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Suppression of over voltage in SiC-based inverter fed induction motor

Australian Journal of Electrical and electronics engineering 2024年第1期21卷 11-28页

作者： Salau, Ayodeji Olalekan Kanchana, Kadirvel Anoop, Kuttichakku Johny Markus, Elisha Didam Braide, Sepiribo Lucky Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electrical Electronics Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering VISAT Engineering College Kerala Ernakulam India Department of Electrical/Electronic and Computer Engineering Central University of Technology Free State Bloemfontein South Africa Department of Electrical/Electronic Engineering University of Port Harcourt Rivers State Choba Nigeria

Silicon carbide (SiC) based inverters are widely used due to their fast switching and ability to withstand high temperatures and Voltage. This helps to increase efficiency and reduces switching losses compared to the silicon-based inverter. However, this fast-switching device causes an increase in the Voltage at the junction of the cable and motor due to the reflected wave. The main causes of overvoltage are cable/motor impedance mismatch, fast-switching inverter devices (IGBT), and inverter-motor separation. To extend the induction motor lifespan, overvoltage reduction is necessary. Passive and active filters solve the problem, but passive filters are bulky and have high power loss. This paper presents a shunt active filter approach for an inverter-fed induction motor to reduce overvoltage. To show the effectiveness of the proposed approach, the findings were compared to the topology of the clamp-type active filter already in use. The shunt active filter used Proportional Integral (PI) and fuzzy controllers to generate reference current according to the Synchronous Reference Frame (SRF) theory. Harmonics in the power system were removed using the SRF theory. The unique aspect of this work is how the reference current is produced in the shunt active filter using the SRF theory. The proposed shunt active filter with a fuzzy controller was added to the hardware implementation to achieve better results than previous active filters. ©, Engineers Australia.

关键词： Silicon carbide

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The Effect of Imperfect Channel-Sensing for Low Power Wide Area Networks with Listen-Before-Talk

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IEEE Internet of Things Journal 2025年第12期12卷 20512-20529页

作者： Hu, Yangqian Seo, Jun-Bae Jin, Hu Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of Gyeongsang National University Department of Information and Communication Engineering Tongyeong53064 Korea Republic of Henan Normal University College of Computer and Information Engineering Xinxiang453007 China Hanyang University School of Electrical Engineering Ansan15588 Korea Republic of

This study investigates ALOHA with Listen-Before-Talk (LBT) to enhance the scalability of Low-Power Wide Area Networks (LPWANs), such as LoRa. The LBT allows devices to sense the channel prior to accessing so that it can mitigate interference by preventing devices from transmitting during ongoing transmissions. However, its effectiveness is compromised by inherent imperfections in channel sensing, such as false negatives and false positives. A false negative occurs when devices incorrectly find the channel idle while it is actually in use. Thus, this leads devices to unintended interferences with ongoing transmissions. A false positive arises when the channel is erroneously sensed as busy, despite the fact that it is free. This deprives devices of access opportunities. This work analyzes the impact of these imperfections of LBT on the performance of ALOHA in terms of throughput, access delay, and system stability. Additionally, we propose an online backoff control algorithm to optimize system performance under imperfect LBT. The results show that even when devices falsely identify the channel as idle or mistakenly detect it as busy nearly half the time, the throughput still outperforms that of ALOHA without LBT. The proposed backoff control algorithm is also shown to be essential to maximize the throughput in the presence of sensing errors. To demonstrate our analysis and algorithm, we incorporate LoRa's physical layer parameters into simulations and validate the results accordingly. © 2014 IEEE.

关键词： Low power electronics

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A Novel Routing Protocol over Time-Varying IoT Network Using RTS/CTS Framework 18

A Novel Routing Protocol over Time-Varying IoT Network Using...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sharma, Neha Pandey, Prajjwal Aryan Rathore, Prateek Pandey, Om Jee Indian Institute of Technology BHU Department of Electronics Engineering Varanasi India National Institute of Technology Department of Electronics and Communication Engineering Silchar India The LNM Institute of Information Technology Department of Communication and Computer Engineering Jaipur India

ISBN: (纸本)9798350391725

In the context of 6G IoT networks, distributed computing has become a critical enabler for seamless communication across diverse sectors, such as smart manufacturing, intelligent transportation, healthcare, and defense. Many of these applications involve mobile IoT devices (IoDs) that change locations dynamically, requiring robust and efficient multi-hop data transmission frameworks. Selecting the optimal relay node in distributed 6G IoT networks is essential, as it directly impacts energy efficiency, latency, data interference, and throughput. Additionally, ensuring the availability of the relay node's queue is crucial to prevent data loss. To address these challenges, this work introduces a novel distributed computing framework based on the Request-To-Send (RTS) and Clear-To-Send (CTS) protocols, specifically designed for time-varying 6G IoT networks. The framework incorporates an RTS/CTS mechanism to manage distributed data transfer and employs the Optimal Relay IoD Selection with Q-Learning (OptRISQL) algorithm to determine the optimal relay path dynamically. By distributing computation across nodes, the proposed approach enhances network flexibility, reduces queue sizes, and minimizes memory costs, leading to improved overall network efficiency. Simulation results demonstrate that this distributed computing method effectively reduces interference and enhances throughput, outperforming other current state-of-the-art techniques. Index Terms-6G Internet of Things (IoT), Distributed Computing, Request-To-Send (RTS), Clear-To-Send (CTS), Quality-of-Service (QoS). © 2024 IEEE.

关键词： Smart manufacturing

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Synergizing Futures: Precision Career Mapping with Llama 2 and AI Fine-Tuning for Personalized Path Prediction and Guided Navigation

Synergizing Futures: Precision Career Mapping with Llama 2 a...

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2024 International Conference on communication, computer Sciences and engineering, IC3SE 2024

作者： Singh, Anuradha Sharma, Himanshu Jindal, Kanika Chaudhary, Ankur Noida Institute Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India Noida Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Greater Noida India

ISBN: (纸本)9798350366846

These days, a lot of students struggle in how to choose the career. As they progress through their studies, students must recognize their abilities and assess their areas of interest to determine the most appropriate job for them. With the aid of this approach, today's youngsters will be able to determine which career route will yield the best outcomes in the long run if they choose the suggested vocation. This will assist in raising the student's performance and spark their interest to keep them concentrated on their desired job. This system is based on an exam that students must complete;based on their responses, it will produce a summary of the test results. The primary goal of this system is to give a summary of the artificial intelligence methods that we employed to forecast the student's performance. This structure likewise be emphasizing how we are identifying characteristics in student data by employing prediction systems. For educators, educational institutions, and students alike, using this system has proven to be advantageous. In this paper we have used various technologies like Flutter, Llama-2 generative A.I. model, Firebase and UI/UX design tools. Our experimental results of proposed system reduce the career searching effort by 80% when tested for Lyman people. © 2024 IEEE.

关键词： Students

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Less Labeling Burden: A SAR Ship Detection Data Selection Method Based on K-means 2

Less Labeling Burden: A SAR Ship Detection Data Selection Me...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yang, Yimin Yin, Junjun Lang, Ping Yang, Jian He, Yaomin Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology School of Computer and Communication Engineering Beijing China Academy of Military Sciences Institute of Systems Engineering Beijing China

ISBN: (纸本)9798331515669

In deep learning-based object detection, labeled data is crucial. However, annotating ship targets in Synthetic Aperture Radar (SAR) images is challenging because of the distinct characteristics of SAR images, especially when employing Oriented Bounding Boxes (OBB). These challenges arise from factors such as varying ship orientations, complex backgrounds, and the presence of noise, making accurate annotation more difficult. To reduce the labeling burden while minimizing its impact on ship detection performance, we propose a SAR data selection method. This method ensures that, despite limited quantities, the data contains a more representative range of scenarios when constructing a new dataset. By selecting 10 features of SAR images and performing K-means clustering based on these features, we choose the samples closest to the cluster centers as the selected data. We have validated the effectiveness of the proposed method by applying two typical object detection models to both randomly selected and clustering-selected datasets, thereby demonstrating the efficacy of proposed approach. © 2024 IEEE.

关键词： Radar imaging

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Identifying Hoaxes in Fake Spotter using XG Boost Machine Learning based Classification Method 4

Identifying Hoaxes in Fake Spotter using XG Boost Machine Le...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Chitti, Sridevi Rinku, Dhruva R. Juluru, Tarun Kumar Rao, P. Ramchandar Gouthami, Shiramshetty Kakatiya Institute of Technology and Science Department of Electronics and Communication Engineering Warangal India Cvr College of Engineering Department of Electronics and Communication Engineering Hyderabad India Jayamukhi Institute of Technological Sciences Department of Computer Science Engineering Warangal India

ISBN: (纸本)9798331529635

Social media has greatly streamlined communication in recent years which uses network, shares information, and keep up with current events. Many posts on social media are questionable and meant to deceive. There is a lot of fake news everywhere and online fake news could cause serious societal issues. It is crucial for us to identify false and misleading internet content. Technically, it is hard for us to differentiate between real and fake information. The development and sharing of content have been made easier by social media platforms, leading to a significant amount that must be examined. Information on the internet is diverse making this process difficult. In order to assess the reliability and purpose of statements, humans and machines must work together. Manually identifying fake news is challenging, and it can only be done when the individual doing the identification has extensive understanding of news. Therefore, there is a need for machine learning classifiers that can identify the false news automatically. The main goal is to measure the accuracy of deceptive detection utilizing various machine learning algorithms. Several machine learning (ML) techniques, such as Decision Trees (DT), Random Forest (RF), Logistic Regression (LR), Passive Aggressive Classifier (PAC), Extra Trees Classifier (ETC) and boosting algorithms were employed on datasets to determine if the content is authentic or untrue in order to identify hoaxes on different platforms for online networking sites. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Polarization Theory Based on Target Polarization Projection 2

Research on Polarization Theory Based on Target Polarization...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： He, Yaomin Yang, Jian Yang, Yimin Yin, Junjun Liu, Chao Academy of Military Sciences Institute of Systems Engineering Beijing China Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

Aiming at the information processing in classical polarization theory, such as polarization decomposition, optimal polarization, polarization filtering, polarization detection, and polarization classification, et al, they are of great significance which supported a large number of polarization applications. Although the theory is relatively rich, there are also some deficiencies, i.e., overlapping theories and inconsistent concepts, which have inconvenient effects on the systematic research of polarization theory. In this regard, this paper proposes the concept and mathematical model of "target polarization projection", in order to form two polarization basic theories with target polarization decomposition. It can achieve a series of polarization information processing such as polarization filtering, polarization detection, and polarization classification, thus providing a clear polarization theory framework and laying a theoretical foundation for the application of polarimetric SAR. © 2024 IEEE.

关键词： Wiener filtering

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Enhancing real time object detection for autonomous driving using YOLO-NAS algorithm with CLEO optimizer

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1321-1328页

作者： Nandal, Priyanka Pahal, Sudesh Malik, Sunesh Sehrawat, Neetu Mamta Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Electronics and Communication Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Information Technology Maharaja Surajmal Institute of Technology New Delhi India Department of Computer Science and Engineering Panipat Institute of Engineering & Technology New Delhi India

For autonomous driving to operate in a safe and effective manner, efficient and precise object detection is essential. The efficacy of the network model is heavily challenged because of the high-speed movement of vehicles and the dynamic nature of the surrounding environment that need the detection of objects of various scales. With notable improvements over earlier iterations, the You Only Look Once-Neural Architecture Search (YOLO-NAS) algorithm is a strong contender for improving real-time object recognition in autonomous vehicles. YOLO-NAS integrated with chimpanzee leader election optimization (CLEO) has the potential to greatly enhance the safety and dependability of autonomous driving systems by utilizing its sophisticated capabilities and skill fully combining it with the vehicle’s perception system. The BDD100K dataset serves as the training and evaluation set for our suggested technique. The YOLO-NAS model with CLEO optimizer achieves mean average precision (mAP) of 83%, which is significantly higher than the mAP of 65.7% attained by the YOLO-NAS model without the optimizer. This improvement highlights the effectiveness of the CLEO optimizer in enhancing detection precision. The proposed approach was compared with existing algorithms and demonstrated remarkable performance in terms of recall, precision, and mAP. It is seen that our proposed model is better by striking equilibrium between detection speed and accuracy. Based on our findings, object detection performance for autonomous driving applications is much enhanced when utilizing our suggested approach. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Autonomous vehicles Deep learning Object detection YOLO-NAS

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Review and Explore the Transformative Impact of Artificial Intelligence (AI) in Smart Healthcare Systems

Review and Explore the Transformative Impact of Artificial I...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Nagar, Sneha Patidar, Mukesh Sheikh, Rashid Singh, Sudhanshu Kashiv, Ashutosh Jain, Ankit Namdeo, Shravan Kumar Paranjpe, Pranav Mandloi, Devendra Singh Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Vadodara India Acropolis Institute of Technology and Research Department of Computer Science & Engineering Indore India Indian Institute of Technology Indore Department of Electronics and Communication Engineering Indore India

ISBN: (纸本)9798350388800

Because of its high level of complexity and data collection capabilities, the healthcare industry employs artificial intelligence (AI) more than other sectors. Artificial intelligence is becoming an increasingly important part of our daily routines. AI's features include extremely low error rates, constant accessibility, quick analysis, and real-time data dissemination. This paper examines the crucial role of artificial intelligence in intelligent healthcare systems, with a particular emphasis on enhancing healthcare delivery. Artificial intelligence in healthcare is rapidly evolving, with the potential to significantly enhance patient care, diagnosis, treatment, and cost distribution. It also presents issues of transparency, honesty, and availability. This study will look into the benefits, drawbacks, and other aspects of artificial intelligence in intelligent healthcare systems. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Ethical Considerations Explainable AI (XAI) Healthcare Resource Allocation Healthcare Systems Smart Treatment Planning

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Modelling of Wideband Concentric Ring Frequency Selective Surface for 5G Devices

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computers, Materials & Continua 2023年第1期74卷 341-361页

作者： Ankush Kapoor Pradeep Kumar Ranjan Mishra Department of Electronics and Communication Engineering Jawaharlal Nehru Government Engineering CollegeSundernagar175018India Discipline of Electrical Electronic and Computer EngineeringUniversity of KwaZulu-NatalDurban4041South Africa Department of Electrical and Electronics Engineering University of Petroleum and Energy StudiesDehradun248007India

Frequency selective surfaces(FSSs)play an important role in wireless systems as these can be used as filters,in isolating the unwanted radiation,in microstrip patch antennas for improving the performance of these antennas and in other 5G *** analysis and design of the double concentric ring frequency selective surface(DCRFSS)is presented in this *** the sub-6 GHz 5G FR1 spectrum,a computational synthesis technique for creating DCRFSS based spatial filters is *** analytical tools presented in this study can be used to gain a better understanding of filtering processes and for constructing the spatial *** of the loop sizes,angles of incidence,and polarization of the concentric rings are the factors which influence the transmission coefficient as per the thorough investigation performed in this paper.A novel synthesis approach based on mathematical equations that may be used to determine the physical parameters ofDCRFSSbased spatial filters is *** proposed synthesis technique is validated by comparing results from high frequency structure simulator(HFSS),Ansys electronic desktop circuit editor,and an experimental ***,the findings acquired from a unit cell are expanded to a 2×2 array,which shows identical performance and therefore proves its stability.

关键词： Spatial filters double concentric ring frequency selective surface(DCRFSS) angle of incidence finite element method(FEM) polarization angles

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