This study presents an analysis and comparison of the MPC and PI controllers' dynamic performance. The two control structures are intended to control a single-phase PUC5 inverter that is connected to the grid via ...
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Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low...
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In recent years, a variety of rolling bearing fault diagnosis methods based on deep learning has become an emerging research orientation. However, there is still a gap between the existing diagnostic model and the pra...
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"Artificial lung" is a device that simulates breathing process of occupants in a room. This allows you to safely test, e.g., the impact of HVAC systems on the spread of pathogens. The paper describes the con...
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Breast cancer is a severe problem for women around the world especially in developing countries, according to recent reports from the World Health Organization (WHO). High accuracy and early detection of breast cancer...
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Air pollution is currently a critical issue for both public health and the environment. It is vital to provide advance notice of pollution levels, and air quality forecasts can play a crucial role in achieving this go...
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Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable gro...
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Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.
When diagnosing the condition of transformers, classification is generally made into normal and faulty states. However, in some cases, an increase in the amount of hydrogen and hydrocarbons in the insulating oil is ob...
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Home hand rehabilitation for stroke is becoming increasingly important due to logistic and financial challenges. Developing Daily-life Integrated Hand-rehabilitation Products (DIHP) aims to enable the application of a...
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Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies a...
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