the traditional approach to identifying paddy leaf diseases in Malaysia's agriculture sector involves visual inspection by farmers or agricultural experts. However, this method heavily relies on human expertise to...
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Event-based or neuro-inspired motor control systems communicate information through pulses, instantaneous bursts of electrical current that encode information very efficiently, as occurs in biological organisms. this ...
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this paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system...
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ISBN:
(数字)9798331530983
ISBN:
(纸本)9798331530990
this paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system within the economy. Accurate forecasting of household finances assists in maintaining positive financial behavior among individuals and the economy. the DeepBoost multi-output regressor proposed in this paper is based on the 1D CNN-ANN and the XGBoost. the proposed model in this paper is compared withthe R 2 , MSE, and MAE since it’s a regression problem. the experimental results reveal that the proposed DeepBoost multi-output regressor has the best application in forecasting the multiple expenditures of households by outperforming the ANN, 1D CNN-ANN, and Random Forest Regressor models. the proposed DeepBoost multi-output regressor evaluated the housing, food, transportation, healthcare, other necessities, childcare, and tax expenditures that had 0.94, 0.98, 0.83, 0.94, 0.97, 0.97, and 0.99 values for the R 2 , 9037.71, 2692.12, 9788, 15077.33, 1373.93, 13629.36, and 1904.52 values for the MSE, and 66.07, 34.05, 73.17, 87.05, 26.25, 78.74, and 29.47 MAE values than the ANN, RFR, and 1D CNN-ANN models.
Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. T...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. these issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. the data is gathered from a dataset on EV charging. the data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). the proposed MSNN-AO is utilized within the MATLAB platform. the proposed MSNN-AO technique is compared withthe existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. the MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. this highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. the MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.
A collision avoidance path planning problem is considered and a simple solution which uses piecewise constant controls generated by discretizing a feasible equilibrium path is presented and investigated.
ISBN:
(纸本)9789898111326
A collision avoidance path planning problem is considered and a simple solution which uses piecewise constant controls generated by discretizing a feasible equilibrium path is presented and investigated.
the paper describes the automation of the shoe grinding process using an industrial robot. One of the major problems of flexible automation using industrial robots is how to avoid joint limitations, singular configura...
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ISBN:
(纸本)9789898111319
the paper describes the automation of the shoe grinding process using an industrial robot. One of the major problems of flexible automation using industrial robots is how to avoid joint limitations, singular configuration and obstacles. this problem can be solved using kinematically redundant robots. Due to the circular shape of the grinding disc, the robot becomes kinematically redundant. this task redundancy was efficiently handled using Virtual mechanism approach, where the tool is described as a serial mechanism.
September 17-19,2011,Qingdao,China http://***/the 5th IEEE international Joint conference on Cybernetics and Intelligent Systems and robotics,automation and Mechatronics(CIS-RAM 2011)will be held in Qingdao,China,duri...
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September 17-19,2011,Qingdao,China http://***/the 5th IEEE international Joint conference on Cybernetics and Intelligent Systems and robotics,automation and Mechatronics(CIS-RAM 2011)will be held in Qingdao,China,during September 17-19,2011.
In this paper we present an experimental test bed for the development and evaluation of control systems for unmanned helicopters. the test bed consists of a small unmanned helicopter, mounted on a flying stand that pe...
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ISBN:
(纸本)9789898111319
In this paper we present an experimental test bed for the development and evaluation of control systems for unmanned helicopters. the test bed consists of a small unmanned helicopter, mounted on a flying stand that permits all possible movements but prevents the helicopter from damaging or crashing. A fuzzy controller is developed in MATLAB and tested in the helicopter using the test bed. the controller is able to perform hovering and altitude control. Experimental results are presented for various test cases.
this paper describes a robust localization system, similar to the used by the teams participating in the Robocup Small size league (SLL). the system, developed in Object Pascal, allows real time localization and contr...
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ISBN:
(纸本)9789898111319
this paper describes a robust localization system, similar to the used by the teams participating in the Robocup Small size league (SLL). the system, developed in Object Pascal, allows real time localization and control of an autonomous omnidirectional mobile robot. the localization algorithm is done resorting to odometry and global vision data fusion, applying an extended Kalman filter, being this method a standard approach for reducing the error in a least squares sense, using measurements from different sources.
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