2022 International Conference for Advancement in Technology (ICONAT)最新文献_第3页

Analysis of Air-To-Ground Ranging (AGR) Mode on Airborne Multi-mode Fire-Control Radar 机载多模火控雷达空对地测距模式分析

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725891

B. B. Mohapatra, P. Joseph, D. K, Vishal Mahajan, D. Seshagiri

{"title":"Analysis of Air-To-Ground Ranging (AGR) Mode on Airborne Multi-mode Fire-Control Radar","authors":"B. B. Mohapatra, P. Joseph, D. K, Vishal Mahajan, D. Seshagiri","doi":"10.1109/ICONAT53423.2022.9725891","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9725891","url":null,"abstract":"Air-to-ground ranging (AGR) is one of the crucial functionalities of an Airborne multi-mode fire-control Radar. This mode provides precise bore-sight range measurements from the antenna to the point of intersection of the antenna bore-sight with the ground for weapon delivery. The bore-sight range is estimated by characterizing the sign cross-over point of the elevation monopulse curve. Conventional Range-Doppler processing is employed for signal processing and a range estimation scheme involving multi-level thresholding and cost minimization is proposed for estimating the actual bore-sight range. The performance of the algorithm is evaluated first on simulated data and then on actual radar data collected via an airborne multi-mode fire control radar system. The bore-sight range estimator is fine-tuned using moving average (MA) filtering to obtain a much accurate and more precise range estimate. The measurements are analyzed under stable roll conditions of the platform to determine the operational performance of the algorithm chain. The height of the terrain is measured from the estimated bore-sight range and is validated using the terrain Digital Elevation Model (DEM).","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"72 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":"122220286","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: 0

Control of Differential Boost Inverter Based Integrated EV Charger Using Traction Machine Winding Inductances 利用牵引电机绕组电感控制差动升压逆变器集成电动汽车充电器

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9726051

Shamik Sen, S. Singh, S. Choudhuri

{"title":"Control of Differential Boost Inverter Based Integrated EV Charger Using Traction Machine Winding Inductances","authors":"Shamik Sen, S. Singh, S. Choudhuri","doi":"10.1109/ICONAT53423.2022.9726051","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9726051","url":null,"abstract":"This study introduces control of grid-connected three-phase Differential Boost Inverter in the stationary alpha-beta reference frame for Electric Vehicle (EV) charging operation. The advantage of the approach is that the EV's low-voltage onboard battery bank can be directly contacted with the input-face of the inverter for charging. Only two Proportional Resonant current controllers accomplish this single-stage process. Only two grid side line voltages and two grid line currents are sensed for the control purpose. Also, grid side inductances have been kept very small in the order of low power drive motor inductances. Moreover, the input side current is continuous, which eliminates the need for a high-value electrolytic capacitor at the input side. This improves systems reliability. Compared to a single-phase system, the input parallel topology of three-phase DBI can share more power and be free from second-order low-frequency ripple at the battery current. Also, the DBI can be utilized as a three-phase motor drive for EV motors, even though not pursued in the present work. Reactive current compensation was also brought out. Apart from that, this DBI based charger introduces a very small THD at the grid current waveform. Validation of the concept has been done in the MATLAB Simulink environment.","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"85 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":"128189059","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

Big data analysis on yelp user-generated reviews yelp用户评论的大数据分析

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9726108

Denish K Kalariya, Shubham Vyas, Dev Savasni, Samir Patel

引用次数: 0

Plant Disease Detection and Classification Using Machine Learning Algorithm 基于机器学习算法的植物病害检测与分类

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9726036

Dhruvi Gosai, B. Kaka, Dweepna Garg, Radhika Patel, Amit Ganatra

{"title":"Plant Disease Detection and Classification Using Machine Learning Algorithm","authors":"Dhruvi Gosai, B. Kaka, Dweepna Garg, Radhika Patel, Amit Ganatra","doi":"10.1109/ICONAT53423.2022.9726036","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9726036","url":null,"abstract":"Agriculture accepts a basic part by virtue of the quick improvement of the general population and extended interest in food in India. Hence, it is required to increase harvest yield. One serious cause of low collect yield is an infection brought about by microorganisms, infection, and organisms. Plant disease investigation is one of the major and essential tasks in the part of cultivating. It tends to be forestalled by utilizing plant disease detection techniques. To monitor, observe or take care of plant diseases manually is a very complex task. It requires gigantic proportions of work, and moreover needs outrageous planning time; consequently, image processing is utilized to distinguish diseases of plants. Plant disease classification can be done by using machine learning algorithms which include steps like dataset creation, load pictures, pre-preparing, segmentation, feature extraction, training classifier, and classification. The main objective of this research is to construct one model, which classifies the healthy and diseased harvest leaves and predicts diseases of plants. In this paper, the researchers have trained a model to recognize some unique harvests and 26 diseases from the public dataset which contains 54,306 images of the diseases and healthy plant leaves that are collected under controlled conditions. This paper worked on the ResNets algorithm. A residual neural network (ResNet) is a subpart of the artificial neural network (ANN). ResNet algorithm contains a residual block that can be used to solve the problem of vanishing/exploding gradient. ResNet algorithm is also used for creating Residual Network. For the image classification, ResNets achieve a much well result. The ResNets techniques applied some of the parameters like scheduling learning rate, gradient clipping, and weight decay. Using the ResNet algorithm, the researchers expect high accuracy results and detecting more diseases from the various harvests.","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"67 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":"131918203","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: 3

Drugs Designing Using Artificial Intelligence Based Pharmaceutical Systems 基于人工智能的药物系统的药物设计

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725820

Sajid Hamed Reshak

{"title":"Drugs Designing Using Artificial Intelligence Based Pharmaceutical Systems","authors":"Sajid Hamed Reshak","doi":"10.1109/ICONAT53423.2022.9725820","DOIUrl":"https://doi.org/10.1109/ICONAT53423.2022.9725820","url":null,"abstract":"In the field of Artificial Neural Networks (ANNs), computer algorithms and comparable to the structure of the brain's neurons are used for modelling and pattern recognition. What the brain does with all of its experiences is learn. When one views the brain as a biological neuron, one finds inputs coming in from a variety of external resources, such as the visual cortex, the hippocampus, and the thalamus, and the cell processes those inputs, performing a nonlinear operation before producing a conclusion. Adaptive biological neurons serve as the ANNs’ analogues, which mimic the biological nervous system. In contrast to statistical modelling, ANNs are simple and versatile and don't need a defined experimental design. They may use partial or historical data to map functions. ANNs are excellent pattern and classification recognizers, as well as having the capacity to make choices while using imprecise input data. The applications of ANNs to many fields, including pharmaceutical research, engineering, psychology, and medicinal chemistry, are well documented. Applied neural network technique has several potential applications in the pharmaceutical sciences. We shall describe several instances of ANNs in drug discovery in this article.","PeriodicalId":377501,"journal":{"name":"2022 International Conference for Advancement in Technology (ICONAT)","volume":"2 3 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":"132042602","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

Exploring Bioinspired Feature Engineering Technique for Online Hate Speech Detection 探索生物特征工程技术用于在线仇恨语音检测

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9726098

Anjum, R. Katarya

引用次数: 0

Improvement of AC Chopper Circuit for EV Operation 电动汽车运行交流斩波电路的改进

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725998

P. Shrivastava, Manmay Banerjee, Harpreet Kaur

引用次数: 1

A Patient-Centric Interoperable, Quorum-based Healthcare System for Sharing Clinical Data 用于共享临床数据的以患者为中心的、可互操作的、基于群体的医疗保健系统

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725924

Merlin George, A. Chacko

引用次数: 2

CoviMon - Pandemic Healthcare Monitoring System CoviMon -大流行医疗监测系统

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725920

Trisha Polly, Robinson Johnwilson, S. Rebeiro

引用次数: 0

Comparative Analysis of Algorithms for Recognizing Emotions by Eye Blink 眨眼情绪识别算法的比较分析

2022 International Conference for Advancement in Technology (ICONAT) Pub Date : 2022-01-21 DOI: 10.1109/ICONAT53423.2022.9725875

A. Borah, S. Subhashini, Atreyee Mohesh, K. Roshini

引用次数: 2