• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.321-344
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• 2017

Smart homes are the new generation of homes where pervasive computing is employed to make the lives of the residents more convenient. Human activity recognition (HAR) is a fundamental task in these environments. Since critical decisions will be made based on HAR results, accurate recognition of human activities with low uncertainty is of crucial importance. In this paper, a novel HAR method based on a difference of convex programming (DCP) problem is represented, which manages to handle uncertainty. For this purpose, given an input sensor data stream, a primary belief in each activity is calculated for the sensor events. Since the primary beliefs are calculated based on some abstractions, they naturally bear an amount of uncertainty. To mitigate the effect of the uncertainty, a DCP problem is defined and solved to yield secondary beliefs. In this procedure, the uncertainty stemming from a sensor event is alleviated by its neighboring sensor events in the input stream. The final activity inference is based on the secondary beliefs. The proposed method is evaluated using a well-known and publicly available dataset. It is compared to four HAR schemes, which are based on temporal probabilistic graphical models, and a convex optimization-based HAR procedure, as benchmarks. The proposed method outperforms the benchmarks, having an acceptable accuracy of 82.61%, and an average F-measure of 82.3%.

• International Journal of Aeronautical and Space Sciences
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• 제11권4호
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• 한국게임학회 논문지
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• 제8권1호
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• pp.79-90
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• 2008

본 논문에서는 동기 기반의 계층적 행동 계획 시스템을 제안한다. 가상 시뮬레이션 게임 환경에서 에이전트는 행동 계획 시스템을 통해 적합한 행동을 선택하게 된다. 행동 선택 시스템은 동기를 추출하고 목표를 선택하고 행동을 생성하고 최적화를 수행한다. 동기를 평가할 때 갑작스럽게 발생하거나 누적된 이벤트에 대해 계산한다. 동기를 선택할 때는 확률 분포를 사용하여 무작위로 선택한다. 계층적 목표 트리를 탐색한 후에 목표를 실행할 수 있다. 행동들을 비교한 후 가장 적합한 행동을 선택하게 된다. 선택을 할 때 안전도 값과 만족도 값을 비교하여 최적화된 행동을 선택한다. 본 연구에서 제안한 시스템을 식당경영 게임에 적용했다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 추계 학술논문 발표대회
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• pp.124-125
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• 2018

Recently, risk analysis studies regarding the architecture development project have been carried out by applying probabilistic method. However, it had a limit that this studies was conducted in only an apartment. Therefore, the objective of this study is to develop an optimal price estimation model that can be utilized on residential officetel project by applying Monte Carlo simulation. To achieve the objective, first, the variables are selected affecting the feasibility of an officetel based on literature review. Second, causal loop diagram is constructed by arranging the relationship between variables, then the import and expense model is suggested. Third, to carry out optimized parcel price, the range limits are set for each variables then Monte Carlo simulation is performed. In the future, the developed model is expected to help decision-makers as a tool to determine both risk and feasibility of the officetel development project.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2010년도 정기 학술대회
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• pp.476-479
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• 2010

In recent years, the deterioration of infrastructures is especially considered. In prestress concrete bridges, one of the important mechanisms of deterioration is the corrosion of the post-tensioned tendon due to environmental agents. In this study, the reliability analysis is performed for a prestress concrete box girder bridge under the pitting corrosion attack with considering the inspection and failure cost. The variation of life-time performance depending on inspection methods have to be quantified. The inspection methods with different accuracy of corrosion detection are presented and applied for model of reliability analysis. The computer program for analysis reliability index of the structure as well as updating process is obtained. An existing bridge is applied for illustrating the influence of inspection cost on the behaviors of structure. Subsequently, the benefit of inspection has shown to predict the time to failure of structure.

• 한국추진공학회지
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• 제22권5호
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• pp.73-80
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• 2018

고속 추진체계의 개발 초기는 자료의 부족, 비용 제약, 지상에서 실제 비행환경 모사의 어려움 등으로 불확실 요소들을 확률분포의 형태로 모델링하기 어려운 실정이다. 이러한 이유로 본 연구에서는 이중연소 램제트를 대상으로 전문가들의 경험에 의한 연소효율 정보를 수집하여 이를 에비던스 이론으로 모델링하여 불확실성을 정량화 하였다. 정량화한 불확실성 정보를 이용하여 흡입구와 연소기의 출구면적에 대하여 추력여유와 열질식의 불확실성을 고려한 신뢰성 최적설계를 수행하였다. 한정된 불확실 정보를 가지고 엔진의 개념설계가 가능함을 확인할 수 있었다.

• Smart Structures and Systems
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• 제30권3호
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Earthquakes and Structures
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• 제23권4호
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• pp.385-401
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• 2022

Uncertainty quantification is the most important challenge in seismic fragility assessment of structures. The precision increment of the quantification method leads to reliable results but at the same time increases the computational costs and the latter will be so undesirable in cases such as reliability-based design optimization which includes numerous probabilistic seismic analyses. Accordingly, the authors' effort has been put on the development and validation of an approach that has reduced computational cost in seismic fragility assessment. In this regard, it is necessary to apply the appropriate methods for consideration of two categories of uncertainties consisting of uncertainties related to the ground motions and structural characteristics, separately. Also, cable-stayed bridges have been specifically selected because as a result of their complexity and the according time-consuming seismic analyses, reducing the computations corresponding to their fragility analyses is worthy of studying. To achieve this, the fragility assessment of three case studies is performed based on existing and proposed approaches, and a comparative study on the efficiency in the estimation of seismic responses. For this purpose, statistical validation is conducted on the seismic demand and fragility resulting from the mentioned approaches, and through a comprehensive interpretation, sufficient arguments for the acceptable errors of the proposed approach are presented. Finally, this study concludes that the combination of the Capacity Spectrum Method (CSM) and Uniform Design Sampling (UDS) in advanced proposed forms can provide adequate accuracy in seismic fragility estimation at a significantly reduced computational cost.

• 대한전자공학회논문지SP
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• 제40권6호
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• pp.98-105
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• 2003

본 논문은 유전자 알고리즘을 이용한 새로운 레이더 펄스열 탐지 기법을 제안하며, 전자전 시스템의 위협식별을 위한 펄스열 분리에 사용될 목적으로 개발되었다. 기존의 펄스열 탐지는 히스토그램 혹은 연속 웨이브렛 변환을 이용한 결정론적 접근이 일반적이었으나, 전자전 신호환경에서 빈번히 발생하는 신호누락, 잡음 및 대전자전 레이더 신호에 대해서는 탐지 신뢰성이 떨어진다. 제안한 기법은 펄스 도래시간만을 이용하는 펄스열 탐지 기법으로서 유전자 알고리즘의 확률론적 특성을 이용하여 설계되었다. 본 기법에서는 펄스의 도래 시간차를 초기 염색체로 구성하였으며, 펄스위상을 정의하여 이를 이용한 적합도 검증을 수행하였다. 그리고 다중 신호원의 분리를 목적으로 하는 레이더 펄스열 탐지를 위해서 비용함수를 이용한 조기 종료 및 그룹화를 적용하였다. 제안한 기법을 이용하여 모의 레이더 신호에 대해 실험한 결과 기존의 방법에 비해 탐지 위협개수 및 펄스 반복 주기의 탐지 정확도가 향상되었음을 확인하였다.

• 대한토목학회논문집
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• 제14권1호
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• pp.231-243
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• 1994

본 연구에서는 하천본류의 유량과 수질, 하수처리장 유입수량과 유입수질을 확률변수로 두고, 취수 문제와 수중보에 의한 재포기현상을 포함하는 추계학적 하천수질관리모형을 개발하였다. Streeter-Phelps식을 이용해서 각 구간 하천수질의 기대값과 분산을 계산하는 확률모형을 만들고, 최적화문제의 확률적 제약식은 chance constrained 방법을 이용해서 확정적 제약식으로 변환한다. 목적함수는 지역내 하수처리장의 년간처리비용으로 두었다. 건설비용함수와 유지관리비함수는 처리효율과 처리용량의 함수인 비선형의 단일식으로 유도되었다. 최적화문제는 비선형계획법으로 해를 구하였다. 본 모형을 한강하류부에 적용한 결과 서울시내 4개 하수처리장에서 2차처리를 하고, 지천유입수의 BOD부하량이 현재와 같을 때, 1996년 DO수질기준을 만족하는 신뢰도는 50% 정도였다. 그리고 탄천, 중량천, 안양천의 BOD부하량을 현재보다 50% 감소시켰을 때 1996년 DO수질기준을 만족하는 신뢰도는 60% 이상이었다. 따라서 한강하류부의 수질보전을 위해서는 유입지천의 수질개선과 2차처리 이상의 하수처리가 요구된다.