• Journal of Astronomy and Space Sciences
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• 제29권4호
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• pp.389-395
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• 2012

The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

• Wind and Structures
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• 제15권2호
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• pp.87-109
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• 2012

A substantial part of South Africa is subject to more than one strong wind source. The effect of that on extreme winds is that higher quantiles are usually estimated with a mixed strong wind climate estimation method, compared to the traditional Gumbel approach based on a single population. The differences in the estimated quantiles between the two methods depend on the values of the Gumbel distribution parameters for the different strong wind mechanisms involved. Cluster analysis of the distribution parameters provides a characterization of the effect of the relative differences in their values, and therefore the dominance of the different strong wind mechanisms. For gusts, cold fronts tend to dominate over the coastal and high-lying areas, while other mechanisms, especially thunderstorms, are dominant over the lower-lying areas in the interior. For the hourly mean wind speeds cold fronts are dominant in the south-west, south and east of the country. On the West Coast the ridging of the Atlantic Ocean high-pressure system dominate in the south, while the presence of a deep trough or coastal low pressure system is the main strong wind mechanism in the north. In the central interior cold fronts tend to share their influence almost equally with other synoptic-scale mechanisms.

• 한국정보기술학회논문지
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• 제16권12호
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• pp.85-92
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• 2018

복잡한 영상처리 알고리즘을 사용할 때 계산량을 줄이기 위해 슈퍼픽셀을 사용한다. 슈퍼픽셀은 특성이 유사한 픽셀들을 군집화하여 하나의 그룹으로 만드는 방법이다. 슈퍼픽셀은 영상처리의 전단계로 사용되기 때문에 빠르게 생성할 수 있어야 하고 영상의 에지 성분들을 잘 보존하여야 한다. 본 논문에서는 에지 성분을 잘 보존하면서도 계산량이 많지 않은 슈퍼픽셀 생성 방법을 제안한다. 제안하는 방법에서는 먼저 기존의 k-mean 방법을 이용하여 영상의 슈퍼픽셀을 충분히 생성하고, 생성된 슈퍼픽셀들을 분석하여 유사한 슈퍼픽셀을 병합하는 방식으로 최종 슈퍼픽셀을 생성한다. 슈퍼픽셀을 병합할 때는 슈퍼픽셀에 대해서만 유사도를 측정하기 때문에 추가되는 계산량은 많지 않다. 실험 결과는 제안하는 방법으로 생성된 슈퍼픽셀이 기존 방법에 의해 생성된 슈퍼픽셀에 비해 보다 정확하게 에지 성분들을 보존하는 것을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1236-1251
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• 2013

Sharing geo-tagged photos has been a hot social activity in the daily life because these photos not only contain geo information but also indicate people's hobbies, intention and mobility patterns. However, the present raw geo-tagged photo routes cannot provide information as enough as complete GPS trajectories due to the defects hidden in them. This paper mainly aims at analyzing the large amounts of geo-tagged photos and proposing a novel travel route restoring method. In our approach we first propose an Interest Measure Ratio to rank the hot spots based on density-based spatial clustering arithmetic. Then we apply the Hidden Semi-Markov model and Mean Value method to demonstrate migration discipline in the hot spots and restore the significant region sequence into complete GPS trajectory. At the end of the paper, a novel experiment method is designed to demonstrate that the approach is feasible in restoring route, and there is a good performance.

• 산업융합연구
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• 제18권3호
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• pp.1-6
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• 2020

이 연구의 목적은 Snyder가 제안한 희망 이론에 대한 이론적 이해와 학교 현장에서의 유용성을 확인하는 것이다. 이에 희망 이론이 우리나라 일반고 학생들에게서 나타나는 군집 유형을 탐색하고, 유형별 학습전략의 차이를 군집분석과 ANOVA를 통해 분석하였다. 연구결과는 다음과 같다. 첫째, 일반고 학생의 희망 군집은 네 유형으로 나타났다. 둘째, 희망과 학습전략은 정적상관관계를 보였으며, 희망의 두 하위 변인 모두 메타인지와 상관관계가 가장 높았다. 연구 결과를 토대로 희망이론은 프로파일적 접근을 통하여 성취나 적응 등 다양한 심리변인과의 관계구조 연구가 필요함을 제시하였다.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.253-260
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• 1999

The recycling cell formation problem means that disposal products me classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences during product use phase and recycling cells are formed design, process and usage attributes. In order to treat the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem far disposal products. In this paper, a heuristic approach fuzzy ART neural network is suggested. The modified fuzzy ART neural network is shown that it has a great efficiency and give an extension for systematically generating alternative solutions in the recycling cell formation problem. We present the results of this approach applied to disposal refrigerators and the comparison of performances between other algorithms. This paper introduced a procedure which integrates economic and environmental factors into the disassembly of disposal products for recycling in recycling cells. A qualitative method of disassembly analysis is developed and its ai is to improve the efficiency of the disassembly and to generated an optimal disassembly which maximize profits and minimize environmental impact. Three criteria established to reduce the search space and facilitate recycling opportunities.

• Smart Structures and Systems
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• 제7권2호
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• pp.133-152
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• 2011

System identification is a fundamental step towards the application of structural health monitoring and damage detection techniques. On this respect, the development of evolved identification strategies is a priority for obtaining reliable and repeatable baseline modal parameters of an undamaged structure to be adopted as references for future structural health assessments. The paper presents the identification of the modal parameters of the Guangzhou New Television Tower, China, using a data-driven stochastic subspace identification (SSI-data) approach complemented with an appropriate automatic mode selection strategy which proved to be successful in previous literature studies. This well-known approach is based on a clustering technique which is adopted to discriminate structural modes from spurious noise ones. The method is applied to the acceleration measurements made available within the task I of the ANCRiSST benchmark problem, which cover 24 hours of continuous monitoring of the structural response under ambient excitation. These records are then subdivided into a convenient number of data sets and the variability of modal parameter estimates with ambient temperature and mean wind velocity are pointed out. Both 10 minutes and 1 hour long records are considered for this purpose. A comparison with finite element model predictions is finally carried out, using the structural matrices provided within the benchmark, in order to check that all the structural modes contained in the considered frequency interval are effectively identified via SSI-data.

• 전기학회논문지
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• 제64권1호
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• Asian-Australasian Journal of Animal Sciences
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• 제15권4호
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• pp.461-465
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• 2002

Nine microsatellite loci were analyzed in 84 random individuals to characterize the genetic variability of three domestic goat breeds found in Korea and China: Korean goat, Chinese goat and Saanen. Allele diversity, heterozygosity, polymorphism information content, F-statistics, indirect estimates of gene flow (Nm) and Nei's standard distances were calculated. Based on the expected mean heterozygosity, the lowest genetic diversity was exhibited in Korean goat ($H_E$=0.381), and the highest in Chinese goat ($H_E$=0.669). After corrections for multiple significance tests, deviations from Hardy-Weinberg equilibrium were statistically significant over all populations and loci, reflecting the deficiencies of heterozygotes (global $F_{IS}$=0.053). Based on pairwise FST and Nm between different breeds, there was a great genetic differentiation between Korean goat and the other two breeds, indicating that these breeds have been genetically subdivided. Similarly, individual clustering based on the proportion of shared alleles showed that Korean goat individuals formed a single cluster separated from the other two goat breeds.

• Nuclear Engineering and Technology
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• 제44권4호
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• pp.355-362
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• 2012

The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict Pressure Tube (PT) diametral creep employing the previously measured PT diameters and operating conditions. There are twelve bundles in a fuel channel, and for each bundle a linear model was developed by using the dependent variables, such as the fast neutron fluences and the bundle coolant temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3, and 4 of the Wolsung nuclear plant in Korea were used to develop the BPLM. The data from the remaining 10 channels were used to test the developed BPLM. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from Units 2, 3, and 4. Two error components for the BPLM, which are the epistemic error and the aleatory error, were generated. The diametral creep prediction and two error components will be used for the generation of the regional overpower trip setpoint at the corresponding effective full power days. The root mean square (RMS) errors were also generated and compared to those from the current prediction method. The RMS errors were found to be less than the previous errors.