFILC) has gained attention for its ability to achieve fine motion control performance without excessive labor. However, the existing frequency-domain MFILC methods that rely on empirical transfer function estimation are not suitable for point-to-point (PTP) motion due to the leakage error that occurs in frequency response function (FRF) estimation. To address this issue, this study proposes an improved frequency-domain MFILC method that employs the differential filtering-based empirical transfer function estimation for FRF estimation. This enhancement enables the proposed method to learn the FF compensation in both reciprocating and PTP motion. Simulations were conducted to evaluate the effectiveness of the proposed method in achieving fast and precise motion control of a galvano scanner. © 2023 IEEE.

关键词： Motion control

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High-Voltage Autonomous Current-Fed Push-Pull Converter with Wireless Communication Applied to X-Ray Generation

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IEEE Transactions on Circuits and Systems II: Express Briefs 2024年第2期71卷 902-906页

作者： Lin, Chang-Hua Ahmad, Javed Liu, Hwa-Dong Kao, Tzu-Hsien National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan National Taiwan Normal University Undergraduate Program of Vehicle and Energy Engineering Taipei106 Taiwan

This brief presents an adjustable 100 kV high voltage DC power supply with ZigBee wireless feedback for the X-ray system. The proposed system is powered by a 48 V DC power source, and a combination of an autonomous current-fed push-pull converter with a high voltage transformer is used to step up the output voltage. At the end of the secondary of the high voltage transformer, a combination of half-wave voltage multipliers is used to achieve desired output of DC high voltage. Two microcontrollers with wireless communication modules are used to control the output voltage. Moreover, the equivalent inductance of the high voltage transformer with an extra capacitor is utilized to form a parallel resonant circuit to lead the power switch of the autonomous current-fed push-pull converter can achieve zero voltage switching to reduce the switching loss. The mathematical model of the system is derived, and the complete design considerations are established. Finally, the feasibility of the proposed system is verified by the experimental results efficiency of the developed model reaches 95% under low power high voltage applications. © 2004-2012 IEEE.

关键词： Inductance

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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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EESRGAN: Efficient & Effective Super-Resolution Generative Adversarial Network

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IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India) 2024年第2期41卷 200-211页

作者： Tsai, An-Chao Tsou, Cheng-Han Wang, Jhing-Fa International Master Program of Information Technology and Application National Pingtung University Pingtung900 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

In Taiwan, traditional production equipment for the mainframe panels is imported from overseas, and the parameters are adjusted through the operation panel for automated manufacturing. However, these parameters are slightly different from those used in Taiwan, requiring manual recording of warning parameters to ensure quality control. Currently, the only way to capture the operation records of these machines is by manually recording the system panel information, which is a time-consuming and laborious process that is expensive for production line personnel. In this work, we use image recognition to capture and analyze data externally without damaging the machine. This research improves the ESRGAN network to restore the image details and textures of the mainframe panels. It also captures relevant data with Google OCR to convert panel images into parameter values. The captured results are combined with data analysis to provide more accurate standard mainframe panel information. Even under different interference source conditions, data extraction and analysis can still be carried out to output the text files, effectively assisting the production line in recording the machine panel parameters and reducing personnel’s workload. © 2024 IETE.

关键词： Extraction

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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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Convolutional Neural Network for Microhydro Power Forecasting

Convolutional Neural Network for Microhydro Power Forecastin...

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2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future, ICUE 2024

作者： Aprillia, Happy Utami, Amalia Rizqi Salam, Anjas Nur Jaya Marindra, Adi Mahmud Saputra, Riza Hadi Institut Teknologi Kalimantan Electrical Engineering Study Program Industrial Technology & Process Department Balikpapan Indonesia

ISBN: (纸本)9798331517076

The demand for electrical energy is always fluctuating, influenced by several factors such as household, industrial, commercial, and other public service electricity usage. Moreover, estimates of future electricity demand play a crucial role in the operation of the power system, ranging from power generation planning to power flow analysis and energy system operation costs. electrical power forecasting aims to prevent sudden decreases or increases in electricity demand. Moreover, in tropical countries, the wet season and rivers offer many opportunities for micro-hydro potentials that can be harnessed in combination with solar energy. To manage the forecasting modeling, Convolutional Neural Network (CNN) is utilized CNN for short-term forecasting in Microhydro Power Plants. The anticipated outcomes aim to contribute to effective electricity demand management, facilitating the estimates of variation in microhydro power generation. © 2024 IEEE.

关键词： Demand response

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