• Journal of the Korea Computer Industry Society
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• v.3 no.2
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• pp.235-242
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• 2002

To minimize restructuring cost of a tree, experiments were conducted to collect quantitative information of searching efficiencies varying degrees of imbalance in a binary search tree. Degrees of tree imbalance were measured by a balance factor, an absolute value of height difference of left subtree and right subtree in a binary search tree. The average number of comparisons increased (p＜0.01), and searching efficiency of O(n) was more appropriate rather than O(logn), as degrees of imbalance in a binary search tree deteriorated. However, there were no significant differences of searching efficiencies in height balanced trees and trees with subtrees to have height 3 less than the other (p＞0.05). Therefore, the findings would be applicable to maintain searching efficiency of a software with a binary search tree.

• Journal of the Korean Geosynthetics Society
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• v.13 no.3
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• pp.21-30
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• 2014

In this study, the applicability of the lightweight dynamic cone penetrometer in the domestic slope site was investigated using the weathered granite soil sampled form the Namwon slope site. And then, the lightweight dynamic cone penetration tests according to the change in the degree of compaction and water content were performed and it was analyzed with the correlations between the degree of compaction, the void ratio, the degree of saturation and the value of cone resistance. From the laboratory test results, the cone penetration resistance was rapidly increased in the dry side of the optimum moisture content, and it was largely decreased in the wet side of the optimum moisture content. Moreover, when the degree of compaction and the degree of saturation are large, the cone resistance is increased linearly. And a high correlativity was shown between water content, void ratio, the degree of saturation and the cone resistance. From these results, it is judged that the lightweight dynamic cone penetrometer can be applied to the investigation on the site slope.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.113-126
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• 2008

This research aims to explore the effects of multicollinearity on the reliability and goodness of fit of logit model. To investigate the effects of multicollinearity on the multinominal logit model, numerical experiments are performed. The exploratory variables(attributes of utility functions) which have a certain degree of correlations from (rho=) 0.0 to (rho=) 0.9 are generated and rho-squares and t-statistics which are the indices of goodness of fit and reliability of logit model are traced. From the well designed numerical experiments, following findings are validated : 1) When a new exploratory variable is added, some of rho-squares increase while the others decrease. 2) The higher relations between generic variables lead a logit model worse with respect to goodness of fit. 3) Multicollinearity has a tendency to produce over-evaluated parameters. 4) The reliability of the estimated parameter has a tendency to decrease when the correlations between attributes are high. These results suggest that we have to examine the existence of multicollinearity and perform the proper treatments to diminish multicollinearity when we develop logit model.

• Journal of Biomedical Engineering Research
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• v.23 no.3
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• pp.243-251
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• 2002

Synthetic zinc wave employs Pulsed plane wave as transmit beam with linear time delay curve. The received echoes in different transmit directions at different transmit times are superposed at imaging Points with Proper time delay compensation using synthetic focusing scheme. This scheme. which uses full aperture in transmit, obtains a high SNR image, and also features high lateral resolution by using two way dynamic focusing at all imaging depths. In this Paper, we consider the Problems in realization of synthetic zinc wave. Also. we have applied the scheme to obtain phantom and in-vivo images using a linear array of 5 MHz. In phantom test. experimental images show high resolution over a more extended imaging depth than conventional fixed Point transmit and receive dynamic focusing schemes In-vivo images show that the resolution could not overcome conventional focusing systems because of motion blurring and(or) aberration of tissue. but the frame rate tan be increased by a factor of more than 5 compared to conventional focusing schemes. with competitive resolution at all imaging depths .

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1998.05a
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• pp.221-228
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• 1998

2차원 노심핵설계 코드 HELIOS를 이용하여 $^{7}$ LiF-BeF$_2$-ThF$_4$-$^{233}$ UF$_4$ 용융염 핵연료와 흑연(Graphite) 감속재로 구성된 AMBIDEXTER(Advanced Molten-salt Break-even Inherently-safe Dual-mission EXperimental and TEst Reactor) 원자로의 육각주형 로심격자에 대해 핵적 자활성 요건의 설계해석을 수행하였다. AMBIDEXTER 원자로는 액체 핵연료의 유동성을 이용한 온라인 핵연료 정화ㆍ처리ㆍ재생의 연속공정을 도입하여 노내의 잔류 핵분열 생성물질의 포화양을 최소로 유지시키고 중성자 경제성을 극대화하므로 높은 전환율을 얻는 설계이다. 핵연료 내에 잔류하는 핵분열생성물질의 포화농도에 대응하는 연소도를 등가연소도로 정의할 때, 열출력 250MW$_{th}$ AMBIDEXTER 원자로의 등가연소도 374MWD/TeH.E.의 평형 로심 모델에 대해 핵적 자활성을 지배하는 주요 핵설계 인자로서 용융염 핵연료의 $^{233}$ U Mole 분율, 흑연-대-용융염의 체적비, 노심격자 간격 및 출력 밀도의 변화에 따른 임계도 및 전환율을 평가하였다. 그 결과, $^{233}$ U Mole 분율과 혹연-대-용융염 체적비를 좌표축으로 하는 2차원상공간에서 핵적 자활성 요건 상태함수는 각 노심격자간격에 대해 완만한 선형 함수로 표현할 수 있음을 확인하였다.

• International Journal of Highway Engineering
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• v.19 no.4
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• pp.1-7
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• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

• International Journal of Highway Engineering
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• v.19 no.1
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• pp.73-80
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• 2017

PURPOSES : This paper presents a comparison study between dynamic and static analyses of falling weight deflectometer (FWD) testing, which is a test used for evaluating layered material stiffness. METHODS: In this study, a forward model, based on nonlinear subgrade models, was developed via finite element analysis using ABAQUS. The subgrade material coefficients from granular and fine-grained soils were used to represent strong and weak subgrade stiffnesses, respectively. Furthermore, the nonlinearity in the analysis of multi-load FWD deflection measured from intact PCC slab was investigated using the deflection data obtained in this study. This pavement has a 14-inch-thick PCC slab over fine-grained soil. RESULTS: From case studies related to the nonlinearity of FWD analysis measured from intact PCC slab, a nonlinear subgrade model-based comparison study between the static and dynamic analyses of nondestructive FWD tests was shown to be effectively performed; this was achieved by investigating the primary difference in pavement responses between the static and dynamic analyses as based on the nonlinearity of soil model as well as the multi-load FWD deflection. CONCLUSIONS : In conclusion, a comparison between dynamic and static FEM analyses was conducted, as based on the FEM analysis performed on various pavement structures, in order to investigate the significance of the differences in pavement responses between the static and dynamic analyses.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.95-100
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• 1996

In this paper, we present an image coder using variable quantizer for newly proposed JPEG extensions which has been standardized as ISO/IEC 10918-3(ITU-T Rec. T.84). It is necessary to alleviate the blocking artifact which is more sensitive to human eye in view of the spatial frequency sensitivity. The blocking artifact arises in the lower activity area rather than in the higher area. Therefore variable quantizer use the horizontal and vertical derivatives for calculating the $8{\times}8$ block activity. We classified nonlinear quantizer parameter into 5 categories in order to finely quantize in the lower active region. As a result of simulation for various images, the proposed coder increases subjective and objective quality at a given bit rate.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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• pp.573-577
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• 2002

A new estimated neural networks are proposed in order to measure nonlinear road environments in realtime. This new neural networks is Error Estimated Neural Networks. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we control 7 degree simulation, this controller and driver were proved to be effective to drive a car in the environments of nonlinear road systems.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2949-2952
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• 2000

A new neural networks and learning algorithm are proposed in order to measure nonlinear heights of complexed road environments in realtime without pre-information. This new neural networks is Error Self Recurrent Neural Networks(ESRN), The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by back-propagation and each weights are updated by RLS(Recursive Least Square). Consequently. this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by ESRN and learning algorithm and control nonlinear models. To show the performance of this one. we control 7 degree of freedom full car model with several control method. From this simulation. this estimation and controller were proved to be effective to the measurements of nonlinear road environment systems.