• 대한전기학회논문지:시스템및제어부문D
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• 제54권9호
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• pp.539-546
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• 2005

This paper describes a method of building the probability grid map for an autonomous mobile robot using the ultrasonic DAF(data association filter). The DAF, which evaluates the association of each data with the rest and removes the data affected by the specular reflection effect, can improve the reliability of the data for the Probability grid map. This method is based on the evaluation of possibility that the acquired data are all from the same object. Namely, the data from specular reflection have very few possibilities of detecting the same object, so that they are excluded from the data cluster during the process of the DAF. Therefore, the uncertain data corrupted by the specular reflection and/or multi-path effect, are not used to update the probability map, and hence building a good quality of a grid map is possible even in a specular environment. In order to verify the effectiveness of the DAF, it was applied to the Bayesian model and the orientation probability model which are the typical ones of a grid map. We demonstrate the experimental results using a real mobile robot in the real world.

• Nuclear Engineering and Technology
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• 제46권1호
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• pp.11-26
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• 2014

The analyses carried out within the Seismic Probabilistic Risk Assessments (SPRAs) of Nuclear Power Plants (NPPs) are affected by significant aleatory and epistemic uncertainties. These uncertainties have to be represented and quantified coherently with the data, information and knowledge available, to provide reasonable assurance that related decisions can be taken robustly and with confidence. The amount of data, information and knowledge available for seismic risk assessment is typically limited, so that the analysis must strongly rely on expert judgments. In this paper, a Dempster-Shafer Theory (DST) framework for handling uncertainties in NPP SPRAs is proposed and applied to an example case study. The main contributions of this paper are two: (i) applying the complete DST framework to SPRA models, showing how to build the Dempster-Shafer structures of the uncertainty parameters based on industry generic data, and (ii) embedding Bayesian updating based on plant specific data into the framework. The results of the application to a case study show that the approach is feasible and effective in (i) describing and jointly propagating aleatory and epistemic uncertainties in SPRA models and (ii) providing 'conservative' bounds on the safety quantities of interest (i.e. Core Damage Frequency, CDF) that reflect the (limited) state of knowledge of the experts about the system of interest.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.908-928
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• 2022

Multiple target tracking mainly focuses on tracking unknown number of targets in the complex environment of clutter and missed detection. The generalized labeled multi-Bernoulli (GLMB) filter has been shown to be an effective approach and attracted extensive attention. However, in the scenarios where the clutter rate is high or measurement-outliers often occur, the performance of the GLMB filter will significantly decline due to the Gaussian-based likelihood function is sensitive to clutter. To solve this problem, this paper presents a robust GLMB filter and smoother to improve the tracking performance in the scenarios with high clutter rate, low detection probability, and measurement-outliers. Firstly, a Student-T distribution variational Bayesian (TDVB) filtering technology is employed to update targets' states. Then, The likelihood weight in the tracking process is deduced again. Finally, a trajectory smoothing method is proposed to improve the integrative tracking performance. The proposed method are compared with recent multiple target tracking filters, and the simulation results show that the proposed method can effectively improve tracking accuracy in the scenarios with high clutter rate, low detection rate and measurement-outliers. Code is published on GitHub.

• 대한토목학회논문집
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• 제31권6C호
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• pp.259-266
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• 2011

기초구조물의 신뢰성 있는 저항계수 산정을 위해서는 충분한 양의 재하시험 결과에 근거한 저항의 분포특성 분석이 선행되어야 한다. 본 연구에서는 베이지안 이론에 근거하여 검증용 정재하시험 결과를 저항의 분포특성 분석에 반영할 수 있는 개선된 해석법을 제안하였고, 이를 통해 기 제안된 국내 타입강관말뚝의 저항계수를 갱신하였다. 측정 지지력이 확인된 정재하시험 결과를 이용하여 저항의 사전 분포특성을 산정하고, 검증용 정재하시험 결과를 우도정보로 고려하여 저항의 사후 분포특성을 평가하였다. 갱신된 저항의 사후 분포특성을 이용하여 일차신뢰도법에 의해 저항계수를 산정하였다. 총 5회의 검증용 재하시험 결과를 반영할 경우, 갱신된 저항계수는 목표신뢰도지수 2.33, 3.0에 대하여 각각 0.27-0.96, 0.19-0.68의 범위를 나타내었다. 본 연구에서 제시된 해석법을 통해 양질의 측정지지력 데이터가 부족하여 신뢰성 있는 저항계수를 산정하기 어려운 경우 현장 검증시험 결과를 반영한 저항계수의 보정이 가능함을 확인하였다.

• 대한기계학회논문집
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• 제16권4호
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• pp.675-683
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• 1992

본 연구에서는 저자에 의해 유도된 바 있는 베이지안 업데이트 모델을 초음파 센서를 갖는 실제 로봇에 주입하여 실험하였다. 초음파센서는 비교적 큰 빔(beam)구 경 때문에 단독의 측정치로도 넓은 영역을 감지하는데는 효율적이다. 그러나 실제상 황에서는 거울효과(specular reflection effect)라는 매우 심각한 문제점을 갖고 있으 며, 이는 지도의 질을 매우 저하시킨다. 이 효과를 상당히 줄일 수 있는, 단순하면 서도 실질적인 방법이 제안된다. 또한 본 논문에서는 이동로봇의 실시간 장애물 회 피를 위한 새로운 방법이 소개된다. 이 방법은 로봇의 현재 위치와, 점령영역과 비 점령 여역 사이의 경계선과 목표지점의 교차점까지의 거리를 이용하며, 점들이 실제 상황에서의 실험을 통해 입증된다.

• 한국콘텐츠학회논문지
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• 제21권11호
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• pp.77-86
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• 2021

시간적인 차이를 두고 획득한 이질적인 과거 전쟁 결과 데이터를 하나의 모형으로 구축하는 방법으로 베이지안 추론에 의한 전쟁시뮬레이션 모형을 구축하는 방법을 제안하였다. 과거의 전쟁 결과를 분석하여 미래에 있을 수 있는 전쟁을 예측하는 방법으로 선형회귀모형을 적용하는 방법을 고려할 수 있다. 그러나 역사적으로 시대가 서로 달라 전장 환경의 변화가 반영된 이질적인 두 유형의 자료들이라면 모형의 가정사항 위반으로 하나의 선형회귀모형으로 적합하는 것은 적절하지 않다. 이러한 문제를 해결하기 위해 앞선 시대에 있는 자료를 비정보적 사전분포로 가정하여 사후분포를 구하고 이를 다음 시대에 얻은 자료를 분석하기 위한 사전분포로 활용하여 최종 사후분포를 추론하는 베이지안 추론 방법을 제안하였다. 베이지안 추론 방법의 또 다른 장점은 마코프 체인 몬테 카를로 방법으로 샘플링한 결과를 이용하여 불확실성이 반영된 사후분포나 사후예측분포를 추론할 수 있다는 점이다. 이렇게 했을 때 고전적인 선형회귀모형으로 분석하는 것보다 다양한 정보를 활용할 수 있을 뿐만 아니라 향후 추가적으로 획득되는 자료도 모형에 반영하여 모형을 계속 업데이트시킬 수 있다는 장점이 있다.

• 제어로봇시스템학회논문지
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• 제15권7호
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• pp.720-727
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• 2009

This paper proposes a new integrated navigation system for a mobile robot in indoor environments. This system consists of five frameworks which are classified by function. This architecture can make the navigation system scalable and flexible. The robot can recover from exceptional situations, such as environmental changes, failure of entering the narrow path, and path occupation by moving objects, using the exception recovery framework. The environmental change can be dealt with using the probabilistic approach, and the problems with the narrow path and path occupation are solved using the ray casting algorithm and the Bayesian update rule. The proposed navigation system was successfully applied to several robots and operated in various environments. Experimental results showed good performance in that the exception recovery framework significantly increased the success rate of navigation. The system architecture proposed in this paper can reduce the time for developing robot applications through its reusability and changeability.

• Journal of the Korean Data and Information Science Society
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• 제24권6호
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• pp.1531-1541
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• 2013

This paper proposes a new method of VaR forecasting using the conditional autoregressive VaR (CAViaR) models and information criteria. Instead of using a single CAViaR model, we propose to utilize several candidate CAViaR models during a forecasting period. By adopting the Akaike and Bayesian information criteria for quantile regression, we can update not only parameter estimates but also the CAViaR specifications. We also propose extended CAViaR models with a constant location parameter. An empirical study is provided to examine the performance of the proposed method. The results suggest that our method shows more stable performance than those using a single specification.

• 로봇학회논문지
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• 제4권3호
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• pp.169-176
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• 2009

Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot's orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot's pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2451-2453
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• 2002

In this paper, we present an infrared sensors aided map building method for mobile robot using genetic algorithm and fuzzy logic. Existing Bayesian update model using ultrasonic sensors only has a problem of the quality of map being degraded in the wall with irregularity which is caused by the wide beam width of sonar waves and Gaussian probability distribution. In order to solve this problem we propose an improved method of map building using supplementary infrared sensors. In the method, wide beam width of sonar waves is divided by infrared sensors and probability is distributed according to infrared sensors' information using fuzzy logic and genetic algorithm.