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A Deep Echo State Network With Scaling Factor Activation Functions for Fault Diagnosis of electrical Drive Systems

IEEE Transactions on Transportation Electrification 2025年第3期11卷 7874-7884页

作者： Gong, Yuanpeng Jiang, Yulian Cheng, Chao Chen, Hongtian Wang, Shenquan Changchun University of Technology College of Electrical and Electronic Engineering Changchun130012 China Changchun University of Technology School of Computer Science and Engineering Changchun130012 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

Deep echo state networks (Deep-ESNs) play an important role in fault diagnosis. However, due to its limitation in the iterative process of dealing with nonlinear data, the accuracy of fault diagnosis is relatively low. In order to improve the fault diagnosis accuracy of electrical drive systems, this article proposes a novel Deep-ESN based on the synergistic effect of golden jackal optimization (GJO), variational mode decomposition (VMD), and scaling factor activation function, called GVSD-ESN. The main work of this study contains: 1) the novel scaling factor activation function is proposed to solve the gradient vanishing problem in the Deep-ESN model;2) GJO is used to solve high-dimensional optimization problems of VMD;3) the power spiral curve is proposed to optimize the position update equation of GJO, which solves the problem of falling into the local optimal;and 4) adding a sparse regularization layer between reservoirs can enhance the class definition of GVSD-ESN. Finally, the effectiveness of the proposed method is verified in electrical drive systems. © 2015 IEEE.

关键词： Variational mode decomposition

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Road Corner Detection System 5

Road Corner Detection System

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5th International Conference on electrical, Control and Instrumentation engineering, ICECIE 2023

作者： Ahmad Suhaimi, Ahmad Ashraf Zamani Jusoh, Ahmad Asnawi, Ani Liza Farihah Abdul Malek, Norun Abdullah, Khaizuran Mohamed Azmin, Nor Fadhillah Faculty of Engineering Iium Department of Electrical and Computer Engineering Selangor Gombak Malaysia

ISBN: (纸本)9798350325041

Taking comer in a vehicle on the road might be dangerous when it is done at a high speed as it might cause one to lose control of the car. Sometimes, the driver loses attention while driving due to many reasons that can be harmful to them, and the worst of them is an accident. This unwanted event has to be paid attention to since accident cases around the globe are increasing from day to day, and need an urgent solution to reduce or at least slow down the expanding percentage of accident cases. This project aims to improve the safety features of a car by developing a system that can detect comer when a car is approaching it and process the data collected to take precautionary steps in avoiding accidents. This system will discover the comer by reading the line lane on the road. A proper algorithm will be. developed using Python, with the assistance of OpenCV and NumPy libraries. This algorithm will be simulated using pre-recorded video, to make the development process more manageable and improvements can be applied directly to the system. © 2023 IEEE.

关键词： Accidents

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Design of Ultra-Wideband Antipodal Vivaldi Antenna with different shapes of Parasitic Patch 20

Design of Ultra-Wideband Antipodal Vivaldi Antenna with diff...

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20th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Wanna, Chanikarn Chudpooti, Nonchanutt Thaiwirot, Wanwisa Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Bangkok Thailand

ISBN: (纸本)9798350310467

The design of ultra-wideband antipodal Vivaldi antenna with parasitic patch is presented. The different shapes of parasitic patch i.e., elliptical shape, diamond shape, stepped shape which are placed on antenna aperture are proposed. The effect of these parasitic patches on reflection coefficient, gain, radiation pattern, front-to-back ratio and electric field distribution is discussed. The simulation results show that all parasitic patches can operate in frequency range from 3.1 to 10.6 GHz for UWB applications. Moreover, using these parasitic patches, the gain, radiation patterns, front-to-back ratio and electric field distribution are improved. © 2023 IEEE.

关键词： Microstrip antennas

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A Robust Framework for Epileptic Seizure Diagnosis: Utilizing GRU-CNN Architectures in EEG Signal Analysis 20

A Robust Framework for Epileptic Seizure Diagnosis: Utilizin...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nikoupour, Maryam Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran Iranian Research Organization for Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository's epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods. © 2024 IEEE.

关键词： Electroencephalography

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Object Detection in Autonomous Vehicles 13

Object Detection in Autonomous Vehicles

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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Fakharurazi, Muhammad Ikhwan Mohammad Jusoh, Ahmad Zamani Asnawi, Ani Liza Malek, Norun Farihah Abdul Abdullah, Khaizuran Azmin, Nor Fadhillah Mohamed IIUM Faculty of Engineering Department of Electrical and Computer Engineering Selangor Gombak Malaysia

ISBN: (纸本)9798350340891

Object detection in self-driving cars is a prime component because there is no human control over the cars' driving. Self-driving cars must detect an object 360 degrees around the car for the car to steer or handle the driving on the correct track. The market for autonomous vehicles is rapidly growing, and as all people now know about the level of technology in the developed countries, which are the United States, the United Kingdom, Germany, England, and Japan, those countries are going all out in the creation of electric and autonomous vehicles. However, most of the current object detection methods are expensive and ineffective. Therefore, in this paper, the new, efficient, and faster design of object detection in autonomous vehicles using the YOLOv5 neural network is investigated, explored, designed, implemented, and evaluated. YOLOv5 is the best model because it is built on the Python framework, is available in a variety of sizes, and is constantly updated and improved. The Python platform, Roboflow tool, and Google Colab were adopted to design the model. Roboflow is great for object detection because of smart polygons, label assist, commenting, and exporting in a variety of formats. Meanwhile, Google Colab is free to use (allows you to run Python code in the cloud), easy to use (user-friendly interface), and offers access to powerful GPUs. The adoption of these with the YOLOv5 neural network is a new attempt in autonomous vehicles. As a result, a better and more efficient object detection method yet affordable for autonomous vehicles was produced and is presented in this paper. © 2023 IEEE.

关键词： Autonomous vehicles

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Comparison between Homodyne and PNR Detection Schemes for Quantum Key Distribution

Comparison between Homodyne and PNR Detection Schemes for Qu...

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2023 Photonics North, PN 2023

作者： Yazdanpour, A. Khajezadeh, M. Kumar, S. McMaster University Faculty of Engineering Department of Electrical & Computer Engineering Hamilton Canada

In this paper, we study the comparison between two different methods of detection regarding key generation rate in a quantum key distribution system. We illustrate that after a threshold PNR detection outperforms homo... 详细信息

ISBN: (纸本)9798350326734

关键词： Homodyne detection

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Improving English to Persian Machine Translation with GPT Language Model and Autoencoders 9

Improving English to Persian Machine Translation with GPT La...

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9th International Conference on Web Research, ICWR 2023

作者： Dorosti, Maryam Pedram, Mir Mohsen Kharazmi University Faculty of Engineering Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350399691

Machine translation is a technology that reduces costs and speeds up the translation for users by mechanizing translation from one language to another. Machine translation is essentially a step towards globalization for every culture, science, industry, and system. This technology has made great strides in cognitive understanding of natural language since 2013 with the advent of deep learning models. But deep models also always need a lot of data for training. Therefore, the lack of a lot of data and parallel corpus in machine translation is one of the most important problems in this area. Machine translation from English to Persian always suffers from the problem of a lack of resources and data. This article tries to study the deep learning models in machine translation from English to Persian and their strengths and weaknesses, In this article, to solve the problem of lack of English- to-Persian data, the Transformer-based model has been integrated and improved with the Persian language model that has GPT architecture, in addition, the CNN model has been integrated and improved with Autoencoder to improve feature selection and reduce dimensions. © 2023 IEEE.

关键词： Computational linguistics

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Offline writer identification using a developed deep neural network based on a novel signature dataset

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12425-12441页

作者： Keykhosravi, Davood Razavi, Seyed Naser Majidzadeh, Kambiz Sangar, Amin Babazadeh Department of IT and Computer Engineering Urmia Branch Islamic Azad University Urmia57169-63896 Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz51666-16471 Iran

Signature is used as an important, typical means of identification in banking, security controls, certificates, and contracts. Moreover, it has an intensive role in legal issues that need to be studied, considering the development of related applications. From this standpoint, this study proposes a new method for identifying offline signatures under different uncertainties such as various experimental conditions and environmental noises using deep learning approaches. To achieve this objective, a comprehensive right-to-left signature dataset based on relevant standards is collected from 85 participants at various time intervals under different experimental conditions. A developed deep neural network based on transfer learning networks is designed to extract features from raw data in a hierarchical manner. One of the benefits of the proposed method is that it is independent of right-handed or left-handed people and can be applied to both. The method proposed is examined not just on the collected dataset but also on a variety of other datasets. The proposed network has a 99% accuracy for author signature classification and can withstand a wide range of SNRs. As a result, the classification accuracy at 15 dB remains above 90%. The study's findings show that the proposed network can learn features hierarchically from raw signature data and achieve greater accuracy than other methods. Because of its superior performance, the proposed model can be used as an assistant by signature experts in a variety of applications, including the detection of forgery and criminals. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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