• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.181-183
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• 2006

This paper proposes an intelligent decision framework for update of the environment model using GSPN(generalized stochastic petri nets). The GSPN has several advantages over direct use of the Markov Process. The modeling, analysis, and performance evaluation are conducted on the mathematical basis. By adopting the probabilistic approach, our decision framework helps the robot to decide the time to update the map. The robot navigates autonomously for a long time in dynamic environments. Experimental results show that the proposed scheme is useful for service robots which work semi-permanently and improves dependability of navigation in dynamic environments.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.301-308
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• 1997

The learning speed of the neural networks, the most important factor in applying to real problems, greatly depends on the learning rate of the networks, Three approaches-empirical, deterministic, and stochastic ones-have been proposed to date. We proposed a new learning-rate selection algorithm using an evolutionary programming search scheme. Even though the performance of our method showed better than those of the other methods, it was found that taking much time for selecting evolutionary learning rates made the performance of our method degrade. This was caused by using static intervals (called static windows) in order to update learning rates. Out algorithm with static windows updated the learning rates showed good performance or didn't update the learning rates even though previously updated learning rates shoved bad performance. This paper introduce a window control scheme to avoid such problems. With the window control scheme, our algorithm try to update the learning ra es only when the learning performance is continuously bad during a specified interval. If previously selected learning rates show good performance, new algorithm will not update the learning rates. This diminish the updating time of learning rates greatly. As a result, our algorithm with the window control scheme show better performance than that with static windows. In this paper, we will describe the previous and new algorithm and experimental results.

• 한국전산구조공학회논문집
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• 제34권1호
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• pp.35-42
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• 2021

이 논문에서는 공용중인 구조물의 상시 계측 자료를 사용한 온라인 유한요소 모델 업데이트 방법을 제안한다. 일반적인 최적화 방법에 기반한 기존의 방법은 최적해를 찾기까지 반복적으로 고유치 해석을 수행해야 하므로 상시 업데이트에 사용하기에는 효과적이지 못하다. 제안하는 방법은 별도의 오프라인 작업이나 사용자의 개입이 없이 자동화된 과정으로 계측과 동시에 온라인 유한요소모델 업데이트를 수행할 수 있는 새로운 방법이다. 자동화된 Cov-SSI 알고리즘을 통해 구조물의 진동 계측 신호로부터 고유진동수 및 모드 형상을 식별하고, 이를 다시 역 고유치 신경망에 입력하여 최종적으로 업데이트된 유한요소 모델의 파라미터를 추정한다. 풍하중을 받는 20층 전단 빌딩 구조 모형에 대한 수치예제를 통해 제시한 방법이 자동으로 연속적인 유한요소모델 업데이트를 할 수 있었음을 확인하였다. 또한, 계측 도중 구조물의 특성이 변화하는 시나리오에 대한 예제에서 구조물의 변화가 일어나는 시점과 변화 후 변동된 구조 모델 파라미터 값을 성공적으로 추정할 수 있음을 확인하였다.

• ETRI Journal
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• 제37권2호
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• pp.395-405
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• 2015

We address the problem of uncooperative person recognition through continuous monitoring. Multiple modalities, such as face, height, clothes color, and voice, can be used when attempting to recognize a person. In general, not all modalities are available for a given frame; furthermore, only some modalities will be useful as some frames in a video sequence are of a quality that is too low to be able to recognize a person. We propose a method that makes use of stochastic information updates of temporal modalities and environment estimators to improve person recognition performance. The environment estimators provide information on whether a given modality is reliable enough to be used in a particular instance; such indicators mean that we can easily identify and eliminate meaningless data, thus increasing the overall efficiency of the method. Our proposed method was tested using movie clips acquired under an unconstrained environment that included a wide variation of scale and rotation; illumination changes; uncontrolled distances from a camera to users (varying from 0.5 m to 5 m); and natural views of the human body with various types of noise. In this real and challenging scenario, our proposed method resulted in an outstanding performance.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• 한국통신학회논문지
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• 제31권12C호
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• pp.1280-1287
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• 2006

본 논문은 변형 확률적 경사도 기반 (MSGB) 필터를 제안하고 그 필터가 최적화 문제에 대한 해가 될 수 있음을 보여준다. 갱신항으로 첨가된 가변적 축척 매개변수를 가진 비선형 적응 필터인 MSGB 필터의 특성을 분석 한다. 가변 매개변수의 MSGB 필터는 가변 축척 매개변수를 통하여 고정 매개변수의 MSGB 필터와 무매개변수의 MSGB 필터를 연결하는 역할을 한다. 그 안정성 영역과 오조정량도 살펴본다. 시스템 식별에 응용하여 컴퓨터 모의실험을 실행하여 MSGB 필터의 개선된 성능 특성을 보여준다.

• 대한기계학회논문집A
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• 제37권11호
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• pp.1315-1321
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• 2013

볼 베어링의 손상 상태를 예측하기 위한 방법을 본 논문에서 제시하였다. 손상 진전율을 추정하기 위해 확률적 베어링 피로 결함 진전 모델을 적용하고 잡음이 포함된 가속도 신호의 RMS 데이터를 이용하여 손상 상태와 고장 시간을 계산하였다. 확률적 결함 진전 모델의 파라미터는 볼 베어링에 대한 일련의 Run-to-Failure 시험을 수행하여 결정하였다. 가속도 RMS값으로부터 손상 진전율과 손상 상태를 추정하기 위해 규칙화된 파티클 필터 추정 방법을 적용하였다. 미래 시점에서의 손상 상태는 최근 측정된 데이터와 직전에 추정된 상태값을 이용하여 예측하였다. 예측된 손상 상태와 시험 데이터와 비교하여 개발된 방법의 적절성을 확인하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.

• 전자공학회논문지B
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• 제30B권8호
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• pp.54-63
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• 1993

This paper proposes a method for improving the performances of the neural network for optimization by an optimal estimation of initial states. The optimal initial state that leads to the global minimum is estimated by using the stochastic approximation. And then the update rule of Hopfield model, which is the high speed deterministic algorithm using the steepest descent rule, is applied to speed up the optimization. The proposed method has been applied to the tavelling salesman problems and an optimal task partition problems to evaluate the performances. The simulation results show that the convergence speed of the proposed method is higher than conventinal Hopfield model. Abe's method and Boltzmann machine with random initial neuron output setting, and the convergence rate to the global minimum is guaranteed with probability of 1. The proposed method gives better result as the problem size increases where it is more difficult for the randomized initial setting to give a good convergence.

• 정보기술과데이타베이스저널
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• 제4권2호
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• pp.21-32
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• 1998

A primary concern of physical database design has been efficient retrieval and update of a record because predictable performance of a DBMS is indispensable to time-critical missions. To maintain such phenomenal performance, database manages often spends more than or as much as the goal of an organization can warrant. The motivation of this research stems from the fact that even predictable performance of a physical database can be hampered by stochastic query processing time, physical configurations of a database, and random arrival processes of queries. They all together affect the overall performance of a DBMS. In particular, if there are queuing delays due to limited capacity or during on-peak congestion, this paper suggest to prioritize database services. A surprising finding of this paper is that such a transition from a non-priority system to a corresponding priority-based system can be Pareto-improving in the sense that no users in the system will be worse off after the transition. Thus prioritizing database services can be a viable option for efficient database management.