109 research outputs found
The neural network-based control system of direct current motor driver
This article aims to propose an adaptive control system for the direct current motor driver based on the neural network. The control system consists of two neural networks: the first neural network is used to estimate the speed of the direct current motor and the second neural network is used as a controller. The plant in this research includes motor and the driver circuit so it is a complex model. It is difficult to determine the exact parameters of the plant so it is difficult to build the controller. To solve the above difficulties, the author proposes an adaptive control system based on the neural network to control the plant reach the high quality in the case of unknowing the parameters of the plant. The results are that the control quality of the system is very good, the response speed always follows the desired speed and the transition time is small. The simulation results of the neural network control system are shown and compared with that of a PID controller to demonstrate the advantages of the proposed method
Fractional-order sliding mode controller for the two-link robot arm
In this paper, the author proposes a sliding mode controller with the fractional-order for the two-link robot arm. Firstly, the model and dynamic equations of the two-link robot arm are presented. Based on these equations, the author builds the controller for each joint of the robot. The controller is a sliding mode controller with its order is not an integer value. The task of the controller is to adjust the torques acted on the joints in order for the angular coordinates of each link to coincide with the desired values. The effectiveness of the proposed control system is demonstrated through Matlab-Simulink software. The robot model and controller are built to investigate the system quality. The results show that the quality of the control system is very high: there is not the chattering phenomenon of torques, the response angles of each link quickly reach the desired values, and the static error equal to zero
The Neural Network-Combined Optimal Control System of Induction Motor
This research aims to propose the optimal control method combined with the neuron network for an induction motor. In the proposed system, the induction motor is a nonlinear object which is controlled at each working point. At these working-points, the state equation of the induction motor is linear, so it is possible to apply the linear quadratic regular algorithm for the induction motor. Therefore, the parameters of the state feedback controller are the functions. The output-input relationships of these functions are set through the neural network. The numerical simulation results show that the quality of the control system of the induction motor is very high: The response speed always follows the desired speed with the short transition time and the small overshoot. Furthermore, the system is robust in the case of changing the load torque, and the parameters of the induction motor are incorrectly define
Sliding mode control-based system for the two-link robot arm
In this research, the author presents the model of the two-link robot arm and its dynamic equations. Based on these dynamic equations, the author builds the sliding mode controller for each joint of the robot. The tasks of the controllers are controlling the Torque in each Joint of the robot in order that the angle coordinates of each link coincide with the desired values. The proposed algorithm and robot model are built on Matlab-Simulink to investigate the system quality. The results show that the quality of the control system is very high: the response angles of each link quickly reach the desired values, and the static error equal to zero
Fuzzy-proportional-integral-derivative-based controller for object tracking in mobile robots
This paper aims at designing and implementing an intelligent controller for the orientation control of a two-wheeled mobile robot. The controller is designed in LabVIEW and based on analyzed image parameters from cameras. The image program calculates the distance and angle from the camera to the object. The fuzzy controller will get these parameters as crisp input data and send the calculated velocity as crisp output data to the right and left wheel motor for the robot tracking the target object. The results show that the controller gives a fast response and high reliability and quickly carries out data recovery from system faults. The system also works well in the uncertainties of process variables and without mathematical modeling
The maximum power point tracking based-control system for small-scale wind turbine using fuzzy logic
This paper presents the research on small-scale wind turbine systems based on the Maximum Power Point Tracking (MPPT) algorithm. Then propose a new structure of a small-scale wind turbine system to simplify the structure of the system, making the system highly practical. This paper also presented an MPPT-Fuzzy controller design and proposed a control system using the wind speed sensor for small-scale wind turbines. Systems are simulated using Matlab/Simulink software to evaluate the feasibility of the proposed controller. As a result, the system with the MPPT-Fuzzy controller has much better quality than the traditional control system
The Determinants of self-medication: evidence from urban Vietnam
This study examines the primary determinants of self-medications among urban citizens in Ho Chi Minh City, Vietnam. To achieve the research objective, the questionnaire is designed to elicit the respondents’ necessary information using in-depth personal interviews. Employing logistic models the paper finds that the probability of self-medication is positively associated with the respondents’ high school degree or vocational certificate, married status, and income while it is negatively related to employed status, the number of children, the geographical distance from home to the nearest hospital, doing exercise, and living in a central region. Meanwhile, using Poisson models the paper finds that the frequency of self-medication is positively associated with the respondents’ high school and vocational, married, income, and chronic disease while the frequency of self-medication is adversely related to male, employed, children number, distance, being close to health professional and central areas
Approximation of mild solutions of the linear and nonlinear elliptic equations
In this paper, we investigate the Cauchy problem for both linear and
semi-linear elliptic equations. In general, the equations have the form
where is a positive-definite, self-adjoint operator with
compact inverse. As we know, these problems are well-known to be ill-posed. On
account of the orthonormal eigenbasis and the corresponding eigenvalues related
to the operator, the method of separation of variables is used to show the
solution in series representation. Thereby, we propose a modified method and
show error estimations in many accepted cases. For illustration, two numerical
examples, a modified Helmholtz equation and an elliptic sine-Gordon equation,
are constructed to demonstrate the feasibility and efficiency of the proposed
method.Comment: 29 pages, 16 figures, July 201
A hierarchical architecture for increasing efficiency of large photovoltaic plants under non-homogeneous solar irradiation
Under non-homogeneous solar irradiation, photovoltaic (PV) panels receive different solar irradiance, resulting in a decrease in efficiency of the PV generation system. There are a few technical options to fix this issue that goes under the name of mismatch. One of these is the reconfiguration of the PV generation system, namely changing the connections of the PV panels from the initial configuration to the optimal one. Such technique has been widely considered for small systems, due to the excessive number of required switches. In this paper, the authors propose a new method for increasing the efficiency of large PV systems under non-homogeneous solar irradiation using Series-Parallel (SP) topology. In the first part of the paper, the authors propose a method containing two key points: a switching matrix to change the connection of PV panels based on SP topology and the proof that the SP-based reconfiguration method can increase the efficiency of the photovoltaic system up to 50%. In the second part, the authors propose the extension of the method proposed in the first part to improve the efficiency of large solar generation systems by means of a two-levels architecture to minimize the cost of fabrication of the switching matrix
ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese
Social media processing is a fundamental task in natural language processing
with numerous applications. As Vietnamese social media and information science
have grown rapidly, the necessity of information-based mining on Vietnamese
social media has become crucial. However, state-of-the-art research faces
several significant drawbacks, including imbalanced data and noisy data on
social media platforms. Imbalanced and noisy are two essential issues that need
to be addressed in Vietnamese social media texts. Graph Convolutional Networks
can address the problems of imbalanced and noisy data in text classification on
social media by taking advantage of the graph structure of the data. This study
presents a novel approach based on contextualized language model (PhoBERT) and
graph-based method (Graph Convolutional Networks). In particular, the proposed
approach, ViCGCN, jointly trained the power of Contextualized embeddings with
the ability of Graph Convolutional Networks, GCN, to capture more syntactic and
semantic dependencies to address those drawbacks. Extensive experiments on
various Vietnamese benchmark datasets were conducted to verify our approach.
The observation shows that applying GCN to BERTology models as the final layer
significantly improves performance. Moreover, the experiments demonstrate that
ViCGCN outperforms 13 powerful baseline models, including BERTology models,
fusion BERTology and GCN models, other baselines, and SOTA on three benchmark
social media datasets. Our proposed ViCGCN approach demonstrates a significant
improvement of up to 6.21%, 4.61%, and 2.63% over the best Contextualized
Language Models, including multilingual and monolingual, on three benchmark
datasets, UIT-VSMEC, UIT-ViCTSD, and UIT-VSFC, respectively. Additionally, our
integrated model ViCGCN achieves the best performance compared to other
BERTology integrated with GCN models
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