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Design of dv/dt Filter for Insulation Protection in SiC Inverter-Fed Motor Drive systems

Transactions of the Korean Institute of Electrical Engineers 2024年第1期73卷 56-62页

作者： Choi, Hye-Won Bae, Yun-Jae Kang, Yong-Jin Park, Cheol-Hyun Lee, Kyo-Beum Dept. of Electrical and Computer Engineering Ajou University Korea Republic of Product Development Group LS ELECTRIC Korea Republic of

This paper proposes design of dv/dt filter for insulation protection in SiC inverter-fed motor drive systems. The SiC inverter is subjected to voltage and current surges. It may cause additional losses in the motor, insulation failure, high bearing current, and electromagnetic interference. To mitigate these problems, a high value of gate resistance is placed at the gate driver;however, it introduces additional switching losses on the devices. In this paper, the filter is injected between the inverter and the motor system. The characteristics of each filter such as rising time, peak voltage, and dv/dt are analyzed to ensure the national electric manufacturers association (NEMA) standard. The proposed design scheme of the dv/dt filter is demonstrated by various experimental results. © 2024 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Silicon carbide

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Utilizing the U-Net Network for Segmenting the Left Ventricle from Cine-MRI Scans 4

Utilizing the U-Net Network for Segmenting the Left Ventricl...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Malhotra, Sonal Thapliyal, Siddhant Aggarwal, Priyanshi Kumar, Mukesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350387490

In this research, we studied the U-Net segmentation model for identifying and demonstrating addresses related to cardiac left ventricle contour in cine-MRI images. Efficacy of the model is evaluated across different stages such as preprocessing, training, validation and robustness testing along with comparisons to conventional methods and other deep learning architectures. But the preprocessing includes standardized pixel intensities, and by data augmentation using rotation, flip method or scaling techniques. The training and validation stages clearly show the robust consolidation of our model with little overfitting, guaranteeing decent performance on different datasets. The developed model is robust by passing the test against noise/ artifacts, and providing a very good segmentation in extreme situations so that there are no accuracy outliers represented. Comparative analyses show better sensitivity-specificity trade-offs and precision-recall characteristics versus other methods. In general, a U-Net model developed here works well for cardiac MRI analysis and can be used as an effective tool to help automatic LV segmentation accurate and efficient in clinical applications which will improve diagnosis of heart diseases from imaging points. © 2024 IEEE.

关键词： Deep learning

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Utilization of Decentralized Finance (DeFi) and Distributed Ledger Technology (DLT) in Banking operations

Utilization of Decentralized Finance (DeFi) and Distributed ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Kumar, Ch Sree Singh, Akhilendra Pratap Reddy, K Hemant K National Institute of Technology Meghalaya Dept. of Computer Science Meghalaya India VIT-AP Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350369175

Distributed Ledger Technology (DLT) is a decentralized database system where transactions are recorded and verified across multiple nodes. Its key features include immutability, time-stamping, and consensus-based validation. Numerous DLT applications are in supply chain management, intellectual property, cross border payments, energy trading, real estate, and online donations. DeFi, a combination of cryptocurrency and blockchain technology, offers financial services without intermediaries. Hence transactions with various digital assets based on cryptocurrency price feeds needs an automated framework. Smart contracts, self-executing contracts with terms directly written into code, automate processes and reduce manual intervention. This paper proposes a decentralized financial trading model using the AAVE protocol. AAVE is a decentralized platform that allows for transactions with various digital assets based on cryptocurrency price feeds. Proposed model uses Chainlink, a decentralized oracle network, to provide accurate and reliable price feeds. IPFS is used for data storage, while Graph is employed for indexing and querying blockchain data. The paper presents an example of a DeFi protocol to simulate banking operations, showcasing the potential of DeFi and DLT in revolutionizing traditional banking processes. © 2024 IEEE.

关键词： Distributed ledger

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Detecting Network Anomalies Using the Rain Optimization Algorithm and Hoeffding Tree-based Autoencoder 10

Detecting Network Anomalies Using the Rain Optimization Algo...

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10th International Conference on Web Research, ICWR 2024

作者： Mohammad, Mohammad Abbas Kolahkaj, Maral Abadan Branch Islamic Azad University Dept. of Computer Engineering Abadan Iran Sousangerd Branch Islamic Azad University Dept. of Computer Engineering Sousangerd Iran

ISBN: (纸本)9798350394986

The utilization of computer systems has rapidly expanded, accompanied by a corresponding rise in security threats such as hackers, viruses, worms, and similar malicious entities spreading at an alarming rate across networks. In response, anomaly intrusion detection methods have been developed to counter these attacks. However, as information systems evolve, certain detection techniques have seen a decline in effectiveness due to the escalating volume of network data traffic and the continuous need for swift responses. Addressing this critical issue, this research proposes a method to enhance the accuracy of feature selection and extraction for intrusion detection and anomaly classification. This is achieved through the integration of optimization and autoencoder algorithms, evaluating the impact of machine learning and artificial intelligence in network anomaly detection. Utilizing the NSL-KDD dataset, the study begins with data collection and preparation, followed by the application of optimization algorithms such as the Rain Optimization Algorithm (ROA) and Artificial Bee Colony (ABC) in conjunction with various neural network architectures, including Radial Basis Function neural network, decision tree, Support Vector Machine, K-Nearest Neighbors, ensemble network, mountain model, SOM clustering, and ultimately the Hoeffding Tree-based Autoencoder network. Results demonstrate that the proposed approach, leveraging the Rain Optimization Algorithm and Hoeffding Tree-based Autoencoder network, excels in feature selection and extraction during training, effectively detecting and classifying intrusion or anomaly occurrences with high accuracy. Notably, among the algorithms tested, the Hoeffding Tree-based Autoencoder network achieved an accuracy of 98.74%, indicating commendable performance in classification and result evaluation. © 2024 IEEE.

关键词： Decision trees

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Computational Web Drug Discovery Application forSARS- Cov-2 Disease Using Predictive and Analytical Approach 10

Computational Web Drug Discovery Application forSARS- Cov-2 ...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Hemkiran, S. Kartik, R. Aswath, S. Rathan Kabilan, K. Sudha Sadasivam, G. Psg Institute of Technology and Applied Research Dept. of Computer Science and Engineering Coimbatore India Psg College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly. © 2024 IEEE.

关键词： COVID-19

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Homogenous Ensemble Learning for Air Quality Index Prediction 15

Homogenous Ensemble Learning for Air Quality Index Predictio...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Medhi, Shrabani Boruah, Rituraj Baruah, Sagar Pratim Das, Hiranya Kumar Girijananda Chowdhury University Dept. of Computer Science and Engineering Guwahati India Girijananda Chowdhury Institute of Management and Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331300579

Air pollution is a global issue of concern. Accurate prediction of the air quality index (AQI) plays a crucial role in understanding pollution patterns and implementing effective mitigation strategies. This research paper presents a comprehensive study on the application of ensemble learning techniques for AQI prediction, aiming to enhance prediction accuracy and provide insights into the driving factors behind pollution levels. The primary objective is to conduct a comparative analysis of homogenous ensemble method, namely, bagging in conjunction with individual base models, such as multiple linear regression, support vector regression, decision trees, and artificial neural networks. In total we have created 4 non-ensembled models and 4 ensembled models. Additionally, the research investigates the importance of different features in AQI prediction models, aiming to identify the key factors influencing air pollution. Hyperparameter tuning is done to optimize the models. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Recognition of Fingerprint Images using CNN for Cybercrime Detection System 2

Recognition of Fingerprint Images using CNN for Cybercrime D...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Kavitha, T. Patil, Bhimaraya Saraswathi, S. Rajarajeswari, S. Patil, Anita New Horizon College of engineering Dept. of Computer Engineering Bangalore India Sri Sivasubramaniya Nadar College of Engineering Dept. of Computer Science Engineering Chennai India Vellore Institute of Technology School of computer science and engineering Vellore India City Engineering College Dept. of Information Science Bengaluru India

ISBN: (纸本)9798350372892

Fingerprint identification plays a crucial role in crime detection, but manual analysis is time-consuming and error-prone. This research aims to develop an automated system for accurate and efficient fingerprint matching using machine learning. The proposed approach involves image acquisition, preprocessing, feature extraction, and classification. Fingerprint images are enhanced, binarized, and segmented. Minutiae (ridge endings and bifurcations) are extracted as features. A convolutional neural network (CNN) is trained on a dataset of fingerprint images for classification. By comparing algorithms, Random Forest achieved over 8 1 % accuracy, outperforming Decision Trees (nearly 75% accuracy). The trained CNN model can accurately match fingerprints from crime scenes with potential suspects, providing reliable evidence. The developed system offers law enforcement agencies an automated and accurate fingerprint identification tool, improving crime-solving efficiency and reducing manual effort. The proposed approach showcases the potential of machine learning in forensic applications. © 2024 IEEE.

关键词： Random forests

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A Multistage Cascaded Neural Network Model to Identify the Severity of Epileptic Attack

A Multistage Cascaded Neural Network Model to Identify the S...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy systems, SPICES 2024

作者： Simon, Martha Thampi, Lidiya Lilly Dept. of Computer Science College of Engineering Trivandrum India Indian Institute of Information Technology Kottayam Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350376135

Epilepsy is a neurological disorder characterized by recurrent seizures, impacting the quality of life for millions of individuals worldwide. Timely and accurate detection of epileptic seizures is crucial for proper diagnosis and effective treatment. Electroencephalogram (EEG) signals provide vital information about brain activity and serve as a valuable tool for epilepsy detection. The methodology opted for this study was an enhanced multilayer feed forward neural network for automatic detection of epilepsy from EEG signals and hence predicting the severity. The authors build a multistage cascaded neural network model for achieving the same. Initially a Simple Artificial Neural Network (SimpleANN) is designed to classify EEG segments into seizure, healthy and interictal. Later a Multilayer Feed Forward Neural Network (MFFNN) is employed for detecting the severity of epileptic patients and hence can be classified into primary and secondary stages. The experimental results showed that the proposed SimpleANN classifier had provided noticeable results, and outperforms all tested classifiers. The study involves the collection of EEG data from a diverse group of patients, including those with epilepsy and healthy individuals. This automated epilepsy identifying system passes through different stages including decomposition, feature extraction for achieving a better performance model. The proposed system was tested and compared with SVM, KNN, MLP and Decision Tree under different measurement metrics. ANN has managed to give an accuracy of 80 - 90% which is significantly higher as compared to other machine learning models. The proposed work can automatically learn complex patterns from EEG data provides an advantage in identifying subtle seizure related features that may not be evident through manual analysis. Furthermore, the model offers real-time capabilities, making it suitable for continuous monitoring of patients at risk of seizures. © 2024 IEEE.

关键词： Electroencephalography

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Survival Prediction for Patients with Cirrhosis

Survival Prediction for Patients with Cirrhosis

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sowmiya, X.L. Sanjana, R. Nishintha, A. Pavithra, S. Kannan, Shree Charan Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331510022

Cirrhosis is one of the major causes of death around the world. Hence, the possibility of survival prediction among patients affected by this disease constitutes the principal factor in properly planning treatment and management procedures. This work aims to design a predictive model of the probability of survival of patients who have cirrhosis based on patient data, including clinical parameters, medical history, and laboratory findings of these patients. The data-driven machine learning algorithm improves the predictive accuracy and significantly enhances the clinical decision-making process. This considers multiple predictors: the patient's liver function tests, the demographic factors, and the indicators of disease progression in the body. It will be validated for reliability and accuracy using real-world patient case data. Therefore, this tool could aid clinicians in devising treatment plans that improve patient outcomes and efficiently utilise healthcare resources. This study also contributes to the emerging genre of AI-assisted care because chronic liver disease management, specifically cirrhosis, in this case, requires constituting predictive analytics. © 2024 IEEE.

关键词： Predictive analytics

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HSCM: A New Framework For Handwritten Signature Verification Using ConvMixer 5

HSCM: A New Framework For Handwritten Signature Verification...

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Fathy, Mona Alaa Mohamed, Amr E. Salem, Sameh A. Helwan University Faculty of Engineering Computer & Systems Engineering Dept Cairo Egypt Faculty of Engineering Computer & Systems Engineering Dept NTRA Egypt

ISBN: (纸本)9798350381030

As nations attempt to recover from the coronavirus disease (COVID-19) epidemic, digital solutions facilitate economic change and put economies on the path to green growth. Therefore, identifying an individual based on biometric characteristics has rapidly become an urgent and essential subject. Banks, intelligence agencies, and high-profile institutions are frequently use handwritten signature verification to authenticate the identification of an individual. This paper introduces an innovative model (HSCM) for the authentication of online handwritten signatures based on the ConvMixer Network. The main idea of the proposed model is to divide the signature into several patches known as strokes, which are then processed using ConvMixer to generate signature features. These features are processed using Feed-Forward network. Experimental results on the real-world dataset show that the proposed model achieved 11.6% equal error rate (EER) compared with other competitors. This vindicates the efficacy and reliability of the proposed model in signature verification). © 2023 IEEE.

关键词： Coronavirus

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