2,312 publications from this institution
Cardiechema is a way to reflect cardiovascular disease where the doctor uses a stethoscope to help determine the heart condition with a sound map. In this paper, phonocardiogram (PCG) is used as a diagnostic signal, and a deep learning diagnostic framework is proposed. By improving the architecture and modules, a new transfer learning and boosting architecture is mainly employed. In addition, a segmentation method is designed to improve on the existing signal segmentation methods, such as R wave to R wave interval segmentation and fixed segmentation. For the evaluation, the final diagnostic architecture achieved a sustainable performance with a public PCG database.
This paper proposes a methodology for stabilization of nonlinear discrete-time systems exhibiting bifurcation phenomena under input constraints. The new stabilizing control law is a combination of a bounded feedback chaotification law and a bounded local stabilization law. The proposed control scheme is applied to the control of pathological rhythm in a cardiac model that undergoes a period-doubling bifurcation into alternans.
In this paper, the existence of heteroclinic orbits of Shil'nikov type in a three-dimensional quadratic autonomous system is proved. Four heteroclinic orbits and four critical points together constitute two cycles simultaneously. The dynamical behaviors of the system are also studied.
In the paper, complete synchronization of two chaotic oscillators via unidirectional coupling determined by white noise distribution is investigated. It is analytically proved that chaos synchronization could be achieved with probability one merely via white-noise-based coupling. The established theoretical result supports the observation of an interesting phenomenon that a certain kind of white noise could enhance chaos synchronization between two chaotic oscillators. Furthermore, numerical examples are provided to illustrate some possible applications of the theoretical result.
No abstract is provided for this article.
Over the last two decades, theoretical design and circuit implementation of various chaos generators have been a focal subject of increasing interest due to their promising applications in various real-world chaos-based technologies and information systems. In particular, generating complex multiscroll chaotic attractors via simple electronic circuits has seen rapid development. This article offers an overview of the subject on multiscroll chaotic attractors generation, including some fundamental theories, design methodologies, circuit implementations and practical applications. More precisely, the article first describes some effective design methods using piecewise-linear functions, cellular neural networks, nonlinear modulating functions, circuit component design, switching manifolds, multifolded tori formation, and so on. Based on different approaches, computer simulation and circuit implementation of various multiscroll chaotic attractors are then discussed in detail, with some theoretical proofs and laboratory experiments presented for verification and demonstration. It is then followed by some discussion on potential applications of multiscroll chaotic attractors, including secure and digital communications, synchronous prediction, random bit generation, and so on. The article is finally concluded with some future research outlooks, putting the important subject into engineering perspective.
No abstract is provided for this article.
This paper investigates the problem of estimating synchronization errors and its application to uniform synchronization with an error bound for the general master-slave chaos synchronization scheme via feedback control, which is subjected to disturbances by unknown but bounded channel noise. Based on the Lyapunov function and nonlinear parametric variation techniques, estimation formulae for synchronization errors are derived. It is possible to synchronize two master-slave chaotic systems with a relatively small error bound, even in the case with unknown but bounded noisy disturbances. After the theoretical analysis, some representative examples and their numerical simulations are given for illustration.
If you are given a simple three-dimensional autonomous quadratic system that has only one stable equilibrium, what would you predict its dynamics to be, stable or periodic? Will it be surprising if you are shown that such a system is actually chaotic? Although chaos theory for three-dimensional autonomous systems has been intensively and extensively studied since the time of Lorenz in the 1960s, and the theory has become quite mature today, it seems that no one would anticipate a possibility of finding a three-dimensional autonomous quadratic chaotic system with only one stable equilibrium. The discovery of the new system, to be reported in this Letter, is indeed striking because for a three-dimensional autonomous quadratic system with a single stable node-focus equilibrium, one typically would anticipate non-chaotic and even asymptotically converging behaviors. Although the equilibrium is changed from an unstable saddle-focus to a stable node-focus, therefore the familiar Ši’lnikov homoclinic criterion is not applicable, it is demonstrated to be chaotic in the sense of having a positive largest Lyapunov exponent, a fractional dimension, a continuous broad frequency spectrum, and a period-doubling route to chaos.
This paper provides a new Active Queue Management (AQM) algorithm, called Adaptive Drop-Tail. It is based on the novel idea of providing more buffering capacity as traffic increases in order to stabilize the transmission rate and to increase the network utilization. This is different from other proposals that increase the drop rate as congestion increases. Adaptive Drop-Tail is simple to implement. We demonstrate improvement in performance of this new load adaptive algorithm over several popular AQM algorithms through simulations.
A bifurcation control problem of modifying the amplitudes of limit cycles via feedback is studied. A graphical approach reminiscent to the familiar describing function method is developed for validating the harmonic balance approximations of both the amplitude and the frequency of the system oscillatory outputs, starting from the Hopf bifurcation mechanism. The second, fourth, and sixth-order harmonic balance approximations provide a sequential graphical testing for the convergence of the oscillatory outputs, thereby yielding an accurate approximation of the desired limit cycles of small amplitudes. The knowledge of degenerate Hopf bifurcations and the associate Poincare normal forms are useful for formulating the control objective: to capture small-amplitude oscillatory system outputs and to avoid unstable equilibria or other complicated limit sets. A power system example is included for illustration.
No abstract is provided for this article.
Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision–making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts’ opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems.
An adaptive control algorithm based on the sliding mode principle, equipped with fuzzy logic to handle system modeling uncertainties, is developed in this paper. Along with a neural-network learning scheme that enhances the adaptive control capability, this controller performs satisfactory tracking control for a large class of robot models that contain significant but unknown friction and disturbances. Both mathematical analysis and computer simulation are enclosed for demonstration.
In this paper, a methodology to design a system that robustly synchronizes a master chaotic system from a sampled driving signal is developed. The method is based on the fuzzy Takagi–Sugeno representation of chaotic systems, from which a continuous-time fuzzy observer is designed as the solution of an LMI minimization problem such that the error dynamics have H ∞ disturbance attenuation performance. Then, from the dual-system approach, the fuzzy observer is digitally redesigned such that the performance is maintained for the sampled master system. The effectiveness of the proposed synchronization methodology is finally illustrated via numerical simulations of the chaotic Chen's system.
In this paper, an efficient approach is developed for real‐time global asymptotic stabilization of the chaotic Chen's circuit, as a typical example for chaotic circuit control. Based on a recently introduced methodology of inverse optimal control for nonlinear systems, a very simple stabilization control law, a linear state feedback, is electronically implemented for the desired global asymptotic stabilization. Both Chen's chaotic system and the designed controller are synthesized and realized by analog electronic components, with the aim of evaluating the physical performance of the real‐time control law and demonstrating the practicality of the control method, which is robust to some input uncertainties. Copyright © 2008 John Wiley & Sons, Ltd.
