• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.77-88
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• 2006

The purpose of this study is to Propose the time dependent K-least time path algorithm applicable to a real-time based operation strategy in multi-modal transportation network. For this purpose, we developed the extended method based on entire path deletion method which was used in the static K-least time path algorithm. This method was applied to time dependent K-least time path algorithm to find k least time paths in order based on both time dependant mode-link travel time and transfer cost In particular, this algorithm find the optimal solution, easily describing transfer behavior, such as walking and waiting for transfer by applying a link-based time dependent label. Finally, we examined the verification and application of the Proposed algorithm through case study.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.167-174
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• 2007

This study proposes a practical and realistic method to establish an optimal lifetime maintenance strategy for deteriorating bridges by considering the life-cycle performance as well as the life-cycle cost. The proposed method offers a set of optimal tradeoff maintenance scenarios among other conflicting objectives, such as minimizing cost and maximizing performance. A genetic algorithm is used to generate a set of maintenance scenarios that is a multi-objective combinatorial optimization problem related to the and the life-cycle cost and performance as separate objective functions. A computer program, which generates optimal maintenance scenarios, was developed based on the proposed method. The subordinate relation between bridge members has been considered to decide optimal maintenance sequence. The developed program has been used to present a procedure for finding an optimal maintenance scenario for steel-girder bridges on the Korean National Road. Through this bridge maintenance scenario analysis, it is expected that the developed method and program can be effectively used to allow bridge managers an optimal maintenance strategy satisfying various constraints and requirements.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1150-1159
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• 2011

Reliability is defined as a probability that a system will operate properly for a specified period of time under the design operating conditions without failure. Reliability-Redundancy Optimization Problem(RROP) involves selection of components with multiple choices, redundancy levels and redundancy strategy(Active or Standby) for maximizing system reliability with constraints such as cost, weight, etc. Based on the design configuration of Multi-Spectral Camera(MSC) system of KOMPSAT-2, the mathematical programming model for RROP is suggested in this study. Due to the nature of RROP, i.e. NP-hard problem, Parallel Particle Swarm Optimization(PPSO) algorithm is proposed to solve it. The result of the numerical experiment for RROP is presented as instance of recommended design configuration at some mission time.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.57-69
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• 2005

The goal of this paper is to develop freeway Origin-Destination (OD) demand estimation model using real-time traffic data collected from Freeway Traffic Management System (FTMS). In existing research, the micro-simulation models had been used to get a link distribution proportion by time process. Because of hi-level problem between the traffic flow model and the optimal OD solution algorithm, it is difficult for the existing models to be loaded at FTMS. The formulation of methodology proposed in this paper includes traffic flow technique to be able to remove the bi-level problem and optimal solution algorithm using a genetic algorithm. The proposed methodology is evaluated by using the real-time data of SOHAEAN freeway, South Korea.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.51-58
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• 2015

This paper suggests assignment-swap algorithm with time complexity O(mn) to obtain the optimal solution for large-scale of transportation problem (TP) with incomplete cost lists. Generally, the TP with complete cost lists can be solved with TSM (Transportation Simplex Method). But, we can't be solved for large-scale of TP with TSM. Especially. It is hard to solve for large-scale TP with incomplete cost lists using TSM. Therefore, experts simply using commercial linear programming package. Firstly, the proposed algorithm applies assignment strategy of transportation quantity to ascending order of transportation cost. Then, we reassign from surplus of supply to shortage of demand. Secondly, we perform the 2-opt and 1-opt swap optimization to obtain the optimal solution. Upon application to $31{\times}15$ incomplete cost matrix problem, the proposed assignment-swap algorithm more improves the solution than LINGO of commercial linear programming.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1080-1086
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• 2014

In this paper, we analyze the tradeoff performance of a consensus algorithm when it is applied to the CSMA/CA-based distributed wireless network. The consensus algorithm has a faster convergence speed as the number of cooperating neighbors increases, but the transmission delay on the wireless network increases due to access collisions as the number of cooperating neighbors increases. Therefore, there exists a tradeoff relationship between these two performances and so there exists an optimal number of cooperating neighbors that minimizes the consensus time. The result for the optimal number of neighbors according to the number of nodes that participate in the consensus shows that it is optimal for all nodes to cooperate together in the small-scale network but it is optimal to limit the number of neighbors to a fixed value in the large-scale network with nodes greater than a certain value.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1666-1667
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• 2011

본 논문에서는 CSRR을 이용하여 0.5~1.5GHz 대역의 광대역 필터를 최적 설계 연구 하였다. Metamaterial의 일종인 CSRR은 LC 공진기 역할을 하며 전송선로와 결합하여 필터 특성을 나타낸다. 또한 높은 Q-factor의 특성을 갖기 때문에 협대역 대역통과 필터 특성을 갖는다. 이에 본 논문에서는 CSRR을 이용하여 광대역 대역통과 특성을 갑기 위해서 전송선로의 형태를 변형하고 진화알고리즘중 하나인 진화 전략기법을 이용하여 단일 셀의 최적 설계를 수행하였다. 이후 단위 셀을 다단으로 연결하여 최종 광대역 필터를 설계하였다. 본 논문에서 설계된 광대역 필터는 0.5~1.5GHz의 대역폭을 갖으며 00~00dB의 삽입손실을 갖는다. 그리고 저지대역에서 00~00dB의 저지 특성을 갖는다.

• The Journal of Information Technology and Database
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• v.5 no.1
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• pp.1-12
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• 1998

분산 환경에서 데이터의 할당(allocation)는 중요한 설계 이슈이다. 데이터의 할당은 분산 데이터에 대한 비용(cost) 감소, 성능(performance) 및 가용성(availability) 향상 등의 이점을 극대화할 수 있도록 최적화되어야 한다. 기존 연구들의 대부분은 트랜잭션의 수행 비용을 최소화하는 방향으로만 최적화된 데이터 할당 결과를 제시하고 있다. 즉, 비용, 성능 및 가용성을 모두 함께 고려하는 연구는 아직까지 제시된 결과가 없으며 이는 복잡한 모델에 대한 적절한 최적화 기법이 없기 때문이다. 본 연구에서는 분산 데이터의 이점들인 비용, 성능 및 가용성 등의 다중측면을 동시에 고려함으로써 데이터 할당에 대한 파레토 최적해를 제공하는 DAMMA (Data Allocation Methodology considering Multiple Aspects) 방법론을 제안하였다. DAMMA 방법론은 데이터 분할 과정을 통하여 생성된 최적의 단편들을 분산 시스템의 운용 비용, 수행 성능, 가용성 등의 요소를 고려하여 각 물리적 사이트에 중복 할당하는 파레토 최적해들을 생성해낼 수 있는 설계 방법론이다.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.195-202
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• 1999

본 논문은 배전 계통에서 부하 제약조건과 운전 제약조건을 고려한 손실 감소와 부하 평형에 대해 시뮬레이티드 어닐링 알고리즘을 적용한 재구성 방법을 서술하였다. 네트워크 재구성은 수많은 연계 개폐기와 구분 개폐기의 조합에 의해 이루어지기 때문에 조합적인 최적화 문제이다. 이러한 문제는 수많은 조합에 제약조건까지 있어 해를 구하기가 쉽지 않을 뿐 아니라 국소 해에 빠질 가능성이 많다. 따라서 신경망 중에서 제약조건에 따라 신경망 구조에 영향을 미치지 않으면서 전역 최소해에 수렴하는 특성을 가진 시뮬레이티드 어닐링 기법을 이용하여 배전 계통의 선로를 재구성하였다. 시뮬레이티드 어닐링은 이론적으로 최적해가 보장되지만 무한대의 시간이 걸리기 때문에 현실적으로 적용할 때 해 공간을 탐색하는 규칙과 온도를 적절히 내리는 냉각 스케줄(cooling schedule)이 중요하다. 본 논문에서는 알고리즘 상에서 제약조건 위반 여부를 점검할 수 있는 제약조건과 페널티 상수(penalty factor)를 통해 목적함수에 반영하는 제약조건으로 나누어 모든 후보해를 가능해가 되게 하였고 기존에 사용되던 Kirkpatrick의 냉각 스케줄 대신에 후보해의 통계적 처리에 의해 온도를 내리는 다항-시간 냉각 스케줄(polynomial-time cooling schedule)을 사용하여 수행시간을 단축하고 수렴성을 높였다. 제안한 알고리즘의 효용성을 입증하기 위해 32, 69모선 예제 계통으로 테스트하였다.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1591-1604
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• 2020

This study is a study on classifying land cover by applying high-resolution satellite images to deep learning algorithms and verifying the performance of algorithms for each spatial object. For this, the Fully Convolutional Network-based algorithm was selected, and a dataset was constructed using Kompasat-3 satellite images, land cover maps, and forest maps. By applying the constructed data set to the algorithm, each optimal hyperparameter was calculated. Final classification was performed after hyperparameter optimization, and the overall accuracy of DeeplabV3+ was calculated the highest at 81.7%. However, when looking at the accuracy of each category, SegNet showed the best performance in roads and buildings, and U-Net showed the highest accuracy in hardwood trees and discussion items. In the case of Deeplab V3+, it performed better than the other two models in fields, facility cultivation, and grassland. Through the results, the limitations of applying one algorithm for land cover classification were confirmed, and if an appropriate algorithm for each spatial object is applied in the future, it is expected that high quality land cover classification results can be produced.