2022 International Conference for Advancement in Technology (ICONAT)最新文献_第4页

Joint Time-Frequency Analysis of Partial Discharge AE Signals for Pattern Recognition 面向模式识别的局部放电声发射信号时频联合分析

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725867

Kavita Sao, M. V. Chilukuri

{"title":"Joint Time-Frequency Analysis of Partial Discharge AE Signals for Pattern Recognition","authors":"Kavita Sao, M. V. Chilukuri","doi":"10.1109/ICONAT53423.2022.9725867","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9725867","url":null,"abstract":"Joint Time-Frequency Analysis is an essential tool in signal processing, especially for developing pattern recognition techniques for condition monitoring and diagnostics. Several available methods in the literature demonstrate the successful application of Joint Time-Frequency Analysis (JTFA) for nonstationary signal processing, partial discharge analysis, condition monitoring, and biomedical engineering. Partial discharge detection and analysis is an essential topic for the condition monitoring of Transformer, Generator, High Voltage Cables, and Gas Insulated substations. There are several tools available for JTFA using both MATLAB and LabVIEW. However, they are limited to the following techniques Short-Time Fourier Transform, Wigner-Ville Transformation, and Wavelet Transform, whose performance reduces under the noise. Hence, there is a need to develop a suitable intelligent tool for real-world applications with superior performance under noise. In this paper, a Joint Time-Frequency Analysis tool has been developed as a first step for the pattern recognition of partial discharge signatures. The developed MATLAB GUI uses an advanced multiresolution analysis algorithm such as Complex S-Transform (CST) for analyzing partial discharge signals. The developed tool has been successfully applied to analyze partial discharges and provides a better result than existing techniques.","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130955486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluating the performance of Fuzzy Clustering using different distance metrics for Image Segmentation 用不同距离度量评价模糊聚类在图像分割中的性能

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725950

J. Rathee, Prabhjot Kaur, Ajmer Singh

引用次数: 0

Detection of Animals in Thermal Imagery for Surveillance using GAN and Object Detection Framework 基于GAN和目标检测框架的热图像动物检测

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725883

K. Khatri, A. S, Jeane Marina D'Souza

{"title":"Detection of Animals in Thermal Imagery for Surveillance using GAN and Object Detection Framework","authors":"K. Khatri, A. S, Jeane Marina D'Souza","doi":"10.1109/ICONAT53423.2022.9725883","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9725883","url":null,"abstract":"Wild animals have been a challenge to farmers worldwide as they are very active during the nighttime. Animals like elephants, deer, monkeys, cows, rats, peacocks, and many cause severe damage to crops by trampling. It is easier to protect crops in daylight, but it is tough for farmers to protect the field at night. Even in the forest, it is hard for zoologists to understand the activity pattern of animals at night. To tackle the challenge of detecting and tracking the animals at night, we propose a model that focuses on animal detection on thermal images. Although object detection is an advanced problem in computer vision, they mainly focus on color images rather than thermal images. Hence, a powerful object detection technique is required to detect and recognize the objects in thermal images. In addition, plenty of datasets are available for normal objects. However, there is a dearth of the thermal for animals to carry out the research. The work aims to create the dataset by collecting thermal images from FLIR videos. In addition, the dataset lacks the training data required for deep learning methods. Hence, the ThermalGAN framework uses color images to convert into thermal images. After that, YOLOv4 is trained to estimate the position of the animal. The proposed model predicts the location of animals with an average precision of 84.77% and an F1-score of 94%.","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132744526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

REDE - Detecting human emotions using CNN and RASA REDE -使用CNN和RASA检测人类情绪

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9726090

Anya Gupta, Monica Arul Raj, Khushi Singh, Rupali Deshmukh

{"title":"REDE - Detecting human emotions using CNN and RASA","authors":"Anya Gupta, Monica Arul Raj, Khushi Singh, Rupali Deshmukh","doi":"10.1109/ICONAT53423.2022.9726090","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9726090","url":null,"abstract":"The involvement of technology in medical health has already been a great success to a large extent; it is used to measure depression and initiate the advancement into the field of mental health toward therapy and counselling. According to the WHO, good health is not only about zero sicknesses or disability but is also about physical well-being, sound mental state and social and spiritual welfare. The technological implementation of artificial intelligence (AI) in mental health has vast potential for personalizing treatment selection, prognostication, and relapse monitoring. Moreover, it provides remedies to reduce stress and anxiety for situations that do not require immediate and necessary medical intrusion and emergency contacts and services in case of a severe condition. Particularly, to discern depressive behaviours, multi-modal data is used to examine and exploit a large variety of parameters. Unlike the usual method of having an observational study that is done by taking surveys or questionnaires, the AI model helps us to understand and explore the inconspicuous and reliable detection of depressive symptoms obtained from visual and vocal features of the user. In today's time, vocalizing one's concerns regarding their mental health must be normalized. As humans, it is normal to feel different emotions at once. The application is free and anonymous to make the users feel empowered and safe in seeking treatment. Mental health is all about how an individual thinks, feels and copes up with events in their life.","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133744101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Voiceproof: Speaker Verification with Captcha A Novel Biometric Access Control Mechanism 防声:用验证码验证说话人——一种新的生物识别访问控制机制

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9726035

M. Nene, R. Mohapatra

引用次数: 0

Stability Analysis of Electromagnetic Levitation System Using Lyapunov-Krasovskii's Method 用Lyapunov-Krasovskii方法分析电磁悬浮系统的稳定性

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9726080

Amitosh Pandey, D. Adhyaru

引用次数: 1

The Optimization of Solar Photovoltaic System for Rural Off-grid Villages 农村离网村太阳能光伏发电系统优化研究

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725993

Prashobh Karunakaran, S. Karunakaran, Favian Cassidy, M. S. Osman, Arjun Karunakaran, Vayalooran Karuppan, Prashanth Karunakaran, Sreeja Haridas

{"title":"The Optimization of Solar Photovoltaic System for Rural Off-grid Villages","authors":"Prashobh Karunakaran, S. Karunakaran, Favian Cassidy, M. S. Osman, Arjun Karunakaran, Vayalooran Karuppan, Prashanth Karunakaran, Sreeja Haridas","doi":"10.1109/ICONAT53423.2022.9725993","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9725993","url":null,"abstract":"This work studies improvements that can be made to the efficiency of solar PV systems such that they can be well utilized especially in non-grid connected rural villages to relieve the combustion engine thereby saving diesel as well as providing much needed rest time for the engine parts. Four efficiency factors were worked upon which are solar tracking, the digitization of the solar panel by providing them more intelligence, an improvement of the battery maintenance system and the digitization of the battery bank. To fully accomplish the digitization of the solar panel and battery bank AI is required and this is still being worked upon. An MPPT or Maximum Power Point Tracking system can still be utilized to provide a maximum power transfer to batteries. Solar PV output especially in equatorial Malaysia has a dismal historical record mainly due to the heavy cloud cover which forms intermittent shadows on the panels. This intermittency and high heat shortens lifespans of the battery banks and reduces the efficiency of the panels respectively.","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124573879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Tile-Based Approach for the LULC Classification of Sentinel Image Using Deep Learning Techniques 基于tile的哨兵图像LULC分类方法的深度学习技术

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9726030

M. Pallavi, T. Thivakaran, Chandankeri Ganapathi

引用次数: 1

A Novel Kalman Filter Based Trilateration Approach for Indoor Localization Problem 一种基于卡尔曼滤波的室内定位新方法

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725834

Hena Kausar, S. Chattaraj

{"title":"A Novel Kalman Filter Based Trilateration Approach for Indoor Localization Problem","authors":"Hena Kausar, S. Chattaraj","doi":"10.1109/ICONAT53423.2022.9725834","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9725834","url":null,"abstract":"Accurate identification of location of a mobile object in indoor environment is very much important due to its role in location based services. Global positioning system suffers in indoor environment due to poor signal strengths of distant satellite. In indoor environment, positioning information is obtained by processing the received signal strengths communicated between the mobile object and various stationary wireless access points. The noise contaminating the measurements and the propagation delay between receivers and senders make this processing complicated. A Kalman filter can be utilized to handle such intricacies. Accuracy of such Kalman filter based approach is very much depended on initialization of parameters, which is further depended on accurate knowledge of the location map. Associating a Kalman filter to preprocess the measurements of all access points of the location makes the system computationally expensive. The current work investigates a Kalman filter based indoor localization system which avoids the need of any prior knowledge of the environment which is essential in methods such as fingerprinting. Instead of preprocessing the measurements available from all access points, it first uses trilateration based localization algorithm on how many data are available. It then applies one Kalman filter algorithm on the data which found nearest to the object based on the proximity obtained in the previous phase. This makes the system computationally efficient. Simulation results show that, < 1 meter accuracy can be obtained by this technique which is at par with some existing techniques.","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123490020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Multi Spectral Image Retrieval in Remote Sensing Big Data using Fast Recurrent Convolutional Neural Network 基于快速递归卷积神经网络的遥感大数据多光谱图像检索

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725921

B. Sathiyaprasad, B. S. Kumar

{"title":"Multi Spectral Image Retrieval in Remote Sensing Big Data using Fast Recurrent Convolutional Neural Network","authors":"B. Sathiyaprasad, B. S. Kumar","doi":"10.1109/ICONAT53423.2022.9725921","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9725921","url":null,"abstract":"The retrieval of Multispectral image is vast area in machine learning which has input data which is not static as per consideration. They has disadvantages in communication, memory in remote sensing area and compression over the lossy data which is very important, still it cannot be avoided for unnecessary objects. Because of the intricacies (spatial, ghastly, unique information sources, and fleeting irregularities) in on the web and time-arrangement multispectral picture investigation, there is a high event likelihood in varieties of otherworldly groups from an information stream, which decays the experiments in classification (in terms of accuracy) else can change as inefficient. For handling these problems with big data, deep learning is specifically efficient. By all accounts there is an extraordinary possibility for misusing the possibilities of such complex big data. The complex of retrieving remote sensed data with higher resolution in terms of effectiveness and accuracy, this research proposed architecture of neural network in feature extractionofimages collected from satellite using fast recurrent convolutional neural network (FRCNN). Here FRCNN is designed for retrieving the image collected by satellite without any loss of data and to identify objects and accurately locate them. Using the accuracy, precision, recall and F1 score the relevance of the results are computed.","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122068419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2