• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.792-801
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• 1997

In this paper we propose an algorithm which fits a surface to noise-corrupted data points using an aceive sur-vace with global deformations.It is 3-dimensional surface extension of our precious works on 2-dimensional curve modell[11,12].We use fimite differences,and represent shapes of surface as global transfromation suring evolution based on the ballon [odel[2,3]which allows partial inflation or deflation by using niegborhood'sextermal foreces on each node points.At first,we make local deformations based on the balloon [odel,and then the blobal transformation from the surface before deformations to the defomed surface is calcualted by the deterministic lest squre method,finally we apply the global transformation to the surface before deformations.In experiments we fit a surface(elliposid,B-spline)to noise-corrupted 3D data points using the active surface with affine deformations.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.4
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• pp.401-408
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• 2015

Conventional model updating methods for the structures have used global structural responses which are modal parameters obtained through vibration measurements. Although models updated by modal parameters estimate global structural responses accurately, they have difficulties to predict local responses for safety assesment of structural members. The safety of structural members in the structures has been evaluated through the stress estimation based on strain measurements. Thus, this study additionally uses measured strain responses of structural members to perform model updating besides modal parameters. In the proposed method, the objective functions are set to the differences of the global and local responses obtained from updated model and measurement and those functions are minimized by NSGA-II, one of the multi-objective optimization techniques. The strain responses predicted from updated model are used for safety assessment of the steel frame structures. The proposed method are verified by numerical and experimental studies through the impact hammer tests for a steel frame specimen.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.523-530
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• 2007

This paper proposes a new stochastic optimization algorithm for hidden Markov models (HMMs) used as a recognizer of automatic lipreading. The proposed method combines a global stochastic optimization method, the simulated annealing technique, and the local optimization method, which produces fast convergence and good solution quality. We mathematically show that the proposed algorithm converges to the global optimum. Experimental results show that training HMMs by the method yields better lipreading performance compared to the conventional training methods based on local optimization.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.75-78
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• 2006

본 논문에서 우리는 HIV-1 바이오 동역학모델의 퍼지 모델링 및 디지털 퍼지 제어 기법을 소개한다. 그것의 제어구조는 샘플링 점들에서 측정한 상태로부터 현재 상태를 대략적으로 예측하는 수치적 적분 구조를 사용한다. 제안된 지능형 디지털 재설계에서는 전역 상태-정합과 안정도 조건들을 동시적으로 만족하는 타당한 디지털 제어 이득들을 찾는 것이다. 우리는 보상된 블록-펄스 함수를 이용하여 새로운 전역 상태-정합 조건을 우선 제시하며 그리고 나서 안정도 조건들을 이 조건들에 추가한다. 유도된 조건들은 선형행렬 부등식으로 묘사되며, 그로인해 볼록 최적화 문제로 쉽게 해결될 수 있다. 또한, 안정도 조건으로 인한 성능 하강을 방지하기 위해 두 단계 지능형 디지털 재설계 과정이 제안된다. 첫 번째 단계에서는 전역 상태-정합만을 고려한 디지털 제어 이득을 찾는다. 두 번째 단계에서는 얻어진 디지털 제어하의 폐루프 시스템을 안정화 시키는 추가디지털 제어기를 설계한다.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.701-705
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• 2008

Design analysis and computer experiments (DACE) model is widely used to express efficiently the nonlinear responses in the field of engineering design. Kriging model, a DACE model, can approximately replace a simulation model that is very expensive or highly nonlinear. The kriging model is composed of the summation of a global model and a local model representing deviation from global model. The local model is determined by correlation coefficient of the pre-sampled points, where determination of the correct correlation coefficient has an effect on accuracy and robustness of the kriging model. Therefore, robustness of the correlation coefficient is explored with respect to degrees of the global model. Then we propose the range of correlation coefficient to make correct and robust kriging model and the influence of the correlation coefficients on the degrees of global model with respect to the nonlinearity of the pre-sampled responses.

• The Journal of Bigdata
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• v.9 no.1
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• pp.31-47
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• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.37-44
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• 2019

With the advance of digital IT technology, the performance of the printing and scanning devices is improved and their price becomes cheaper. As a result, the public can easily access these devices for crimes such as forgery of official and private documents. Therefore, if we can identify which printing device is used to print the documents, it would help to narrow the investigation and identify suspects. In this paper, we propose a deep learning model for printer identification. A convolutional neural network model based on local features which is widely used for identification in recent is presented. Then, another model including a step to calculate global features and hence improving the convergence speed and accuracy is presented. Using 8 printer models, the performance of the presented models was compared with previous feature-based identification methods. Experimental results show that the presented model using local feature and global feature achieved 97.23% and 99.98% accuracy respectively, which is much better than other previous methods in accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.70-72
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• 2002

본 논문에서는 분산된 XML 문서를 수집 처리하여 상호 제공하는 과정에서 활용될 통합된 XML 문서의 전역 스키마를 생성하는 시스템을 제안한다. 제안된 시스템에서는 분산 환경에 존재하는 개별적인 XML 문서들에 대한 지역 스키마 정보를 관계형 데이터베이스로 구축하고, 통합된 XML 문서의 데이터를 기반으로 각각의 지역 스키마 데이터베이스를 검색한 후 데이터에 적합한 스키마 정의를 추출하게 된다. 또한 추출과정에서 중복 정의에 의한 충돌 범주를 분석하고, 이를 해결하는 방법도 제시하였다. 시스템 모델링 결과 XML 문서의 구조와 검증을 명확하게 보장하는 통합된 XHL 문서의 전역 스키마 생성과 지역 스키마간 발생되는 충돌문제 해결이 가능함을 알 수 있었다.

• The Transactions of the Korea Information Processing Society
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• v.1 no.2
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• pp.172-183
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• 1994

In the multidatabase systems(MDBS), local autonomy and global consistency are important issues. Global consistency could be maintained by a global concurrency control algorithm and a global recovery algorithm. In this thesis, we propose a global concurrency control algorithm to ensure local autonomy and to guarantee global serializability, and a global recovery algorithm which is possible to recover the multudatabase from any failures. The proposed global concurrency control algorithm uses bottom-up approach, based on three-level transaction processing model. It can produce a local history that the execution order of subtransactions is identical to their serialization order by using dummy-operations in the server when an indirect conflict is caused between subtransactions due to local transactions. At the global module, it can efficiently validate global serializability of global transactions by checking global serializability only for the global transactions which conflict with each other.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.366-369
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• 2006

본 논문에서는 계층적 공정 경쟁 유전자 알고리즘을 통한 비선형시스템의 정보입자 기반 퍼지집합 퍼지집합 모델의 최적화 방법을 제안한다. 퍼지집합 모델은 주로 전문가의 경험에 기반을 두어 얻어지기 때문에 동정과 최적화 과정이 필요하며 GAs를 이용하여 퍼지모델을 최적화한 연구가 많이 있다. GAs는 전역 해를 찾을 수 있는 최적화 알고리즘으로 잘 알려져 있지만 조기 수렴 문제를 포함하고 있다. 병렬유전자 알고리즘(PGA)은 조기수렴를 더디게 하고 전역 해를 찾기 위한 진화알고리즘이다. 적응형 계층적 공정 경쟁기반 유전자 알고리즘(AHFCGA)을 이용하여 퍼지모델의 입력변수, 멤버쉽함수의 수, 멤버쉽함수의 정점 등의 전반부 구조와 파라미터를 동정하였고, LSE를 사용하여 후반부 파라미터를 동정하였으며 실험적 예제를 통하여 제안된 방법의 성능을 평가한다.