• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.13-21
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• 2002

Fuzzy logic ignores some information in the reasoning process. Neural network is powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule-inference network. And the traditional propagation rule is modified. Experiments are performed to compare search costs by sequential searching and searching by priority. The experimental results show that the searching by priority is more efficient than the sequential searching as the size of the fuzzy inference network becomes larder and an the number of searching increases.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.565-570
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• 2013

Previous researches on mining sequential patterns mainly focused on discovering patterns from the point-based event. Interval events with a time interval occur in the real world that have the start and end point. Existing interval pattern mining methods that discover relationships among interval events based on the Allen operators have some problems. These are that interval patterns having three or more interval events can be interpreted as several meanings. In this paper, we propose the I_TPrefixSpan algorithm, which is an efficient sequence pattern mining technique for removing ambiguity in the Interval Patterns Mining. The proposed algorithm generates event sequences that have no ambiguity. Therefore, the size of generated candidate set can be minimized by searching sequential pattern mining entries that exist only in the event sequence. The performance evaluation shows that the proposed method is more efficient than existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1251-1255
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• 2011

In this paper, we propose an optimal path search algorithm that contains all nodes using genetic algorithm. An object in this approach is formed as an equation related with the Euclidean distance between an intermediate node and the starting node and between an intermediate node and the goal node. Like other genetic algorithm structures, our algorithm defines a fitness function and selects a crossover spot node and a bitwise crossover point. A new node out of such operation survives only if it satisfies the fitness criteria and that node then becomes the starting node for the next generation. Repetition continues until no changes are made in the population. The efficiency of this proposed approach is verified in the experiment that it is better than two other contestants - sequential approach and the random approach.

• Journal of Wetlands Research
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• v.11 no.2
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• pp.107-121
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• 2009

Many rainfall-runoff models have been used for the flood forecasting. However, the determination of rainfall-runoff model parameters is very difficult. In this study, we investigated the efficiency of flood forecasting models by studying the optimization techniques for parameter calibration of SFM, Tank, and SSARR models. We analyzed the correlations between parameters in optimization techniques, then classified the parameters into parameter groups. For this we applied the sequential calibration method through the sensitivity analysis. As the results of the analysis, the parameter groups clibration method showed better result for peak flow and clibtation time.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.2
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• pp.39-49
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• 2000

In this paper, we propose an optimizing feature extraction method for multiclass problems assuming normal distributions. Initially, We start with an arbitrary feature vector Assuming that the feature vector is used for classification, we compute the classification error Then we move the feature vector slightly in the direction so that classification error decreases most rapidly This can be done by taking gradient We propose two search methods, sequential search and global search In the sequential search, an additional feature vector is selected so that it provides the best accuracy along with the already chosen feature vectors In the global search, we are not constrained to use the chosen feature vectors Experimental results show that the proposed algorithm provides a favorable performance.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.234-236
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• 2003

본 논문에서는 Tabu 탐색법을 이용하여 UPFC용 퍼지 댐핑 제어기를 설계하였다. 또한 UPFC가 장착된 1기 무한대 계통에 대한 Dynamic 모델을 유도하였고, 기존에 순차적인 해석 방법의 문제점을 해결하였다. 경험적 최적화알고리즘인 Tabu 탐색법을 이용하여 UPFC용 퍼지 댐핑 제어기의 입 출력 이득을 튜닝하였다. 제안한 방법의 유용성을 입증하기 위해 1기 무한대 계통에 적용하여 비선형 시뮬레이션을 수행하였다. 이때 설계한 UPFC용 퍼지 댐핑 제어기는 사례계통의 운전 조건에 대해서 기존의 제어기보다 더 나은 제어 성능을 나타내었다.

• Journal of Korean Society of Transportation
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• v.23 no.1
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• pp.103-114
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• 2005

The transit fare resulted from the renovation of public transit system in Seoul is basically determined based on the distance-based fare policy (DFP). In DFP, the total fare a passenger has to pay for is calculated by the basic-transfer-premium fare decision rule. The fixed amount of the basic fare is first imposed when a passenger get on a mode and it lasts within the basic travel distance. The transfer fare is additionally imposed when a passenger switches from one mode to another and the fare of the latter mode is higher than the former. The premium fare is also another and the fare of the latter begins to exceed the basic travel distance and increases at the proportion of the premium fare distance. The purpose of this study is to propose an algorithm for finding K number of paths, paths that are sequentially sorted based on total amount of transit fare, under DFP of the idstance-based fare policy. For this purpose, the link mode expansion technique is proposed in order to save notations associated with the travel modes. Thus the existing K shortest path algorithms adaptable for uni-modal network analysis are applicable to the analysis for inter-modal transportation networks. An optimality condition for finding the K shortest fare routes is derived and a corresponding algorithms is developed. The case studies demonstrate that the proposed algorithm may play an important role to provide diverse public transit information considering fare, travel distance, travel time, and number of transfer.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.699-706
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• 2016

Outlier detection methods without performing a test often do not succeed in detecting multiple outliers because they are structurally vulnerable to a masking effect or a swamping effect. This paper considers testing procedures supplemented to a clustering-based method of identifying the group with a minority of the observations as outliers. One of general steps is performing a variety of t-test on individual outlier-candidates. This paper proposes a sequential procedure for searching for outliers by changing cutoff values on a cluster tree and performing a test on a set of outlier-candidates. The proposed method is illustrated and compared to existing methods by an example and Monte Carlo studies.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.11
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• pp.771-781
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• 2022

Sequential convex programming based performance analysis of the designed UAV is performed. The nonlinear optimization problems generated by aerodynamics are approximated to socond order program by discretization and convexification. To improve the performance of the algorithm, the solution of the relaxed problem is used as the initial trajectory. Dive trajectory optimization problem is analyzed through iterative solution procedure of approximated problem. Finally, the maximum final velocity according to the performance of the actuator model was compared.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8A
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• pp.942-949
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• 2004

This paper proposes and analyzes the anti-collision algorithm in Ubiquitous ID system. We mathematically compares the performance of the proposed algorithm with that of binary search algorithm, slotted binary tree algorithm using time slot, and bit-by-bit binary tree algorithm proposed by Auto-ID center. We also validated analytic results using OPNET simulation. Based on the analytic results, comparing the proposed algorithm with bit-by-bit algorithm which is the best of existing algorithms, the performance of proposed algorithm is about 5% higher when the number of tags is 20, and 100% higher when the number of tags is 200.