2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)最新文献_第5页

Performance Analysis of Three-Phase Cascaded Hbridge Multi Level Inverters 三相级联桥式多电平逆变器性能分析

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085108

Dharmbir Prasad, R. Singh, Azizul Islam, Aritra Roy, Ranadip Roy, S. Mukherjee

{"title":"Performance Analysis of Three-Phase Cascaded Hbridge Multi Level Inverters","authors":"Dharmbir Prasad, R. Singh, Azizul Islam, Aritra Roy, Ranadip Roy, S. Mukherjee","doi":"10.1109/ICCECE51049.2023.10085108","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10085108","url":null,"abstract":"Power generation from renewable energy sources is environment friendly. These are being integrated into the grid to enhance green power generation. It is necessary to enhance system performance by attaining improved output power quality, decreased losses, and fewer filtering components and transformers in order to effectively utilize these resources. There are many energy sources in nature that can be turned into either ac or dc power. The user should be receiving high quality electricity from this converter. Due to their superior harmonic profile and higher power ratings, multilevel inverters are becoming more and more used in high- power applications. Inverters with many levels take precedence over those with just two levels. In this study, a cascaded H- bridge multilevel inverter with a single hardware topology, based on sinusoidal pulse width modulation-phase disposition (SPWM-PD) modulation techniques, has been assessed. This study uses FFT techniques to demonstrate the total harmonic distortion (THD) performance of the suggested inverter.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123395583","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

Improvement of Transient Stability in Power System Using Rotating Disc Type Passive Magnetic Fault Current Limiter 旋转圆盘型无源磁故障限流器改善电力系统暂态稳定性

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085619

Asit Kumar Mondal, T. Santra

引用次数: 0

Relative Study on Performance analysis of DMFET and DMTFET based Transducers DMFET及基于DMTFET的换能器性能分析的相关研究

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085414

Shayantika Dhar, Bishal Maji, Sunai Das, Munai Das, S. Mukhopadhyay

引用次数: 0

Detection and Identification of Rice Pests Using Memory Efficient Convolutional Neural Network 基于记忆高效卷积神经网络的水稻害虫检测与识别

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10084936

Zihad Hossain Nayem, Iqbal Jahan, Abdul Aziz Rakib, Solaiman Mia

{"title":"Detection and Identification of Rice Pests Using Memory Efficient Convolutional Neural Network","authors":"Zihad Hossain Nayem, Iqbal Jahan, Abdul Aziz Rakib, Solaiman Mia","doi":"10.1109/ICCECE51049.2023.10084936","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10084936","url":null,"abstract":"Rice pest detection is a very important part for the development of our agriculture. Numerous farmers are impacted worldwide by rice pests that frequently endanger the sustainability of rice production. There are many types of machine learning techniques for detecting the rice pests. CNNs (Convolutional Neural Networks) are currently regarded as the state-of-the-art technology for image recognition. Most of the models in existing researches worked with datasets that have small number of images and classes. In this paper, We have performed the training of our proposed model with 10400 images, containing ten different classes including Bacterial Leaf Blight, Bacterial Leaf Streak, Bacterial Panicle Blight, Blast, Brown Spot, Dead Heart, Downy Mildew, Healthy, Hispa and Tungro. A custom CNN has been used in the proposed model for pest detection, which will detect different classes of rice pests. To implement our model, we have used the Keras framework with a TensorFlow backend. In addition, our proposed model gives 88.18% validation accuracy while having only 0.57 million parameters.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127811980","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