• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2028-2041
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• 2019

Compressed sensing (CS) is a new theory. With regard to the sparse signal, an exact reconstruction can be obtained with sufficient CS measurements. Nevertheless, in practical applications, the transform coefficients of many signals usually have weak sparsity and suffer from a variety of noise disturbances. What's worse, most existing classical algorithms are not able to effectively solve this issue. So we proposed an efficient algorithm based on smoothed ${\ell}_0$ norm for sparse signal reconstruction. The direct ${\ell}_0$ norm problem is NP hard, but it is unrealistic to directly solve the ${\ell}_0$ norm problem for the reconstruction of the sparse signal. To select a suitable sequence of smoothed function and solve the ${\ell}_0$ norm optimization problem effectively, we come up with a generalized approximate function model as the objective function to calculate the original signal. The proposed model preserves sharper edges, which is better than any other existing norm based algorithm. As a result, following this model, extensive simulations show that the proposed algorithm is superior to the similar algorithms used for solving the same problem.

• Management Science and Financial Engineering
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• 제13권1호
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• pp.35-53
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• 2007

We consider a(worst-case) robust optimization version of the Economic Order Quantity(EOQ) model. Order setup costs and inventory carrying costs are assumed to have uncertainty in their values, and the uncertainty description of the two parameters is supposed to be given by an ellipsoidal representation. A genetic algorithm combined with Monte Carlo simulation is proposed to approximate the ellipsoidal representation. The objective function of the model under ellipsoidal uncertainty description is derived, and the resulting problem is solved by another genetic algorithm. Computational test results are presented to show the performance of the proposed method.

• 한국전산구조공학회논문집
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• 제16권4호
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• pp.431-437
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• 2003

본 연구에서는 순차 설계영역 (SDD： sequential Design Domain) 개념을 사용한 GUI(Graphic User Interface)환경 프로그램을 개발하였다. 본 프로그램은 상용프로그램인 ANSYS와 최적설계 프로그램인 PLBA(Pshenichny-Lim-Belegundu-Arora)를 연결하고 비주얼 베이직을 이용하여 GUI환경에서 사용자가 초기값과 입력파일을 작성하고 결과를 확인할 수 있도록 하였다. 프로그램의 신뢰도를 검증하기 위해서 3부재 및 5부재 트러스 구조물을 수치예제로 선정하여 해석하였다.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.77-85
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• 2002

The statistical testing methods have been widely recognized to determine the plain and cipher texts. In fact, the randomness for a sequence from an encryption algorithm is necessary to guarantee security and reliance of cipher algorithm. Thus, the statistical randomness tests are used to discover cipher text. In this paper, we would provide the critical value for an approximate entropy test by estimating the nonlinear regression equation when the number of sequence and the level of significance are given. Thus, we can discern plan and cipher text for real problem with given number of sequence and the level of significance. Also, we confirm the fitness of the estimated critical values from the rate of success for plain or cipher text.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (C)
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• pp.171-176
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• 2007

Parsing 작업의 결과인 Parse Tree 정보는 문장에 관한 구조적 정보를 가지고 있는 Tree 정보로 이 정보를 이용하여 정보 검색에 활용하는 알고리즘을 제안한다. 제안하는 알고리즘은 XML 기술과 스트링 매칭 기법을 이용하였으며, 사용한 스트링 매칭 기법은 Approximate String Matching 기법이다. Query 정보와 문서 정보를 Parsing하여 얻은 Parse Tree를 XML 형태의 정보로 변환한 후, 두 정보를 가지고 Approximate String Matching 기법을 적용하여 Query 정보와 문서 정보 간의 유사도를 계산한다. 제안하는 알고리즘의 장점은 구조 기반의 정보 검색 기능이 가능하고 비슷한 정보에 대한 검색 기능이 가능하며 비슷한 구조에 대한 검색 기능이 가능하다는 것이다.

• Journal of applied mathematics & informatics
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• 제26권3_4호
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• pp.793-809
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• 2008

The aim of this article is focused on providing numerical solutions for system of second order robot arm problem using the RK-eight stage seventh order limiting formulas. The parameters governing the arm model of a robot control problem have also been discussed through RK-eight stage seventh order limiting algorithm. The precised solution of the system of equations representing the arm model of a robot has been compared with the corresponding approximate solutions at different time intervals. Results and comparison show the efficiency of the numerical integration algorithm based on the absolute error between the exact and approximate solutions. Based on the numerical results a thorough comparison is carried out between the numerical algorithms.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1858-1859
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• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The gradient descendant method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

• 한국생산제조학회지
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• 제24권2호
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• pp.192-197
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• 2015

In many fields, the importance of reducing weight is increasing. A product should be designed such that it is profitable, by lowering costs and exhibiting better performance than other similar products. In this study, the mass and deflection of steel structures have to be reduced as objective functions under constraint conditions. To reduce computational analysis time, central composite design(CCD) and D-Optimal are used in design of experiments(DOE). The accuracy of approximate models is evaluated using the $R^2$ value. In this study, the objective functions are multiple, so the non-dominant sorting genetic algorithm(NSGA-II), which is highly efficient, is used for such a problem. In order to verify the validity of Pareto solutions, CAE results and Pareto solutions are compared.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.142-147
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• 1996

In this paper, we describe the algorithm which controls an unknown nonlinear system with multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate unknown nonlinear function on the nonlinear system by using of multilayer neural network. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. The whole control system constitutes controller using feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• 한국정밀공학회지
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• 제17권5호
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• pp.84-92
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• 2000

Recently, the progress of industrialization has been taken concern of material handling automation. So for, the conveyor belt has been popular for material handling. However, this system has many disadvantages such as the space, cost, etc. In this paper, a new navigation algorithm using fuzzy is introduced. The mobile robot follows a line installed on the roads. These informations are inputted with three approximate sensors. These obtained informations are analyzed with fuzzy control technique fur autonomous steering. Therefore, unlike existing systems, high reliability is guaranteed under bad environment conditions. The installation and maintenance of a line is easily made at lower cost. This developed mobile robot can be applied to material handling automation in manufacturing system, hospital, inter-office document del ivory.