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1st IEEE International Power Electronics and Application Symposium, PEAS 2021

作者： Zhao, Shanshan Cui, Shenghui Hu, Jingxin Chen, Yu Huazhong University of Science And Technology School of Electrical And Electronic Engineering Wuhan China Seoul National University Department of Electrical And Computer Engineering Seoul Korea Republic of RWTH Aachen University Faculty of Electrical Engineering And Information Technology Aachen Germany

ISBN: (纸本)9781665413596

The modular multilevel DC-DC converter (MMDC) is a promising solution for the interconnection of MVDC distribution systems with LVDC ports. Due to the symmetric structure and operation scheme, the MMDC is normally only capable of operating with monopole MVDC distribution systems. This paper proposes a bipolar operation scheme of the MMDC. Based on the concept of the flux dc-bias cancellation, a center-tapped high-frequency interface transformer is employed on the MV side of the MMDC. A dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be manipulated independently with a simple control scheme. Compared to the conventional MMDC with monopole operation, no additional component is required and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer. Moreover, a single-pole operation is also possible with the proposed operation scheme. If the valves of one pole are faulty, it can be isolated from the main converter for maintenance while the MMDC can continue operation without interruption and deliver at least 50% of the rated power capacity. Hence, the reliability and availability of the power delivery can be significantly enhanced. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype. © 2021 IEEE.

关键词： Poles

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Increasing resilience of an embedded design

Increasing resilience of an embedded design

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2021 Selected Issues of electrical engineering and Electronics, WZEE 2021

作者： Dvorsky, Petr Fiedler, Petr Brno University of Technology Faculty of Electrical Engineering and Communication Department of Control and Instrumentation Brno61600 Czech Republic

ISBN: (纸本)9781665421645

This paper aims to bring a different view and approach to embedded device design. The main focus of this article is to enumerate the key components of a resilient design, define them and try to apply them to a real-life scenario. This can show possible approaches and rules which can make the overall design of a device or a systen more resilient to various adverse conditions and situations. © 2021 IEEE.

关键词： electrical engineering Resilience

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Analysis of Power Transformer Inrush Current in the Presence of Quasi-Direct Currents

Analysis of Power Transformer Inrush Current in the Presence...

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International Conference on Environment and electrical engineering (EEEIC)

作者： Behzad Behdani Reza Shariatinasab Mousa Afrasiabi Jamshid Aghaei Department of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical and Computer Engineering Department University of Birjand Birjand Iran Department of Electrical Engineering School of Energy Systems Lappeenranta University of Technology Lappeenranta Finland

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

The flow of quasi-direct currents (QDCs) in AC electrical networks, is a disturbing factor that mainly prevails upon mutual impacts between different system components or due to geophysical phenomena. These QDCs can alter the normal behavior of the system components, e.g., power transformer inrush currents. In this paper, an analysis of the inrush current phenomenon in power transformers under the influence of QDCs has been performed. The effect of QDCs on power transformer inrush currents is first mathematically analyzed, and then investigated by computer simulations in EMTP-RV software. Results show that power transformer inrush currents can severely increase in the presence of QDCs.

关键词： Resistors Mathematical analysis Switches Transformer cores Behavioral sciences Steady-state Power transformers

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MIMO-NOMA with OSTBC for B5G Cellular Networks with Enhanced Quality of Service

MIMO-NOMA with OSTBC for B5G Cellular Networks with Enhanced...

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International Conference on Wireless Networks and Mobile Communications (WINCOM)

作者： Haleema Sadia Hafsa Iqbal Ridha Alaa Ahmed Faculty of Electrical Engineering Ghulam Ishaq Khan Institute of Engineering and Technology Topi Pakistan School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan Department of Electronics and Communication Engineering University of Kufa Najaf Iraq

Non-orthogonal multiple access (NOMA) scheme has proved a potential candidate to meet the design challenges of orthogonal multiple access (OMA) schemes. High spectrum efficiency and higher data rates are further achieved by integrating NOMA with multiple input multiple output (MIMO) technology. In this article, we have explored orthogonal space-time block coding (OSTBC) to improve the quality of service (QoS) for the proposed MIMO-NOMA framework. We specifically employed the Alamouti STBC scheme, which provides full diversity for the proposed network. At the transmitter side, OSTBC and power domain multiplexing with superposition coding are utilized, while at the reception side, a low complexity maximum likelihood (ML) detector is used, followed by successive interference cancellation (SIC). Based on bit error rate (BER) evaluations, Monte Carlo simulations are performed to analyze the performance of OSTBC MIMO-NOMA system under various fading channels. The proposed framework is further investigated with a higher modulation scheme and different channel code rates. The investigated Alamouti scheme provides a maximum diversity gain of 2M (M shows the number of receiving antennas) without any need for channel state information at the transmitter end and a less complex ML detector at the receiver end. This research lays the groundwork for designing adaptive modulation and coding methods for future MIMO-NOMA based B5G wireless communication systems.

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Redefining the Classification of Extravasation Severity Using CLIP Linear Probe with Few-shot Instances

Redefining the Classification of Extravasation Severity Usin...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： A. Munthuli M. Thepsoparn P. Pooprasert P. Phienphanich P. Pongcharoen D. Sakonlaya P. Sittiwattanawong S. Weschawalit P. Chakkavittumrong B. Thongthawee T. Pathomjaruwat C. Tantibundhit Department of Electrical and Computer Engineering Faculty of Engineering Medical Invention and Innovation Laboratory (MeDii) Thammasat School of Engineering Thammasat University Thailand MeDii and Bangkok Patana School Thailand Faculty of Medicine Thammasat University Thailand Faculty of Nursing Thammasat University Thailand

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study addresses extravasation, a critical issue requiring prompt detection for effective management to avoid severe complications. This work harnesses the capability of zero-shot capabilities from pre-trained vision transformer models–specifically, GroundingDINO and Segment Anything Model (SAM) for segment human skin regions, and Contrastive Language-Image Pretraining (CLIP) for extracting rich features from these regions using its frozen image encoder. Our methodology applies linear probe techniques to feature vectors obtained from CLIP using few-shot instances. The results demonstrate accurate classification of extravasation severities with training using only 64 instances per class, achieving average F1 macro scores of 74.08% for GroundingDINO-CLIP. This marks an improvement result to the previous study which utilized dual U-Nets for skin and lesion segmentation alongside DenseNet-121 models for classification with training using 975 instances. Notably, this approach increases previous F1 macro scores by 3.27% in mild extravasation cases. This research advances fine-grained extravasation classification, specifically in early detection, achieved through few-shot models in a unique context.

关键词： Training Image segmentation Feature extraction Transformers Skin Vectors Lesions Probes engineering in medicine and biology Context modeling

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Study on the Detection of Object Buried under Railway by using Clutter Removal Technique

Study on the Detection of Object Buried under Railway by usi...

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2021 Research, Invention, and Innovation Congress: Innovation electricals and Electronics, RI2C 2021

作者： Chomdee, Pongsathorn Boonpoonga, Akkarat Lertwiriyaprapa, Titipong Navamindradhiraj University Urban Community Development College Department of Technology Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665403009

This paper presents the study on the ground penetrating radar (GPR) based-detection of an object buried under the railway. The object is presented as an improvised explosive device (IED) often occurred in the south Thailand. A clutter removal technique is applied to the GPR system in order to mitigate the effect of the clutter due to the rocks resulting to the inhomogeneous ground under the railway. Several simulations were conducted by using gprMax in order to investigate the performance of the GPR based-detection in term of the B-scan image. Simulations were setup as practical situations of the terrorism burying the IED under the railway. In simulations, the inhomogeneous ground under the railway was modelled by using a method of random sequential adsorption (RSA). Simulations were divided into several categories i.e. cases with and without the railway, non-homogeneous soil case, and homogeneous soil case. The simulations results show that the railway and non-homogeneous soil directly affect to degradation of the parabolic curve in the B-scan GPR image. The clutter removal was performed by subtracting the results of simulations with railway and non-homogeneous soil from those of simulations without railway and homogeneous soil. After performing the clutter removal, the results validate the improvement of the parabolic curve in the B-scan GPR image. © 2021 IEEE.

关键词： Ground penetrating radar systems

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Modeling and Lumped-Element Extraction of PCB-Based Laterally Coupled Coplanar Transformers

Modeling and Lumped-Element Extraction of PCB-Based Laterall...

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Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Ozberk Ozturk Najeh Zeidi Fares Tounsi Denis Flandre Murat Kaya Yapici Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkiye ICTEAM Institute UCLouvain Louvain-la-Neuve Belgium Faculty of Engineering and Natural Sciences Sabanci University Istanbul Türkiye Department of Electrical and Computer Engineering University of Washington Seattle USA

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

In this paper, we present a comprehensive analysis of concentric and interleaved coplanar transformer models, focusing on key performance parameters such as self-inductance, resistance, mutual inductance, and interwinding capacitance. These parameters are used to develop a lumped-element model that accurately represents the behavior of a coplanar transformer. We propose a universal method based on FEM simulations to extract each parameter value, while the validity of the lumped model is verified by comparisons with measurements obtained from printed circuit board (PCB) transformers and HFSS simulations.

关键词： Resistance Analytical models Inductance Printed circuits Transformers Capacitance Vectors Finite element analysis Thermal analysis Integrated circuit modeling

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Experiences in PCB manufacturing with ultra-lowcost and home-made CNC machine

Experiences in PCB manufacturing with ultra-lowcost and home...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Bernardo Yaser LeÓN ÁVila Carlos Alberto GarcÍA VÁZquez Osmel PÉRez Baluja Luis Alberto Quintero DomÍNguez Daniel Tudor Cotfas Petru Adrian Cotfas Department of Electronics and Computers Faculty of Electrical Engineering and Computer Science Transilvania University of Braşov Braşov Romania Department of Computer Engineering Faculty of Business and Technical Sciences University of Sancti Spiritus “José Martí Pérez” Sancti Spiritus Cuba

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In the context of printed circuit board (PCB) manufacturing, accessibility and affordability are pivotal, especially for individuals and institutions with limited financial resources. The advent of ultra-low-cost and homemade computer numerical control (CNC) machines offers a promising solution to address these concerns. However, the plethora of online resources often presents solutions fraught with practical challenges. This paper delves into firsthand experiences in utilizing such technologies, navigating the delicate balance between cost-effectiveness and functionality in prototyping. By examining the inherent obstacles, it aims to offer valuable insights for those seeking to achieve PCB manufacturing goals with minimal resources.

关键词： Vibrations Economics Systematics Navigation Printed circuits Milling Manufacturing

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Machine Learning vs. Deep Learning in 5G Networks - A Comparison of Scientific Impact

arXiv

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arXiv 2022年

作者： Turker, Ilker Tan, Serhat Orkun Karabuk University Faculty of Engineering Department of Computer Engineering Turkey Karabuk University Faculty of Technology Department of Electrical Engineering Turkey

Introduction of fifth generation (5G) wireless network technology has matched the crucial need for high capacity and speed needs of the new generation mobile applications. Recent advances in Artificial Intelligence (AI) also empowered 5G cellular networks with two mainstreams as machine learning (ML) and deep learning (DL) techniques. Our study aims to uncover the differences in scientific impact for these two techniques by the means of statistical bibliometrics. The performed analysis includes citation performance with respect to indexing types, funding availability, journal or conference publishing options together with distributions of these metrics along years to evaluate the popularity trends in a detailed manner. Web of Science (WoS) database host 2245 papers for ML and 1407 papers for DL-related studies. DL studies, starting with 9% rate in 2013, has reached to 45% rate in 2022 among all DL and ML-related studies. Results related to scientific impact indicate that DL studies get slightly more average normalized citation (2.256) compared to ML studies (2.118) in 5G, while SCI-Expanded indexed papers in both sides tend to have similar citation performance (3.165 and 3.162 respectively). ML-related studies those are indexed in ESCI show twice citation performance compared to DL. Conference papers in DL domain and journal papers in ML domain are superior in scientific interest to their counterparts with minor differences. Highest citation performance for ML studies is achieved for year 2014, while this peak is observed for 2017 for DL studies. We can conclude that both publication and citation rate for DL-related papers tend to increase and outperform ML-based studies in 5G domain by the means of citation metrics. © 2022, CC BY.

关键词： Wireless networks

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Low resolution image area classifier based on Convolutional Neural Network 18

Low resolution image area classifier based on Convolutional ...

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18th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Ngernplubpla, Jaturon Warunsin, Kulwarun Chitsobhuk, Orachat King Monkut's Institute of Technology School of Engineering Department of Electrical Engineering Ladkrabang Bangkok Thailand Ramkhamhaeng University Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9780738111278

Deep learning techniques are widely implemented in computer vision applications. The Convolutional Neural Networks (CNN) is a deep learning class that is the most effective in categorizing the statistical characteristics of images. It is often a challenging task to classify the frequency level region in various low-resolution image. In this research, we proposed the CNN for classification of gradient profile priors by learning on several gradient characteristics such as horizontal gradient acceleration, vertical gradient acceleration, the Relational Gradient Direction and Edge Sketch Image. This technique is used multiple building blocks to designed features through backpropagation with automatic and adaptive spatial hierarchies learning. The performance comparison was improved in classification of the frequency level area in various low-resolution image input that was illustrated in the experimental results which evaluate with several predictive and conventional classification techniques. © 2021 IEEE.

关键词： Convolutional neural networks

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