An Adaptive Genetic Algorithm of Adjusting Sensor Acquisition Frequency

Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses

The current work aims to design a computational framework based on artificial neural networks (ANNs) and the optimization procedures of global and local search approach to solve the nonlinear dynamics of the spread of COVID-19, i.e., the SEIR-NDC model. The combination of the Genetic algorithm (GA) and active-set approach (ASA), i.e., GA-ASA, works as a global-local search scheme to solve the SEIR-NDC model. An error-based fitness function is optimized through the hybrid combination of the GA-ASA by using the differential SEIR-NDC model and its initial conditions. The numerical performances of the SEIR-NDC nonlinear model are presented through the procedures of ANNs along with GA-ASA by taking ten neurons. The correctness of the designed scheme is observed by comparing the obtained results based on the SEIR-NDC model and the reference Adams method. The absolute error performances are performed in suitable ranges for each dynamic of the SEIR-NDC model. The statistical analysis is provided to authenticate the reliability of the proposed scheme. Moreover, performance indices graphs and convergence measures are provided to authenticate the exactness and constancy of the proposed stochastic scheme.

Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks

In this study, the numerical computation heuristic of the environmental and economic system using the artificial neural networks (ANNs) structure together with the capabilities of the heuristic global search genetic algorithm (GA) and the quick local search interior-point algorithm (IPA), i.e., ANN-GA-IPA. The environmental and economic system is dependent of three categories, execution cost of control standards and new technical diagnostics elimination costs of emergencies values and the competence of the system of industrial elements. These three elements form a nonlinear differential environmental and economic system. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions.

Compensation of Rotary Encoders Using Fourier Expansion-Back Propagation Neural Network Optimized by Genetic Algorithm

The measurement accuracy of the precision instruments that contain rotation joints is influenced significantly by the rotary encoders that are installed in the rotation joints. Apart from the imperfect manufacturing and installation of the rotary encoder, the variations of ambient temperature could cause the angle measurement error of the rotary encoder. According to the characteristics of the 2π periodicity of the angle measurement at the stationary temperature and the complexity of the effects of ambient temperature changes, the method based on the Fourier expansion-back propagation (BP) neural network optimized by genetic algorithm (FE-GABPNN) is proposed to improve the angle measurement accuracy of the rotary encoder. The proposed method, which innovatively integrates the characteristics of Fourier expansion, the BP neural network and genetic algorithm, has good fitting performance. The rotary encoder that is installed in the rotation joint of the articulated coordinate measuring machine (ACMM) is calibrated by using an autocollimator and a regular optical polygon at ambient temperature ranging from 10 to 40 °C. The contrastive analysis is carried out. The experimental results show that the angle measurement errors decrease remarkably, from 110.2″ to 2.7″ after compensation. The mean root mean square error (RMSE) of the residual errors is 0.85″.

Method for Localization Aerial Target in AC Electric Field Based on Sensor Circular Array

The traditional method of using electric field sensors to realize early warning of electric power safety distance cannot measure the distance of dangerous sources. Therefore, aiming at the electric field with a frequency of 50 to 60 Hz (AC electric field), a new method for localization of aerial AC target by the capacitive one-dimensional spherical electric field sensor circular array is studied. This method can directly calculate the distance, elevation, and azimuth of the detector from the dangerous source. By combining the measurement principle of the spherical electric field sensor and the plane circular array theory, a mathematical model for the localization of aerial targets in an AC electric field is established. An error model was established using Gaussian noise and the effects of different layout parameters on the localization error were simulated. Based on mutual interference between sensors, minimum induced charge, and localization error, an optimal model for sensor layout was established, and it was solved by using genetic algorithms. The optimization results show that when the number of sensors is 4, the array radius is 20 cm, and the sensor radius is 1.5 cm, the ranging error is 8.4%. The detector was developed based on the layout parameters obtained from the optimization results, and the localization method was experimentally verified at 10 and 35 kV alarm distances. The experimental results show that when the detector is located at 10 kV alarm distance, the distance error is 0.18 m, the elevation error is 6.8°, and the azimuth error is 4.57°, and when it is located at 35 kV alarm distance, the distance error is 0.2 m, the elevation error is 4.8°, and the azimuth error is 5.14°, which meets the safety distance warning requirements of 10 and 35 kV voltage levels.