1,256 publications from this institution
Design of a low-cost embedded iris recognition system is described in this paper. Firstly, we develop a simple and effective iris image acquisition unit, which is cheap and easy to use. This is achieved by both of hardware design and image evaluation algorithm development. Secondly, the iris recognition algorithm is introduced, including iris segmentation, image normalization, feature extraction, and code matching. The algorithm implementation architecture is based on an embedded dual-core processor platform, Texas Instruments TMS320DM6446 evaluation module (Davinci), which contains an ARM core and a DSP core in one chip. Thirdly, the evaluation experiments are performed on the established iris database, and the experimental results suggest that the system shows good performance both on accuracy and efficiency.
This paper studies the problem of fault detection for linear discrete time-varying systems with multiplicative noise in finite-horizon, where our main object is to provide an optimal fault detection filter (FDF) design scheme such that stochastic sensitivity/robustness ratio for fault diagnosis is maximized in the sense of probability 1. An operator-aided optimization approach and a generalized FDF are proposed such that solutions to the filter design issues are derived in the operator forms. The relationships among the deduced solutions are explicitly revealed via the proposed operator-aided methodology. The parameter matrices of the filter are computed in an analytical way by solving some recursive matrix equations. It is shown that the addressed approaches establish an operator-based framework of optimal FDF design for some categories of linear discrete-time systems. An example is given to illustrate the efficacy of our algorithms.
This paper presents mathematical logarithmic, statistical linear regression, and neural models capable of predicting maximum A-weighed noise level (L A,max) for the Tehran–Karaj express train. The models have been developed upon the basis of the measurements from sampling locations at distances of 25m, 45m, and 65m from the centreline of the track and at a height of 1.5m. In the next step, the predictive capability of the models have been tested on the data associated with the sampling locations, situated, respectively at distances of 35 and 55m from the centreline of the track at a height of 1.5m. The non-parametric tests i.e. two-related samples Wilcoxon, and two-independent samples Kolmogorov–Smirnov, carried out, respectively for training and testing steps, indicate satisfactory results. In the final step the non-parametric k-related samples Friedman test detects no significant differences amongst the absolute testing set error of the models.
Continuous-time semi-Markovian jump neural networks (semi-MJNNs) are those MJNNs whose transition rates are not constant but depend on the random sojourn time. Addressing stochastic synchronization of semi-MJNNs with time-varying delay, an improved stochastic stability criterion is derived in this paper to guarantee stochastic synchronization of the response systems with the drive systems. This is achieved through constructing a semi-Markovian Lyapunov-Krasovskii functional together as well as making use of a novel integral inequality and the characteristics of cumulative distribution functions. Then, with a linearization procedure, controller synthesis is carried out for stochastic synchronization of the drive-response systems. The desired state-feedback controller gains can be determined by solving a linear matrix inequality-based optimization problem. Simulation studies are carried out to demonstrate the effectiveness and less conservatism of the presented approach.
This paper deals with a computational optimization approach to the problem of state-feedback control design for a wind turbine system. The first step of the study is to develop a reduced order model for the system by considering the most important physical phenomena of aerodynamics and structural dynamics. Moreover, the behavior of the system can be influenced by the coupled dynamics between the tower motions and the blade pitch and turbine speed which can cause instabilities in the control loops in the worst case. By using a suitable wavelet funcation, called Haar functions, a recursive computational procedure is established for finding the system dynamics approximately by solving only algebraic equations instead of solving the Riccati differential. Simulation results are given to illustrate the usefulness of the proposed control methodology.
The global dissipative and multipeakon dissipative behavior of the two-component Camassa-Holm shallow water system after wave breaking was studied in this paper. The underlying approach is based on a skillfully defined characteristic and a set of newly introduced variables which transform the original system into a Lagrangian semilinear system. It is the transformation, together with the associated properties, that allows for the continuity of the solution beyond collision time to be established, leading to a uniquely global dissipative solution, which constructs a semigroup, and the multipeakon dissipative solution.
Convolutional neural networks (CNNs) are considered black boxes due to their robust nonlinear fitting capability. In the context of fault diagnosis for rotating machinery, it may happen that a standard CNN makes a final decision based on a mixture of significant and insignificant features, therefore, it is required to establish a trustworthy intelligence fault diagnosis model with the controllable feature learning capability to identify fault types. In this paper, an explainable intelligence fault diagnosis framework is proposed to recognize the fault signals, using data obtained through short-time Fourier transformation, which is easily modified from a standard CNN. The post hoc explanation method is used to visualize the features the model learned from a signal. The experimental results show that the proposed explainable intelligence fault diagnosis framework provides 100% testing accuracy and visualizations, the Average Drop and the Average Increase from a classification activation mappings method demonstrate the interpretability of the proposed framework.
In this paper, the problem of fuzzy dynamic sliding mode control design is investigated for a class of disturbed systems. Specifically, the fuzzy controller is constructed based on one feedback signal to estimate the unknown nonlinear terms and to develop the sliding mode control according to the fuzzy rules. Furthermore, it possesses the characteristic of simplicity in design and effectiveness in attenuating chattering. Finally, a numerical example is included to demonstrate the effectiveness and advantage of the proposed method.
No abstract is provided for this article.
This paper consists of designing fuzzy and PID controllers for controlling the vehicle speed. The dynamic of the system is modeled to provide a transfer function for the plant. Fuzzy and PID controller are designed for linear model. The external disturbances such road grade is considered to stabilizing the system. Both controllers are modeled using MATLAB Simulink software. Finally, a comparative assessment of each simulated result is done based on the response characteristics.
The paper is concerned with the application of wavelet-based neural networks for optimal control of robotic manipulators motion. The model of robotic manipulators with regard to frictions and disturbances is nonlinear and uncertain. Optimal control law is found by the optimization of the Hamilton–Jacobi–Bellman (H-J-B) equation and it shows how wavelet-based neural networks can overcome nonlinearities through optimization without preliminary off-line learning phase. The neural network is learned as on-line and an adaptive learning algorithm is derived from the Lyapunov theory. This is so that both tracking stability and error convergence of the estimation for the nonlinear function can be guaranteed in the closed-loop system. The Lyapunov function for the nonlinear analysis is derived from the user input in terms of a specified quadratic performance index. Simulation results on a three-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances. Furthermore, it is shown that the tracking error for wavelet neural networks is less than conventional neural networks.
In response to the increasingly severe traffic congestion problem, this paper proposes a novel method based on Double Deep Q-Learning Network to enhance the performance of adaptive traffic signal control agents in alleviating traffic congestion and delays. By designing a novel state space model and reward function, the proposed method can minimize vehicle queue lengths and reduce vehicle delay duration when dealing with complex intersections or segments with significant traffic fluctuations. To evaluate the performance of this method, the paper utilizes the Simulation of Urban MObility software to set up environments for complex intersections. Simulation results demonstrate that compared to previous works and current mainstream algorithms, the proposed method can efficiently control signals in complex traffic environments, effectively addressing congestion and improving traffic efficiency.
This article presents a self-triggered bipartite fault-tolerant consensus control scheme in a fixed-time stability sense for multiagent systems (MASs) with function constraints on states. Neural networks are introduced to identify unknown nonlinearities, thereby relaxing constraints on unknown functions. Then, based on time-varying barrier Lyapunov functions including time and previous states, all states of the considered MASs are constrained to given ranges. In addition, to improve the utilization of system transmission resources, a self-triggered control method in which the next triggering time can be computed according to the current information of the controller is proposed. By applying the Lyapunov theory, the designed self-triggered controller can ensure that all signals containing consensus tracking errors are fixed-time bounded under a given communication topology, and the Zeno phenomenon is successfully avoided. Finally, a practical example is provided to testify the feasibility of the developed control scheme.
In this work, the cruise control problem of high-speed trains’ movements is investigated. Both cases of a single high-speed train and multiple high-speed trains are under consideration. Different with most existing studies where the centralized control or the decentralized control methods are adopted based on a single point mass model of the train, in this paper, a distributed control mechanism is proposed by virtue of the graph theory, and the high-speed train’s model is built as a cascade of point masses connected by flexible couplers. For a single high-speed train, the neighboring cars interact through the coupling force with each other, which can be described by a connected topological graph by regarding each car as a node. Besides, the speed information communication among the cars is considered to be described by another directed topological graph. A distributed control strategy is then developed, with which all the cars of a train track a desired speed asymptotically and the neighboring cars keep a safety distance from each other. For the multiple high-speed trains running on a railway line, the in-train force interaction topology and the speed information communication topology of all the trains are more complex than those of a single train. A new cluster consensus technique is developed, by which a distributed control law is designed. Under the control law, the trains can track the desired speeds asymptotically, the headway distance between adjacent trains and the distance between the neighboring cars of a train can be kept in appropriate ranges. Finally, simulations are provided to illustrate the effectiveness of the obtained theoretical results.
Information is crucial to the function of a democratic society where well-informed citizens can make rational political decisions. While in the past political entities primarily utilized newspapers and later radio and television to inform the public, the political arena has transformed into a more complex structure with the rise of the Internet and online social media. Now, more than ever, people express themselves online while mainstream news agencies attempt to utilize the power of the Internet to spread their articles as much as possible. To grasp the political coexistence of mainstream media and online social media, in this paper, we analyze these two sources of information in the context of the U.S. 2020 presidential election. In particular, we collected data during the 2020 Democratic Party presidential primaries pertaining to the candidates, and, by analyzing this data, we highlight similarities and differences between these two main types of sources, detect the potential impact they have on each other, and understand how this impact relationship can change over time.
This paper proposes an intelligent method based on artificial neural networks (ANNs) to detect bearing defects of induction motors. In this method, the vibration signal passes through removing non-bearing fault component (RNFC) filter, designed by neural networks, in order to remove its non-bearing fault components, and then enters the second neural network that uses pattern recognition techniques for fault classification. Four different categories include; healthy, inner race defect, outer race defect, and double holes in outer race are investigated. Compared to the regular fault detection methods that use frequency-domain features, the proposed method is based on analyzing time-domain features which needs less computational effort. Moreover, machine and bearing parameters, and the vibration signal spectrum distribution are not required in this method. It is shown that better results are achieved when the filtered component of the vibration signal is used for fault classification rather than common methods that use directly vibration signal. Experimental results on three-phase induction motor verify the ability of the proposed method in fault diagnosis despite low quality (noisy) of measured vibration signal.
This paper investigates the problems of stability and <svg style="vertical-align:-3.27605pt;width:12.15px;" id="M2" height="17.424999" version="1.1" viewBox="0 0 12.15 17.424999" width="12.15" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"> <g transform="matrix(.017,-0,0,-.017,.625,12.725)"><use xlink:href="#x1D459"/></g> <g transform="matrix(.012,-0,0,-.012,5.2,16.8)"><use xlink:href="#x31"/></g> </svg>-gain controller design for positive switched systems with time-varying delays via delta operator approach. The purpose is to design a switching signal and a state feedback controller such that the resulting closed-loop system is exponentially stable with <svg style="vertical-align:-3.27605pt;width:12.15px;" id="M3" height="17.424999" version="1.1" viewBox="0 0 12.15 17.424999" width="12.15" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"> <g transform="matrix(.017,-0,0,-.017,.625,12.725)"><use xlink:href="#x1D459"/></g> <g transform="matrix(.012,-0,0,-.012,5.2,16.8)"><use xlink:href="#x31"/></g> </svg>-gain performance. Based on the average dwell time approach, a sufficient condition for the existence of an <svg style="vertical-align:-3.27605pt;width:12.15px;" id="M4" height="17.424999" version="1.1" viewBox="0 0 12.15 17.424999" width="12.15" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"> <g transform="matrix(.017,-0,0,-.017,.625,12.725)"><use xlink:href="#x1D459"/></g> <g transform="matrix(.012,-0,0,-.012,5.2,16.8)"><use xlink:href="#x31"/></g> </svg>-gain controller for the considered system is established by constructing an appropriate copositive type Lyapunov-Krasovskii functional in delta domain. Moreover, the obtained conditions can unify some previously suggested relevant methods in the literature of both continuous- and discrete-time systems into the delta operator framework. Finally, a numerical example is presented to explicitly demonstrate the effectiveness and feasibility of the proposed method.
