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Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 34-40页

作者： Joice G.Philip Jaesung Jung Ahmet Onen the Department of Energy Systems Research Ajou UniversitySuwonSouth Korea the Department of Electrical and Electronic Engineering Abdullah Gul UniversityKayseri38080Turkey the Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscatOman

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)*** phasors from PMUs are preprocessed to check for the presence of *** the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida *** superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest *** show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

关键词： Empirical wavelet transform(EWT) sub-synchronous oscillation Prony-based method Yoshida algorithm variational mode decomposition phasor measurement unit(PMU)

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Onion Crop Cultivation & Prediction of Yields by Machine Learning 2

Onion Crop Cultivation & Prediction of Yields by Machine Lea...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Chaudhari, Dipti Dhaygude, Rakhi Mulani, Umar Rane, Priyanka Khalate, Yogesh Borate, Vishal Department of Computer Engineering Pimpri Chinchwad College of Engineering and Research Pune India Department of Computer Engineering SVPM College of Engineering Maharashtra Malegoan India Department of Computer Engineering K J College of Engineering & Management Research Pune India Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Sion Mumbai India Department of Computer Engineering SVPMs College of Engineering Pune Malegaon Bk India Department of Computer Engineering Dr. D. Y. Patil College of Engineering and Innovation Pune Talegaon India

ISBN: (纸本)9798331541217

The rising worldwide populace prompts the Indian government to upgrade food supply the executives for a long time to come. Reasonable farming practices are critical for guaranteeing steady food creation while protecting the climate against unsafe synthetics, accomplished through mechanical progressions in administration. This study presents a complex multi-phantom imaging framework customized for accuracy farming. Including a RGB camera and Pi NoIR camera worked by a Raspberry Pi installed a robot;this framework catches ethereal pictures for investigation. The pictures are communicated to a Java application for handling, including honing and resizing. Ongoing information is utilized to register the Standardized Contrast Vegetation Record (NDVI), working with crop wellbeing appraisal. Utilizing Profound Learning (DL) strategies, the framework successfully distinguishes onion crop development stages from the gained dataset. The detail the execution of a dynamic profound brain network model for this reason. The framework exhibits a great presentation precision of 97.63% for a cluster size of 24 and 98.45% for a bunch size of 56. © 2024 IEEE.

关键词： Vegetables

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A Deep Learning Model that can Generate Abstractive Summaries for Multimedia Content such as Videos, Audio and Text to Provide Concise and Informative Descriptions 4

A Deep Learning Model that can Generate Abstractive Summarie...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Kotkar, Vijay Charhate, Vaidehi Ghate, Shruti Moraskar, Akshata Gaikwad, Rucha Pimpri Chinchwad College of Engineering and Research Department of Computer Engineering Ravet Pune India

ISBN: (纸本)9798350354218

Multimedia data, comprising text, audio, and video, has become increasingly prevalent in today's digital landscape, leading to a growing need for effective summarization techniques. In an era inundated with multimedia data, spanning text, audio, and video, the task of generating coherent and concise summaries has assumed paramount importance. Deep Learning (DL) techniques have emerged as powerful tools for tackling the multifaceted challenges posed by multimedia summarization. This research focuses on summarization methods that harness the power of Deep Learning (DL) to extract meaningful content from diverse multimedia formats. The objective is to provide a comprehensive overview of the state-of-the-art DL techniques employed in summarizing multimedia data. By exploring various multimedia formats and their associated challenges, this study contributes to the evolving landscape of multimedia summarization, offering insights into its applications and future potential. Overall, this study aims to deepen our understanding of DL-based multimedia summarization techniques and their implications in the context of an increasingly data-rich digital world. © 2024 IEEE.

关键词： Contrastive Learning

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A deep learning framework for enhanced mass spectrometry data analysis and biomarker screening

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computer Methods in Biomechanics and Biomedical engineering 2025年 1-13页

作者： Zhang, Shuyu Li, Zhiyu Peng, Weili Chen, Yuanyuan Wu, Yao Machine Intelligence Lab College of Computer Science Sichuan University Chengdu China National Engineering Research Center for Biomaterials School of Biomedical Engineering Sichuan University Chengdu China

Mass spectrometry (MS) serves as a powerful analytical technique in metabolomics. Traditional MS analysis workflows are heavily reliant on operator experience and are prone to be influenced by complex, high-dimensional MS data. This study introduces a deep learning framework designed to enhance the classification of complex MS data and facilitate biomarker screening. The proposed framework integrates preprocessing, classification, and biomarker selection, addressing challenges in high-dimensional MS analysis. Experimental results demonstrate significant improvements in classification tasks compared to other machine learning approaches. Additionally, the proposed peak-preprocessing module is validated for its potential in biomarker screening, identifying potential biomarkers from high-dimensional data. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liver disease deep learning mass spectrometry pre-processing

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Fault-tolerant capability analysis of six-phase induction motor with distributed, concentrated, and pseudo-concentrated windings

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Scientia Iranica 2024年第10期31卷 775-789页

作者： Alemi-Rostami, Mehdi Rezazadeh, Ghasem Tahami, Farzad Akbari-Resketi, Hamid Reza Ministry of Science Research and Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran

Six-phase motors are becoming more popular because of their advantages such as lower torque ripple, better power distribution per phase, higher efficiency, and fault-tolerant capability compared to the three-phase ones. This paper presents the fault-tolerant capability analysis of a symmetrical six-phase induction motor equipped with distributed, conventional concentrated, and pseudo-concentrated windings under open-circuit fault scenarios. For further investigation, different load types such as constant-speed, constant-torque, and constant-power are applied to the motor. Two concepts of magnetic and physical phase separations are introduced as factors affecting the motor reliability. Analytically, these factors give an insight into how the pseudo-concentrated winding could be a fault-tolerant alternative. Moreover, five parameters such as the change of output power, power loss, power factor, efficiency, and expected load loss are considered as the fault-tolerant capability parameters to evaluate the windings reliability. The aforementioned parameters are reported using the finite element analysis for different fault scenarios and different load types. Although the baseline motor dimensions are not optimized for applying the pseudo-concentrated winding, the pseudo-concentrated shows a promising performance with high fault-tolerant capability. © 2024 Sharif University of Technology. All rights reserved.

关键词： Reliability analysis

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Predictive HR: Ensemble & Deep Learning Methods for Strategic Employee Retention 3

Predictive HR: Ensemble & Deep Learning Methods for Strategi...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Begum, Sheik Ariffa Nammalwar, Subbulakshmi Vijayalakshmi, Jeyapal Vallabha, Manju Kalasalingam Academy of Research and Education Computer Science and Engineering Krishnankoil India Vaigai College of Engineering Computer Science and Engineering Madurai India

ISBN: (纸本)9798350369083

In contemporary organizational landscapes, the strategic retention of employees stands as a paramount concern for sustained success. To address this challenge, this research endeavors to harness the power of predictive analytics, employing a multifaceted approach integrating ensemble learning and deep learning methodologies. The preprocessing phase employs Min-Max Scaler to normalize the feature space, enhancing the efficacy of subsequent algorithms. Additionally, Synthetic Minority Over-sampling Technique (SMOTE) is employed to mitigate class imbalances, ensuring robustness in predictive modeling. Ensemble methods such as CatBoost, AdaBoost, Light GBM, and Gradient Boosting Machine (GBM) are leveraged to capture complex interactions and nonlinear relationships within employee data. Furthermore, Long Short-Term Memory (LSTM), a variant of recurrent neural networks, is utilized to uncover temporal dependencies and intricate patterns inherent in longitudinal employee records Ultimately, this endeavor contributes to the advancement of HR analytics, empowering organizations to preemptively address attrition risks and foster a resilient and engaged workforce. © 2024 IEEE.

关键词： Predictive analytics

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Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

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computer Systems Science & engineering 2023年第2期44卷 1719-1733页

作者： S.Nandagopal G.Karthy A.Sheryl Oliver M.Subha Department of Computer Science and Engineering Nandha College of TechnologyErode638052TamilnaduIndia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science and Engineering St.Joseph's College of EngineeringChennai600119TamilnaduIndia Department of Electronics and Communication Engineering University College of Engineering NagercoilNagercoil629004TamilnaduIndia

Human Action Recognition(HAR)and pose estimation from videos have gained signiﬁcant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,*** considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difﬁcult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so *** the literature,it is observed that hard to deal with the temporal dimension in the action recognition *** neural network(CNN)models could be used widely to solve *** this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity *** KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose *** the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human ***,an optimal DCNN model is developed to classify the human activities label based on the extracted key *** improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch *** experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.

关键词： Human activity recognition pose estimation key point extraction classiﬁcation deep learning RMSProp

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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

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TGFN-SD:A text-guided multimodal fusion network for swine disease diagnosis

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第2期15卷 266-279页

作者： Gan Yang Qifeng Li Chunjiang Zhao Chaoyuan Wang Hua Yan Rui Meng Yu Liu Ligen Yu Research Center of Information Technology Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China College of Computer and Information Engineering Tianjin Agricultural UniversityTianjin 300384China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China Department of Agricultural Structure and Bioenvironmental Engineering College of Water Resources and Civil EngineeringChina Agricultural UniversityBeijing 100083China

China is the world's largest producer of pigs,but traditional manual prevention,treatment,and diagnosis methods cannot satisfy the demands of the current intensive production *** computer-aided diagnosis(CAD)systems for pigs are dominated by expert systems,which cannot be widely applied because the collection and maintenance of knowledge is difficult,and most of them ignore the effect of multimodal information.A swine disease diagnosis model was proposed in this study,the Text-Guided Fusion Network-Swine Diagnosis(TGFN-SD)model,which integrated text case reports and disease *** model integrated the differences and complementary information in the multimodal representation of diseases through the text-guided transformer module such that text case reports could carry the semantic information of disease images for disease ***,it alleviated the phenotypic overlap problem caused by similar diseases in combination with supervised learning and self-supervised *** results revealed that TGFN-SD achieved satisfactory performance on a constructed swine disease image and text dataset(SDT6K)that covered six disease classification datasets with accuracy and F1-score of 94.48%and 94.4%*** accuracies and F1-scores increased by 8.35%and 7.24%compared with those under the unimodal situation and by 2.02%and 1.63%compared with those of the optimal baseline model under the multimodal ***,interpretability analysis revealed that the model focus area was consistent with the habits and rules of the veterinary clinical diagnosis of pigs,indicating the effectiveness of the proposed model and providing new ideas and perspectives for the study of swine disease CAD.

关键词： computer-aided diagnosis Electronic health records Multimodal fusion Self-supervised learning Swine disease

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Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

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Digital Communications and Networks 2024年第5期10卷 1245-1257页

作者： Abeer A.Z.Ibrahim Fazirulhisyam Hashim Aduwati Sali Nor K.Noordin Keivan Navaie Saber M.E.Fadul Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Wireless and Photonics Networks Research Centre(WiPNET) Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Department of Communication and Computer Engineering College of Engineering and Medical SciencesKhartoum11111Sudan School of Computing and Communications Lancaster UniversityLancasterLA14YWUnited Kingdom Department of Electrical and Electronics Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia

The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and ***,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low ***,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane *** fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network *** this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and *** considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular *** algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and *** effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline *** findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

关键词： Software defined networking Dynamic mapping Particle swarm optimization Reliability Multi-objective optimization Evolutionary

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