International Journal of Circuits, Systems and Signal Processing最新文献_第4页

Short Term Renewable Energy Forecasting Based on Feed Forward Back Propagation Neural Network Strategy 基于前馈-反传播神经网络策略的短期可再生能源预测

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-08-24 DOI: 10.46300/9106.2022.16.134

Dhanalaxmi H R, A. G S, Sunil Kumar A V

{"title":"Short Term Renewable Energy Forecasting Based on Feed Forward Back Propagation Neural Network Strategy","authors":"Dhanalaxmi H R, A. G S, Sunil Kumar A V","doi":"10.46300/9106.2022.16.134","DOIUrl":"https://doi.org/10.46300/9106.2022.16.134","url":null,"abstract":"The fundamental inputs used as a renewable energy source are wind speed and solar radiation. Both parameters are very nonlinear and depending on their surroundings. As a result, reliable prediction of these characteristics is required for usage in a variety of agricultural, industrial, transportation, and environmental applications since they reduce greenhouse gas emissions and are environmentally benign. In this study, we used a Feed Forward Back Propagation Neural Network (FFBPN) technique to predict proper data such as temperature, relative moisture, sun radiations, rain, and wind speed. The FFBPN will be trained in such a way that it can conduct hybrid forecasting with little changes to the programming codes, ranging from hourly (short term forecasting) to daily forecasting (medium term forecasting). This feature is one of the significant improvements, showing the suggested hybrid renewable energy forecasting system's high robustness. Because the hybrid forecasting system is a unique approach, the system's accuracy will be determined by comparing the findings to the corresponding values of the persistent model, a stand-alone forecasting model. Finally, the completely created system package could be sold and/or used in future research initiatives to help researcher’s analyses, validate, and illustrate their models across a variety of areas.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87518001","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

Logic System Design for Fault Detection and Classification of Voltage Source Inverter Driving Induction Motor 电压源逆变器驱动感应电机故障检测与分类逻辑系统设计

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-07-27 DOI: 10.46300/9106.2022.16.133

K. Mahafzah, M. Obeidat

引用次数: 0

Research on Target Tracking Algorithm Based on Kernel Correlation 基于核相关的目标跟踪算法研究

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-07-26 DOI: 10.46300/9106.2022.16.132

Shengbo Liu, Yi Guo, Yandong Zhao

引用次数: 1

Autoregressive Modeling based ECG Cardiac Arrhythmias’ Database System 基于自回归建模的心电心律失常数据库系统

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-07-26 DOI: 10.46300/9106.2022.16.130

Q. Hamarsheh

引用次数: 0

Arecanut Bunch Segmentation Using Deep Learning Techniques 使用深度学习技术分割槟榔束

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-07-26 DOI: 10.46300/9106.2022.16.129

A. A. C., R. Dhanesha, Shrinivasa Naika C. L., K. A. N., Parinith S. Kumar, Parikshith P. Sharma

{"title":"Arecanut Bunch Segmentation Using Deep Learning Techniques","authors":"A. A. C., R. Dhanesha, Shrinivasa Naika C. L., K. A. N., Parinith S. Kumar, Parikshith P. Sharma","doi":"10.46300/9106.2022.16.129","DOIUrl":"https://doi.org/10.46300/9106.2022.16.129","url":null,"abstract":"Agriculture and farming as a backbone of many developing countries provides food safety and security. Arecanut being a major plantation in India, take part an important role in the life of the farmers. Arecanut growth monitoring and harvesting needs skilled labors and it is very risky since the arecanut trees are very thin and tall. A vision-based system for agriculture and farming gains popularity in the recent years. Segmentation is a fundamental task in any vision-based system. A very few attempts been made for the segmentation of arecanut bunch and are based on hand-crafted features with limited performance. The aim of our research is to propose and develop an efficient and accurate technique for the segmentation of arecanut bunches by eliminating unwanted background information. This paper presents two deep-learning approaches: Mask Region-Based Convolutional Neural Network (Mask R-CNN) and U-Net for the segmentation of arecanut bunches from the tree images without any pre-processing. Experiments were done to estimate and evaluate the performances of both the methods and shows that Mask R-CNN performs better compared to U-Net and methods that apply segmentation on other commodities as there were no bench marks for the arecanut.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73357142","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

Flood Prediction using Deep Spiking Neural Network 基于深度峰值神经网络的洪水预测

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-07-26 DOI: 10.46300/9106.2022.16.127

Roselind Tei, Abdulrazak Yahya Saleh

引用次数: 1

Improved SURF in Color Difference Scale Space for Color Image Matching 基于色差尺度空间的彩色图像匹配改进SURF

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-07-26 DOI: 10.46300/9106.2022.16.128

Haifeng Luo, Yue Han, J. Kan

引用次数: 1

Effects of Different Superpixel Algorithms on Interactive Segmentations 不同超像素算法对交互式分割的影响

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-07-26 DOI: 10.46300/9106.2022.16.131

Kok Luong Goh, G. Ng, Muzaffar Hamzah, S. Chai

{"title":"Effects of Different Superpixel Algorithms on Interactive Segmentations","authors":"Kok Luong Goh, G. Ng, Muzaffar Hamzah, S. Chai","doi":"10.46300/9106.2022.16.131","DOIUrl":"https://doi.org/10.46300/9106.2022.16.131","url":null,"abstract":"Semi-automated segmentation or more commonly known as interactive image segmentation is an algorithm that extracts a region of interest (ROI) from an image based on the input information from the user. The said algorithm will be repetitively fed with such input information until required region of interest is successfully segmented. To accelerate this segmentation procedure as well as enhancing the result, pre-processing steps can be applied. The application of superpixel is an example of such pre-processing step. Superpixel can be defined as a collection of pixels that share common features such as texture and colours. Though employed as pre-processing step in many interactive segmentation algorithms, to date, no study has been conducted to assess the effects of such incorporations on the segmentation algorithms. Thus, this study aims to address this issue. In this study, five different types of superpixels ranging from watershed, density, graph, clustering and energy optimization categories are evaluated. The superpixels generated by these five algorithms will be used on two interactive image segmentation algorithms: i) Maximal Similarity based Region Merging (MSRM) and ii) Graph-Based Manifold Ranking (GBMR) with single and multiple strokes on various images from the Berkeley image dataset. The result of testing had shown that MSRM achieved better result compared to GBMR in both single and multiple input strokes using SEEDS superpixel algorithm. This study summary concluded that at different superpixel algorithms produced different results and that it is not possible to single out one particular superpixel algorithm that can work well for all the interactive segmentation algorithms. As such, the key to achieving a decent segmentation result lies in choosing the right superpixel algorithms for a given interactive segmentation algorithm.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90914867","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

Design Consideration of Charging Station with Hybrid Energy Sources 混合能源充电站的设计考虑

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-06-23 DOI: 10.46300/9106.2022.16.126

B. Gilev, Gergana Vacheva, Plamen Stanchev, N. Hinov

{"title":"Design Consideration of Charging Station with Hybrid Energy Sources","authors":"B. Gilev, Gergana Vacheva, Plamen Stanchev, N. Hinov","doi":"10.46300/9106.2022.16.126","DOIUrl":"https://doi.org/10.46300/9106.2022.16.126","url":null,"abstract":"In current research a hybrid autonomous supplying system for electric vehicles applications is presented. The hybrid system is consisted of fuel cell, micro gas turbine and supercapacitor. There are realized with averaged models in MATLAB/Simulink environment. The supplying elements are connected to a DC bus for charging a different type of EVs. In this case as a load is use two EVs: BMW-i3 and Nissan Leaf. This system can operate autonomously in hard-to-reach places where there is no supplying from the distributed grid and other sources. These places could be remote holiday villages, research centers positioned at hard-to-reach places and also for production of agricultural crops with the aids of electric vehicles. This requires the necessity for searching of different structural and conceptual solutions for production and storage of electric energy. An optimization problem is resolved in order to reduce the value of the capacitance of the supercapacitor with which it will decrease his price. Thus, it also decreases the price for construction of the entire charging station. Recently, the usage of natural gas and his transportation is well organized which can contribute for assuring of the reserved energy for the autonomous charging station.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82613251","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

Train Driver Fatigue Detection Using Eye Feature Vector and Support Vector Machine 基于眼特征向量和支持向量机的列车驾驶员疲劳检测

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-06-01 DOI: 10.46300/9106.2022.16.123

Taiguo Li, Tiance Zhang, Quanqin Li

{"title":"Train Driver Fatigue Detection Using Eye Feature Vector and Support Vector Machine","authors":"Taiguo Li, Tiance Zhang, Quanqin Li","doi":"10.46300/9106.2022.16.123","DOIUrl":"https://doi.org/10.46300/9106.2022.16.123","url":null,"abstract":"Fatigue driving is one of the main causes of traffic accidents. The eye features are the important cues of fatigue detection. In order to improve the accuracy and robustness of detection based on a single eye feature, we propose a fatigue detection algorithm based on the eye feature (EFV) vector. Firstly, the coordinates of the eye region were localized with facial landmarks detector and the landmarks geometric relation (LGR) was calculated as a feature value. Secondly, a deep transfer learning network was designed to classify the driver eye state on a small dataset. The probability value of the eyes being open state was calculated. Then an eye feature vector was constructed to overcome the limitations of a single fixed threshold and a support vector machine (SVM) model was trained for eye state classification recognition. Finally, the performance of the proposed detection model was evaluated by the percentage of eyelid closure over time (PERCLOS) criterion. The results show that the accuracy of this model can reach 91.67% on the test database, which is higher than the single-feature-based method. This work lays a foundation for the online fatigue detection of train drivers and the deployment of the train driving monitoring system.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73158804","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