• International Journal of Aeronautical and Space Sciences
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• 제2권2호
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• pp.28-38
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• 2001

A numerical method for the assessment and correction of tunnel wall interference effects on forced-oscillation testing is presented. The method is based on the wall pressure signature method using computed wall pressure distributions. The wall pressure field is computed using unsteady three-dimensional full Navier-Stokes solver for a 70-degree pitching delta wing in a wind tunnel. Approximately-factorized alternate direction implicit (AF-ADI) scheme is advanced in time by solving block tri-diagonal matrices. The algebraic Baldwin-Lomax turbulence, model is included to simulate the turbulent flow effect. Also, dual time sub-iteration with, local, time stepping is implemented to improve the convergence. The computed wall pressure field is then imposed as boundary conditions for Euler re-simulation to obtain the interference flow field. The static computation shows good agreement with experiments. The dynamic computation demonstrates reasonable physical phenomena with a good convergence history. The effects of the tunnel wall in upwash and blockage are analyzed using the computed interference flow field for several reduced frequencies and amplitudes. The corrected results by pressure signature method agree well with the results of free air conditions.

• 경영과학
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• 제23권1호
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• pp.63-74
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• 2006

Decision tree induction algorithm is one of the most widely used methods in classification problems. However, they could be trapped into a local minimum and have no reasonable means to escape from it if tree algorithm uses top-down search algorithm. Further, if irrelevant or redundant features are included in the data set, tree algorithms produces trees that are less accurate than those from the data set with only relevant features. We propose a hybrid algorithm to generate decision tree that uses genetic programming with sequentially selected features. Correlation-based Feature Selection (CFS) method is adopted to find relevant features which are fed to genetic programming sequentially to find optimal trees at each iteration. The new proposed algorithm produce simpler and more understandable decision trees as compared with other decision trees and it is also effective in producing similar or better trees with relatively smaller set of features in the view of cross-validation accuracy.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.297-301
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• 2005

In this paper, we propose a method to estimate camera motion parameter based on invariant point features. Typically, feature information of image has drawbacks, it is variable to camera viewpoint, and therefore information quantity increases after time. The LM(Levenberg-Marquardt) method using nonlinear minimum square evaluation for camera extrinsic parameter estimation also has a weak point, which has different iteration number for approaching the minimal point according to the initial values and convergence time increases if the process run into a local minimum. In order to complement these shortfalls, we, first propose constructing feature models using invariant vector of geometry. Secondly, we propose a two-stage calculation method to improve accuracy and convergence by using homography and LM method. In the experiment, we compare and analyze the proposed method with existing method to demonstrate the superiority of the proposed algorithms.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -1
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• pp.46-49
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• 2000

An iterative deblocking algorithm for DCT-compressed images using two-dimensional FIR filters adapted for local directionality of each block, is proposed. First, we introduce a set of simple lowpass filters, which are adapted for edges of different angles. In conventional deblocking methods based on lowpass-filtering and convex projections, a single filter is applied to a whole image. In the proposed method, on the other hand, a suitable filter is chosen out of the directional filters designed previously in every subimage (typically $8{\times}8$ block). Experimental results indicate that adaptive filtering improves PSNR at each iteration.

• 대한전기학회논문지
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• 제35권8호
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• pp.309-315
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• 1986

We proposed a preliminary Data Flow Machine Model(DNL1) operating on the basis of Node Label. In this model, all the PMs(Processing Modules) were synchronized with the content of LC(Level Counter) and were not implemented dy the processing cability on conditional nodes. This paper presents an architecture of a concurrent multiprocessor system which was developed from DNL1 with two additional types of memories, CF(Control Flag) and ETF (Enabled Token Flag). The CF memory holds the control condition flag ('1' or '0') to be referenced to when a node is fired and the ETF represents the firability of a certain node. Firable nodes are fetched to the PU(Processing Unit) and processed. This Data Flow system can be extended hierarchically by a network of simple modules. The principle working elements of the machine are a set of PMs, each of which performs the execution of the data flow procedures held in a local memory, NTM(Node Token Memory) within the PM.

• 정보학연구
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• 제9권1호
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• pp.55-63
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• 2006

In this paper, a Hangul recognition system using of Kohonen Network in the phoneme separation and learning is proposed. A Hangul consists of phoneme that are consists of strokes. The phoneme recognition and separation are very important in the recognition of character. So, the phonemes which mismatching has been happened are correctly separated through the learning of neural networks. also, learning rate($\alpha$) adjusted according to error, in order to solved that its decreased the number of iteration and the problem of local minimum, adaptively.

• Structural Engineering and Mechanics
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• 제77권6호
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• pp.779-795
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• 2021

In the recent decades, various optimization algorithms have been considered for the optimization of structures. In this research, a new enhanced algorithm is used for the size and topology optimization of truss structures. This algorithm, which is obtained from the combination of Crow Search Algorithm (CSA) and the Cellular Automata (CA) method, is called CA-CSA method. In the first iteration of the CA-CSA method, some of the best designs of the crow's memory are first selected and then located in the cells of CA. Then, a random cell is selected from CA, and the best design is chosen from the selected cell and its neighborhood; it is considered as a "local superior design" (LSD). In the optimization process, the LSD design is used to modify the CSA method. Numerical examples show that the CA-CSA method is more effective than CSA in the size and topology optimization of the truss structures.

• Current Optics and Photonics
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• 제6권5호
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• pp.473-478
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• 2022

To overcome the capture-range problem in phase-diversity phase retrieval (PDPR), a geometrical-optics initial-estimate method is proposed to avoid a local minimum and to improve the accuracy of laser-wavefront measurement. We calculate the low-order aberrations through the geometrical-optics model, which is based on the two spot images in the propagation path of the laser, and provide it as a starting guess for the PDPR algorithm. Simulations show that this improves the accuracy of wavefront recovery by 62.17% compared to other initial values, and the iteration time with our method is reduced by 28.96%. That is, this approach can solve the capture-range problem.

• Journal of Radiation Protection and Research
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• 제42권4호
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• pp.212-221
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• 2017

Background: This study aims to calculate detector positions as a design of a radioactive source localizing radiation portal monitor (RPM) system using an improved genetic algorithm. Materials and Methods: To calculate of detector positions for a source localizing RPM system optimization problem is defined. To solve the problem, a modified iterative genetic algorithm (MIGA) is developed. In general, a genetic algorithm (GA) finds a globally optimal solution with a high probability, but it is not perfect at all times. To increase the probability to find globally optimal solution rather, a MIGA is designed by supplementing the iteration, competition, and verification with GA. For an optimization problem that is defined to find detector positions that maximizes differences of detector signals, a localization method is derived by modifying the inverse radiation transport model, and realistic parameter information is suggested. Results and Discussion: To compare the MIGA and GA, both algorithms are implemented in a MATLAB environment. The performance of the GA and MIGA and that of the procedures supplemented in the MIGA are analyzed by computer simulations. The results show that the iteration, competition, and verification procedures help to search for globally optimal solutions. Further, the MIGA is more robust against falling into local minima and finds a more reliably optimal result than the GA. Conclusion: The positions of the detectors on an RPM for radioactive source localization are optimized using the MIGA. To increase the contrast of the measurements from each detector, a relationship between the source and the detectors is derived by modifying the inverse transport model. Realistic parameters are utilized for accurate simulations. Furthermore, the MIGA is developed to achieve a reliable solution. By utilizing results of this study, an RPM for radioactive source localization has been designed and will be fabricated soon.

• 한국지능시스템학회논문지
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• 제21권3호
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• pp.401-406
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• 2011

복잡해진 최적화문제를 전통적인 방법보다 효율적으로 해결하기위해 유전알고리즘이나 개미군집화, 하모니서치알고리즘과 같은 다양한 메타휴리스틱이 개발되었다. 그 중에서 하모니 서치알고리즘이 다른 메타휴리스틱알고리즘보다 좋은 결과를 보이고 있다. 하모니 서치 알고리즘은 음악을 작곡할 때 아름다운 소리를 내는 하모니를 찾는 과정을 모방했다. 성능은 하모니 메모리에서 선택하는 비율인 HMCR값과 하모니 메모리에서 선택된 값의 조정 비율을 결정하는 PAR값에 따라 달라지는 것으로 알려져 있다. 다르게 말하면 두 변수의 기반이 되는 하모니 메모리의 사용방법의 문제로 볼 수 있다. 본 논문은 설정한 기간 동안 더 좋은 최적해를 찾지 못할 경우 하모니 메모리의 일부를 좋은 하모니로 구성되게 수정하는 방법을 제안했다. 테스트 함수를 이용한 검증 실험결과에서 하모니 메모리를 수정할 경우 정확도 변화가 적어 신뢰성 있는 정확도를 보였으며, Iteration이 짧더라도 최적값에 근접한 값을 찾았다.