2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)最新文献_第8页

Deep Neural Network-based Single Object Tracking 基于深度神经网络的单目标跟踪

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862438

Shiv Kumar, Sandeep Kumar Singh

引用次数: 0

Classification of SSVEP Signals using Neural Networks for BCI Applications 基于脑机接口应用的SSVEP信号分类

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862368

Rebba Prashanth Kumar, Sangineni Siri Vandana, Dushetti Tejaswi, K. Charan, Ravichander Janapati, Usha Desai

引用次数: 3

Full-Bridge DC-DC Converter and Boost DC-DC Converter with Resonant Circuit For Plug-in Hybrid Electric Vehicles 插电式混合动力汽车全桥DC-DC变换器和带谐振电路的升压DC-DC变换器

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862426

Lalmalsawmi, P. Biswas

引用次数: 2

Underwater Fish Detection and Classification using Deep Learning 基于深度学习的水下鱼类检测与分类

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862410

Vrushali Pagire, A. Phadke

{"title":"Underwater Fish Detection and Classification using Deep Learning","authors":"Vrushali Pagire, A. Phadke","doi":"10.1109/ICICCSP53532.2022.9862410","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862410","url":null,"abstract":"The researchers face a difficult problem in detecting and identifying underwater fish species. Marine researchers and ecologists must evaluate the comparative profusion of fish species in their environments on a regular basis and track population trends. Researchers have presented a number of underwater computer vision, machine learning-based automatic systems for fish detection and classification. However, due of the changing undersea environment, it is extremely challenging to find the ideal system for detecting and classifying fish. Because light has such a strong influence in the aqueous medium, conducting research in this environment is difficult. The MobileNet model is utilised to detect and recognise the fish breed in the proposed work. The dataset is preprocessed before the model is implemented in order to obtain appropriate performance metrics. The work is based on the Kaggle dataset, which has nine different fish breeds in total. With a 99.74 percent accuracy, the model can detect and recognise nine different breeds. In comparison to other state of art methods, the model exhibits promising results.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128321016","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

Detection of Ventricular Fibrillation by combining Signal Processing and Machine Learning approach 结合信号处理和机器学习方法检测心室颤动

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862477

Soumik Kundu, Subhankit Prusti, S. Patnaik

{"title":"Detection of Ventricular Fibrillation by combining Signal Processing and Machine Learning approach","authors":"Soumik Kundu, Subhankit Prusti, S. Patnaik","doi":"10.1109/ICICCSP53532.2022.9862477","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862477","url":null,"abstract":"Ventricular Fibrillation is a potentially fatal cardiac disorder that occurs when electrical impulses in the ventricles are disrupted, causing the heart to quiver instead of pump. In order to preserve lives during this form of arrhythmia, a strong current impulse is passed. Electrocardiograms (ECGs) record the electrical activity of the human heart, and specialists with years of experience may interpret the ECG signal to determine the heart's condition. Since it is a life-threatening disease, its earlier detection and prevention can help survive a patient's life. The fundamental idea behind tackling this challenge was to create an algorithm that could identify trends from continuous ECG readings from various individuals and identify arrhythmias early on. An efficient data was built for classification utilizing a Random Forest classifier algorithm employing signal processing tools such as Empirical Mode Decomposition (EMD) and Discrete Fourier Transform (DFT) for feature extraction. The pre-processed data when fed into the proposed machine learning method results in an accuracy of 96.58% and two classes were classified correctly with equal confidence (Specificity = 94.26% and Sensitivity = 98.97%). Furthermore, the results are compared with various other machine learning classification algorithms like Logistic Regression, Decision Tree classifier, Extra tree classifier where the accuracy was 86.49%, 91.77%, 95.84% respectively. The results obtained after experimental validation of proposed Random Forest classifier algorithm against the other machine learning achieves highest accuracy with optimal specificity and sensitivity.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125321406","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

Combined ALFC-AVR Control of Diverse Energy Source Based Interconnected Power System using Cascade Controller 基于级联控制器的多能量互联电力系统ALFC-AVR联合控制

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862433

Biswanath Dekaraja, L. Saikia, Satish Kumar Ramoji

{"title":"Combined ALFC-AVR Control of Diverse Energy Source Based Interconnected Power System using Cascade Controller","authors":"Biswanath Dekaraja, L. Saikia, Satish Kumar Ramoji","doi":"10.1109/ICICCSP53532.2022.9862433","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862433","url":null,"abstract":"This article presents a novel fractional-order (FO) cascade controller named FO tilt-derivative with filter cascaded to FO proportional-derivative with filter (CFOTDN-FOPDN) controller for unified automatic load frequency control study considering automatic voltage regulator loop. The considered system includes hydro and dish-Stirling solar thermal system in area-1 and area-2 consists of thermal and solar thermal power plant. Pertinent physical constraints are provided to the thermal and hydro units. The communication time delay (CTD) among load dispatch center and location of the power generation unit is considered. The optimization method named artificial flora algorithm is utilized to accomplish superlative solution. Investigations reveal that the proposed controller outperforms the PIDN and TIDN controllers. Analysis reflects that the higher value of CTD degrades the system performance. Moreover, the system performance improves with the higher value of the solar insolation. Lastly, the sensitivity analysis divulges the AFA optimized controller parameters are more robust against wide variations of system loading.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127700764","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

A Smart Solar Charge Controller Based on IOT Technology with Hardware Implementation 基于物联网技术的智能太阳能充电控制器及其硬件实现

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862445

Sarita Samal, Roshan Kumar Soni, Sarthak Nayak, P. K. Barik, Rudranarayan Dash, Geetanjali Dei

{"title":"A Smart Solar Charge Controller Based on IOT Technology with Hardware Implementation","authors":"Sarita Samal, Roshan Kumar Soni, Sarthak Nayak, P. K. Barik, Rudranarayan Dash, Geetanjali Dei","doi":"10.1109/ICICCSP53532.2022.9862445","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862445","url":null,"abstract":"This Paper presents the concept of a Maximum Power Point Tracking (MPPT) based Solar Charge Controllers (SCC) for charging a battery in stand-alone Solar Photo-voltaic (SPV) systems. A SCC is a battery charge regulator which is connected in between the SPV panel and the battery, the primary purpose of the SCC is to regulate the charging of the battery so that it charges correctly. PWM based SCCs may get the job done but they have very low efficiency as compared to MPPT based ones and thus waste a lot of SPV power. This fact has been analyzed in our article by executing the simulations for both the charge controller types and the efficiency of PWM was found to be only 65% whereas that of the MPPT based is 94%. Another useful feature in modern day SCCs and in our prototype is the facility to monitor the device parameters remotely on a wireless network which provides major flexibility to controllers. In this prototype model we have implemented MPPT based SCC along with one Wi-Fi module for monitoring the battery voltage, current, PWM pulses and battery status on smartphones. The user also gets notified about the battery status, whether the battery is charging or it is over charging. If the SPV voltage is more than 12V the relay disconnects the battery from the SPV cell and a notification regarding this is given to the user.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114043054","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

Crewman Deployment Model for Improving the Resiliency of the Power System 提高电力系统弹性的机组人员配置模型

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862400

Sneha Gope, Imon Dutta, Kairab Roy, Indrayudh Chakrabarti, D. Bose, C. K. Chanda

引用次数: 0

Facial Behaviour Realization using Statistical Features 使用统计特征实现面部行为

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862441

Swapna Subudhiray, H. Palo, Niva Das

引用次数: 0

Predicting The Monthly Average Incident Shortwave Solar Energy for Hubli, India by Using Training Functions in ANN 用神经网络训练函数预测印度Hubli的月平均入射短波太阳能

2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) Pub Date : 2022-07-21 DOI: 10.1109/ICICCSP53532.2022.9862510

S. Prasanna, Kumaresh Pal, Debesh Mandal

{"title":"Predicting The Monthly Average Incident Shortwave Solar Energy for Hubli, India by Using Training Functions in ANN","authors":"S. Prasanna, Kumaresh Pal, Debesh Mandal","doi":"10.1109/ICICCSP53532.2022.9862510","DOIUrl":"https://doi.org/10.1109/ICICCSP53532.2022.9862510","url":null,"abstract":"Solar radiation is one of the vital resources found on Earth which can be renewed and hence tested and tried to be beneficiary for humankind. Solar energy is harnessed to fulfill the basic requirements of humans i.e.; supply power to operate any kind of machine or device. The way to utilize the energy for our maximum benefit is by approximating the radiation values of Sun and this can be achieved by installing measuring equipments. The main issue arises here as the equipment's maintenance and installation cost is too high to be affordable by the general people. To overcome this inconvenience, an affordable solution was developing models and methods to calculate the radiation and find the approximate values. We focus on city, Hubli, India and estimate the monthly mean radiation received on this particular city by creating a neural network in ANN (Artificial Neural network) using MATLAB. The models are validated for 3 training functions: resilient back-propagation (RP), Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM). The predicted values accuracy is also tested through statistical indicators like MSE, RMSE, MBE and MAPE.","PeriodicalId":326163,"journal":{"name":"2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121380588","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