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Power, Energy and Electrical engineering (CPEEE), International Conference on

作者： Gyu Yeon Kim Danyao Peng Sangyub Lee Seon Han Choi Graduate Program in Smart Factory Ewha Womans University Seoul South Korea Department of Electronic and Electrical Engineering Ewha Womans University Seoul South Korea Energy IT Convergence Research Center Korea Electronics Technology Institute Seongnam South Korea

ISBN: (数字)9798331520090

ISBN: (纸本)9798331520106

The high penetration of renewable energy sources poses a challenge to stability of power systems, particularly microgrids, which are more vulnerable due to their small capacity and low inertia. To address instability, system components and their interactions need to be represented to more efficiently understand and optimize complex systems. Agent-based modeling enables autonomous operation and collaborative interaction, making the system adaptable to changes efficiently, making it ideal for microgrid management. In this paper, an agent-based microgrid simulation model is proposed. By independently representing microgrid components such as generators, energy storage systems (ESS), loads, environments and controllers, models can dynamically simulate the behavior of power systems, respond to system changes in real time. To validate the proposed model, experiments are conducted based on IEEE 13 bus system, and the results show that with the use of ESS, the frequency peak is reduced from over 62 Hz to around 60 Hz. In the case of sudden demand fluctuations, the frequency remains relatively stable, staying close to 60 Hz. The model demonstrates flexibility and robustness in managing renewable energy fluctuations and sudden load changes.

关键词： Renewable energy sources Adaptation models Fluctuations Microgrids Power system stability Stability analysis Real-time systems Smart grids Thermal stability Load modeling

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ICU Bed Capacity Analysis with Transformer-Based Length of Stay Prediction and Erlang Loss Formula

ICU Bed Capacity Analysis with Transformer-Based Length of S...

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International Conference on Electrical engineering/electronics, Computer, Telecommunications and Information technology, ECTI-CON

作者： Sahawat Tanutsiriteeradet Wuttipong Kumwiaisak Jisue Youn Kumwilaisak Ratchapon Thammacharo Kanya Kumwilaisak Phornlert Chatkaew Electronics and Telecommunication Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Gifted Math Program Triam Udom Suksa School Bangkok Thailand Gifted Math Program Mahidol Wittayanusorn School Nakorn Pathom Thailand Department of Anesthesiology King Chulalongkorn Memorial Hospital Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

This paper introduces a new method for predicting the length of stay in the ICU using a transformer-based architecture. The predicted results are used for analyzing ICU bed capacity with the Erlang loss formula. The prediction architecture comprises two transformer layers, one global average pooling layer, and two fully connected layers. The proposed model classifies the length of stay into four classes depending on how long patients are admitted to the ICU based on patient data. In addition, the model estimates the length of stay in days of each patient. We then utilize the estimated length of stay to analyze ICU resources with the Erlang loss model. Given ICU blocking probabilities, the required ICU bed numbers are estimated with Erlang-B. Patient data from the MIMIC-III dataset, including various vital signs and patient health conditions, and the actual statistics from the Pediatric ICU at King Chulalongkorn Memorial Hospital are deployed to evaluate the proposed method. Experimental results show that the transformed-based architecture surpasses previous works regarding length of stay prediction accuracy. In addition, our analysis of ICU resources provides close results to those from the actual statistics from the Pediatric ICU at King Chulalongkorn Memorial Hospital.

关键词： Analytical models Accuracy Hospitals Computational modeling MIMICs Predictive models Probability

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Ravindra Pandey Aruna Sri Rongali S. Gopinath Nilesh Anute Forda Nama Sachin Pund Department - Management Hult International Business School Doctoral Information Technology/Information Technology Emphasis/Information Technology University of The Cumberlands US Department of Electronics And Communication Engineering Karpagam Institute of Technology Coimbatore Management ASM's Institute of Business Management and Research Pune Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets.

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End-to-End Reinforcement Learning for Torque Based Variable Height Hopping

End-to-End Reinforcement Learning for Torque Based Variable ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Raghav Soni Daniel Harnack Hauke Isermann Sotaro Fushimi Shivesh Kumar Frank Kirchner Department of Electronics Engineering Indian Institute of Technology (Banaras Hindu University) Varanasi India DFKI GmbH Robotic-s Innovation Center Bremen Germany Undergraduate Course Program of Mechanical and Systems Engineering Kyoto University Kyoto Japan

Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has recently yielded great advances, both in the optimal control and reinforcement learning (RL) literature. Hopping is a challenging dynamic task involving a flight phase and has the potential to increase the traversability of legged robots. Model based control for hopping typically relies on accurate detection of different jump phases, such as lift-off or touch down, and using different controllers for each phase. In this paper, we present a end-to-end RL based torque controller that learns to implicitly detect the relevant jump phases, removing the need to provide manual heuristics for state detection. We also extend a method for simulation to reality transfer of the learned controller to contact rich dynamic tasks, resulting in successful deployment on the robot after training without parameter tuning.

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Impact of High-k Materials and Tube Numbers on CNFET Gate’s Performances

Impact of High-k Materials and Tube Numbers on CNFET Gate’s...

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International Conference on Smart Systems and Inventive technology (ICSSIT)

作者： Christofer Yalung Doldet Tantraviwat Sermsak Uatrongjit Wittawat Yamwong Philosophy Program in Nanoscience and Nanotechnology (International Program/Interdisciplinary) Chiang Mai University Chiang Mai Thailand Department of Electrical Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Thai Microelectronics Center (TMEC) National Electronics and Computer Technology Center (NECTEC) Chachoengsao Thailand

Carbon nanotube field effect transistor (CNFET) performance is primarily influenced by a variety of characteristics, such as nanotube diameter, operating voltage, pitch, the number of tubes, dielectric constant, and contact materials. In this study, the dielectric constant and the number of carbon nanotubes (CNTs) were emphasized to analyze the propagation delay and average power dissipation of basic logic gates. Zirconium dioxide (ZrO 2 ), hafnium oxide (HfO 2 ), and silicon dioxide (SiO 2 ) were used as dielectric materials. In this experiment, the Stanford CNFET model was utilized, and variable factors such as the number of CNTs and the dielectric constant $(K_{ox})$ accumulated on the nanotube gate's surface were diversified. The correlation coefficients of the delay, the average power, and the CNT numbers were also presented. Furthermore, the multi-objective genetic algorithm and the Rmethod were utilized to find the optimal K ox and the number of CNTs for minimizing the average dissipation power and the propagation delay of some logic gates designed with CNFET. For all the gates considered, the optimal number of CNTs is 4 while the dielectric constant is within the ranges of 19 to 24.

关键词： Silicon compounds Dielectric constant Correlation Logic gates CNTFETs Power dissipation Electron tubes

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Analysis of Mumbai Floods in recent Years with Crowdsourced Data

arXiv

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arXiv 2023年

作者： Tripathy, Shrabani Sailaja Chaudhuri, Sautrik Murtugudde, Raghu Mharte, Vedant Parmar, Dulari Pinto, Manasi Zope, P.E. Dixit, Vishal Ghosh, Subimal Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Powai Mumbai400076 India Department of Civil Engineering Indian Institute of Technology Bombay Powai Mumbai400076 India Electronics and Telecommunication Engineering Vidyalankar Institute of Technology Mumbai400 037 India Kharghar Navi Mumbai410201 India

Mumbai, a densely populated city, experiences frequent extreme rainfall events leading to floods and waterlogging. However, the lack of real-time flood monitoring and detailed past flooding data limits the scientific analysis to extreme rainfall assessment. To address this, we explore the usability of crowdsourced data for identifying flood hotspots and extracting reliable flood information from the past. Through an automated program, we filter and retrieve flood-related data from Twitter, using location information to generate flood maps for past heavy rainfall events. The validity of the retrieved data is confirmed by comparing it with volunteered geographic information (VGI) which is more accurate but less abundant. In the absence of direct flood information, Twitter data is cross-verified with the Height above the Nearest Drainage (HAND) map, which serves as a proxy for elevation. Interestingly, while extreme rainfall events are increasing in frequency, recent Twitter-based information shows a decrease in flood reporting, attributed to effective mitigation measures implemented at various flood hotspots. Local surveys support this finding and highlight measures such as underground storage tanks and pumping stations that have reduced flood severity. Our study demonstrates the value of crowdsourced data in identifying urban flood hotspots and its potential for real-time flood monitoring and forecasting. This approach can be adapted for data-sparse urban regions to generate location-specific warnings, contributing to improved early warnings and mitigating the impact on lives and property. © 2023, CC BY.

关键词： Floods

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Modified Turnstile Antenna Design for Multi-Band and Broadband Applications

Modified Turnstile Antenna Design for Multi-Band and Broadba...

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USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)

作者： I-Fong Chen Chia-Mei Fei-Lung Wu Jian-An Xie Dept. of Computer and Communication School of Electronics and Electrical Engineering Dongguan Polytechnic Jinwen University of Science and Technology New Taipei City Taiwan Department of Communications Engineering Feng Chia University Taichung City Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung City Taiwan

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

The proposed antenna with a size of 90 mm x 90 mm, and the ground portion is 64 mm x 64 mm, which material is FR4 glass epoxy substrate with the thickness of 1.6 mm, relative permittivity of 4.3 and loss tangent of 0.023, consists of four modified dipole elements and four power dividers, as shown in Figure. 1. For the multi-band and broadband operation, three dipole antennas were shunted to be a dipole element. The electrical-length of the dipole element can be determined from the one quarter-wave length at the 2.45 GHz, 5.5 GHz and 6.525 GHz, for covering 2400 MHz-2500 MHz, 5150 MHz-5850 MHz and 5925 MHz-7125 MHz. In order to operates in various modes such as normal mode and axial mode, the modified four port triple-band microstrip series power divider was designed. In normal mode, it radiates horizontally polarized waves where as in axial mode, it radiates circularly polarized waves. The radiated patterns of the proposed antenna as shown in Figure. 2. The features of the proposed antenna are shown in the Table I. Impressive radiated gains and efficiencies are obtained.

关键词： Power dividers Dipole antennas Conferences Glass Broadband communication Broadband antennas Microstrip

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Role of Wireless Communications in Digital Economy in the Present Context 6

Role of Wireless Communications in Digital Economy in the Pr...

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6th International Conference on Trends in electronics and Informatics, ICOEI 2022

作者： Revathi, S. Shrivastava, Arpan Yussupova, Ardak Hidayatulloh, A. Nururrochman Saltanat, Daurbekova Mishra, Arpana Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India Management Devi Ahilya Vishva Vidyalaya Madhya Pradesh India Kazakh National University of Arts Department of Art Studies Nur-Sultan Kazakhstan Universitas Gadjah Mada Leadership and Policy Innovation Program Indonesia International Education Corporation Department of Construction Almaty050042 Kazakhstan Sharda University Electrical Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9781665483285

Wireless communication has connected millions of people across the world to internet to enable them to reap benefits of digital economy in present scenario. Almost all the sectors of economy including healthcare, agriculture and transportation nowadays rely largely upon wireless communications. In the present context, the sudden outbreak of Covid-19 has caused governments to impose restrictions on various activities and employees have been directed to work from their homes to ensure safety. In such a situation, wireless communication has made it possible to connect with each other via the internet. Apart from this, Smart healthcare-system has been developed through utilisation of wireless communication systems. During this pandemic situation, delivery of healthcare services in remote areas has gained huge momentum and wireless communication plays a vital role in this regard. Aim of current critical assessment is to provide an in-depth analysis of the role played by wireless communication in establishment of a well-structured digital economy in present scenario. In literature-review, different applications of wireless technology via ICT in various sectors and aspects of life have been discussed through a conceptual and a theoretical framework. The Method section of current study has provided an in-depth insight into the ways required information have been collected in this study. As found in the results and discussion section, a positive relation exists between wireless-communication and establishment of digital economy in current situation. This study has concluded that there are certain challenges associated with wireless communication;however, it has significant influence in making processes smarter and faster as compared to earlier. © 2022 IEEE.

关键词： Internet of things

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Microstructuring of cobalt (Co) thin films by UV laser irradiation

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Applied Physics A: Materials Science and Processing 2023年第12期129卷 864-864页

作者： Itapu, Srikanth Borra, Vamsi Selvendran, S. Department of Electronics and Communication Engineering Alliance University Chandapura-Anekal Road Bengaluru562106 India Electrical and Computer Engineering Program Rayen College of Engineering Youngstown State University 1 University Plaza YoungstownOH44555 United States Vellore Institute of Technology Chennai Kelambakam-Vandaloor Road Chennai600127 India

This work reports the microstructuring of cobalt (Co) thin films by high-energy UV pulsed laser irradiation. The parameters responsible for the efficient formation of such microstructures include laser fluence and film parameters. Cracks and island formations were observed for Co thin films deposited by RF sputtering. Such rough films rendered to produce inefficient microstructuring. To overcome this limitation, Co thin films were deposited by vacuum evaporation resulting in a smooth thin film compatible for laser-induced microstructuring. Two stages of conical patterning are discussed in terms of localized melting and abrupt condensation of the melted Co film owing to laser irradiation. An efficiency of 90% for laser-based microstructuring suggests that it is an efficient and non-invasive technique for fabricating nanostructures. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Vacuum evaporation

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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