International Journal of Electrical and Computer Engineering最新文献_第3页

A cost-effective and optimized maximum powerpoint tracking system for the photovoltaic model 光伏模型的成本效益和优化的最大ppt跟踪系统

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp4942-4949

Yoganandini Arehalli Puttalingaiah, Anitha Gowda Shesadri

{"title":"A cost-effective and optimized maximum powerpoint tracking system for the photovoltaic model","authors":"Yoganandini Arehalli Puttalingaiah, Anitha Gowda Shesadri","doi":"10.11591/ijece.v13i5.pp4942-4949","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp4942-4949","url":null,"abstract":"Solar energy is naturally available from sun, and it can be extracted by using a photovoltaic (PV) cell. However, solar energy extraction entirely depends on the climatic conditions and angle of rays falling on PV cells. Hence, maximum powerpoint tracking (MPPT) is considered in most areas under variable climatic conditions, which acts as a controller unit for PV cells. MPPT can enhance the efficiency of PV cells. However, designing an MPPT model is challenging as different uncertainties in the climatic condition may lead to more fluctuations in voltage and current in PV cells. Under the shaded condition, the PV cell may have other MPPT points that lead to the PV cell’s low efficiency in analyzing maximum power. Hence, this paper introduces a cost-effective and optimized system for the PV model that can find optimal power and improve PV cells’ efficiency. The proposed system achieves better computational performance with ~35% and ~42% than existing MPPT techniques. The improved particle swarm optimization (PSO) is smoother due to the enhanced form of MPP tracking. Hence, improved PSO takes 0.038 sec while the existing PSO technique takes 0.045 sec to obtain the MPP tracking.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47285009","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

Performance analysis of smart optimization antenna for wireless networks 无线网络中智能优化天线的性能分析

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5222-5231

Jacob Abraham, K. Suriyan, Beulah Jackson, Mahendran Natarajan, Thanga Mariappan Lakshmanaperumal

引用次数: 0

An effective technique for increasing capacity and improving bandwidth in 5G narrow-band internet of things 一种提高5G窄带物联网容量和带宽的有效技术

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5232-5242

A. Mohammed, H. Mostafa, A. A. Ammar

{"title":"An effective technique for increasing capacity and improving bandwidth in 5G narrow-band internet of things","authors":"A. Mohammed, H. Mostafa, A. A. Ammar","doi":"10.11591/ijece.v13i5.pp5232-5242","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5232-5242","url":null,"abstract":"In recent years, the wireless spectrum has become increasingly scarce as demand for wireless services has grown, requiring imaginative approaches to increase capacity within a limited spectral resource. This article proposes a new method that combines modified symbol time compression with orthogonal frequency division multiplexing (MSTC-OFDM), to enhance capacity for the narrow-band internet of things (NB-IoT) system. The suggested method, MSTC-OFDM, is based on the modified symbol time compression (MSTC) technique. The MSTC is a compressed waveform technique that increases capacity by compressing the occupied symbol time without losing bit error rate (BER) performance or data throughput. A comparative analysis is provided between the traditional orthogonal frequency division multiplexing (OFDM) system and the MSTC-OFDM method. The simulation results show that the MSTC-OFDM scheme drastically decreases the symbol time (ST) by 75% compared to a standard OFDM system. As a result, the MSTC-OFDM system offers four times the bit rate of a typical OFDM system using the same bandwidth and modulation but with a little increase in complexity. Moreover, compared to an OFDM system with 16 quadrature amplitude modulation (16QAM-OFDM), the MSTC-OFDM system reduces the signal-to-noise ratio (SNR) by 3.9 dB to transmit the same amount of data.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49191145","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

Regional feature learning using attribute structural analysis in bipartite attention framework for vehicle re-identification 基于二元注意框架属性结构分析的区域特征学习用于车辆再识别

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5824-5832

Cynthia Sherin, Kayalvizhi Jayavel

{"title":"Regional feature learning using attribute structural analysis in bipartite attention framework for vehicle re-identification","authors":"Cynthia Sherin, Kayalvizhi Jayavel","doi":"10.11591/ijece.v13i5.pp5824-5832","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5824-5832","url":null,"abstract":"Vehicle re-identification identifies target vehicles using images obtained by numerous non-overlapping real-time surveillance cameras. The effectiveness of re-identification is further challenging because of illumination changes, pose differences of captured images, and resolution. Fine-grained appearance changes in vehicles are recognized in addition to the coarse-grained characteristics like color of the vehicle along with model, and other custom features like logo stickers, annual service signs, and hangings to overcome these challenges. To prove the efficiency of our proposed bipartite attention framework, a novel dataset called Attributes27 which has 27 labelled attributes for each class are created. Our framework contains three major sections: The first section where the overall and semantic characteristics of every individual vehicle image are extracted by a double branch convolutional neural network (CNN) layer. Secondly, to identify the region of interests (ROIs) each branch has a self-attention block linked to it. Lastly to extract the regional features from the obtained ROIs, a partition-alignment block is deployed. The results of our proposed system’s evaluation on the Attributes27 and VeRi-776 datasets has highlighted significant regional attributes of each vehicle and improved the accuracy. Attributes27 and VeRi-776 datasets exhibits 98.5% and 84.3% accuracy respectively which are comparatively higher than the existing methods with 78.6% accuracy.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44011080","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

An effective feature selection using improved marine predators algorithm for Alzheimer’s disease classification 改进的海洋捕食者算法用于阿尔茨海默病分类的有效特征选择

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5126-5134

P. Topannavar, D. M. Yadav

{"title":"An effective feature selection using improved marine predators algorithm for Alzheimer’s disease classification","authors":"P. Topannavar, D. M. Yadav","doi":"10.11591/ijece.v13i5.pp5126-5134","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5126-5134","url":null,"abstract":"Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43693961","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

Explainable extreme boosting model for breast cancer diagnosis 可解释的乳腺癌诊断极端增强模型

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5764-5769

Tamilarasi Suresh, Tsehay Admassu Assegie, S. Ganesan, R. Tulasi, Radha Mothukuri, Ayodeji Olalekan Salau

引用次数: 0

System of gender identification and age estimation from radiography: a review 射线照相的性别识别和年龄估计系统综述

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5491-5500

Nur Nafi’iyah, C. Fatichah, D. Herumurti, E. Astuti, R. Putra, E. Prakasa, Yosi Kristian

引用次数: 0

Ensembling techniques in solar panel quality classification 太阳能电池板质量分类中的集成技术

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5674-5680

Trong Hieu Luu, Phan Nguyen Ky Phuc, T. Lam, Zhi-qiu Yu, Van Tinh Lam

引用次数: 0

Channel and spatial attention mechanism for fashion image captioning 时尚图片字幕的通道和空间注意机制

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5833-5842

Bao T. Nguyen, S. T. Nguyen, Anh H. Vo

{"title":"Channel and spatial attention mechanism for fashion image captioning","authors":"Bao T. Nguyen, S. T. Nguyen, Anh H. Vo","doi":"10.11591/ijece.v13i5.pp5833-5842","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5833-5842","url":null,"abstract":"Image captioning aims to automatically generate one or more description sentences for a given input image. Most of the existing captioning methods use encoder-decoder model which mainly focus on recognizing and capturing the relationship between objects appearing in the input image. However, when generating captions for fashion images, it is important to not only describe the items and their relationships, but also mention attribute features of clothes (shape, texture, style, fabric, and more). In this study, one novel model is proposed for fashion image captioning task which can capture not only the items and their relationship, but also their attribute features. Two different attention mechanisms (spatial-attention and channel-wise attention) is incorporated to the traditional encoder-decoder model, which dynamically interprets the caption sentence in multi-layer feature map in addition to the depth dimension of the feature map. We evaluate our proposed architecture on Fashion-Gen using three different metrics (CIDEr, ROUGE-L, and BLEU-1), and achieve the scores of 89.7, 50.6 and 45.6, respectively. Based on experiments, our proposed method shows significant performance improvement for the task of fashion-image captioning, and outperforms other state-of-the-art image captioning methods.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46325567","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

Software defined fog platform 软件定义的雾平台

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5454-5461

Sepideh Sheikhi Nejad, Ahmad Khademzadeh, A. Rahmani, A. Broumandnia

引用次数: 0