• Journal of the Chungcheong Mathematical Society
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• v.37 no.2
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• pp.75-80
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• 2024

In this paper, we study some dynamics of the uniform limits of sequences in dynamical systems on a noncompact metric space. We show that if a sequence of homeomorphisms on a noncompact metric space has the uniform ergodic shadowing property, then the uniform limit also has the ergodic shadowing property. Then we apply this result to nonwandering maps.

• Journal for History of Mathematics
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• v.18 no.2
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• pp.107-116
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• 2005

A suspension bridge is an example of a nonlinear dynamical system, especially systems with the so called jumping nonlinearity. The fact that we deal with a serious and topical problem is demonstrated for example by the collapse of the Tacoma Narrow suspension bridge. So it would be very contributive to determine under what conditions a similar situation cannot occur and find out safe parameters of the bridge construction. In this paper, we show various possibilities how to model the behaviour of suspension bridge. Then we introduce our own results concerning existence and uniqueness of time-periodic solutions.

• Nuclear Engineering and Technology
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• v.27 no.6
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• pp.845-852
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• 1995

We investigate the concurrent solution of differential equations by the waveform relaxation (WR) method, an iterative method for analyzing linear and nonlinear dynamical systems in the time do-main. The method, at each iteration, decomposes the dynamical system into several subsystems, each of which is analyzed for the entire given time interval. The method, when efficiently implemented, results in algorithms with a highly parallelizable concurrent fraction. In this paper, the waveform relaxation method is introduced and applied to two types of reactor dynamics problems. It is concluded that the U method can be applied to reactor dynamics equations, but that its parallel performance on the KMRR dynamics is only modest.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.327-335
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• 2003

Cellular Automata is discrete dynamical systems which natural phenomena may be specified completely in terms of local relation. In this Paper we Propose noise removal and edge detection algorithm using a Potts Automata which is based on Cellular Automata. The proposed method is aimed to locally increase or decrease the differences in gray level values between pixel of the image without loss of the main characteristics of the image. The dynamical behavior of these automata is determined by Lyapunov operators for sequential and parallel update. We have found that proposed automata rules Present very fast convergence to fixed points, stability in front of random noisy images. Based on the experimental results we discuses the advantage and efficiency.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.285-288
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• 1998

A least-squares identification method is studied that estimates a finite number of coefficients in the series expansion of a transfer function, where the expansion is in terms of recently introduced generalized basis functions, We will expand and generalize the orthogonal functions as basis functions for dynamical system representations. To this end, use is made of balanced realizations as inner transfer functions. The orthogonal functions can be considered as generalizations of, for example, the pulse functions, Laguerre functions, and Kautz functions, and give rise to an alternative series expansion of rational transfer functions. We show that the Laplace transform of the expansion for some sets$\Psi_{\kappa}(Z)$ is equivalent to a series expansion . Techniques based on this result are presented for obtaining the coefficients $c_{n}$ as those of a series. One of their important properties is that, if chosen properly, they can substantially increase the speed of convergence of the series expansion. This leads to accurate approximate models with only a few coefficients to be estimated. The set of Kautz functions is discussed in detail and, using the power-series equivalence, the truncation error is obtained.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.451-454
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• 1998

This paper proposes a new method of Model Predictive Control (MPC) of nonlinear process by us-ing the measured Volterra kernels as the nonlinear model. A nonlinear dynamical process is usually de-scribed as Volterra kernel representation, In the authors' method, a pseudo-random M-sequence is ar plied to the nonlinear process, and its output is measured. Taking the crosscorrelation between the input and output, we obtain the Volterra kernels up to 3rd order which represent the nonlinear characteristics of the process. By using the measured Volterra kernels, we can construct the nonlinear model for MPC. In applying Model Predictive Control to a nonlinear process, the most important thing is, in general, what kind of nonlinear model should be used. The authors used the measured Volterra kernels of up to 3rd order as the process model. The authors have carried out computer simulations and compared the simulation results for the linear model, the nonlinear model up to 2nd Volterra kernel, and the nonlinear model up to 3rd order Vol-terra kernel. The results of computer simulation show that the use of Valterra kernels of up to 3rd order is most effective for Model Predictive Control of nonlinear dynamical processes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.87-94
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• 2019

A wireless sensor network is emerging technology and intelligent wireless communication paradigm that is dynamically aware of its surrounding environment. It is also able to respond to it in order to achieve reliable and efficient communication. The dynamical cognition capability and environmental adaptability rely on organizing dynamical networks effectively. However, optimally clustering the cognitive wireless sensor networks is an NP-complete problem. The objective of this paper is to develop an optimal sensor network design for maximizing the performance. This proposed Ranking Artificial Bee Colony (RABC) is developed based on Artificial Bee Colony (ABC) with ranking strategy. The ranking strategy can make the much better solutions by combining the best solutions so far and add these solutions in the solution population when applying ABC. RABC is designed to adapt to topological changes to any network graph in a time. We can minimize the total energy dissipation of sensors to prolong the lifetime of a network to balance the energy consumption of all nodes with robust optimal solution. Simulation results show that the performance of our proposed RABC is better than those of previous methods (LEACH, LEACH-C, and etc.) in wireless sensor networks. Our proposed method is the best for the 100 node-network example when the Sink node is centrally located.

• Steel and Composite Structures
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• v.31 no.5
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• pp.489-502
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• 2019

This article presents a unified mathematical model to investigate free and forced vibration responses of perforated thin and thick beams. Analytical models of the equivalent geometrical and material characteristics for regularly squared perforated beam are developed. Because of the shear deformation regime increasing in perforated structures, the investigation of dynamical behaviors of these structures becomes more complicated and effects of rotary inertia and shear deformation should be considered. So, both Euler-Bernoulli and Timoshenko beam theories are proposed for thin and short (thick) beams, respectively. Mathematical closed forms for the eigenvalues and the corresponding eigenvectors as well as the forced vibration time response are derived. The validity of the developed analytical procedure is verified by comparing the obtained results with both analytical and numerical analyses and good agreement is detected. Numerical studies are presented to illustrate effects of beam slenderness ratio, filling ratio, as well as the number of holes on the dynamic behavior of perforated beams. The obtained results and concluding remarks are helpful in mechanical design and industrial applications of large devices and small systems (MEMS) based on perforated structure.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.85-94
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• 2023

Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.20-23
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• 2020

This research is to design an effective prefetching method required for DRAM-PCM hybrid main memory systems especially used for big data applications and massive-scale computing environment. Conventional prefetchers perform well with regular memory access patterns. However, workloads such as graph processing show extremely irregular memory access characteristics and thus could not be prefetched accurately. Therefore, this research proposes an efficient dynamical prefetching algorithm based on the regression method. We have designed an intelligent prefetch engine that can identify the characteristics of the memory access sequences. It can perform regular, linear regression or polynomial regression predictive analysis based on the memory access sequences' characteristics, and dynamically determine the number of pages required for prefetching. Besides, we also present a DRAM-PCM hybrid memory structure, which can reduce the energy cost and solve the conventional DRAM memory system's thermal problem. Experiment result shows that the performance has increased by 40%, compared with the conventional DRAM memory structure.