• 한국자기학회지
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• 제23권2호
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• pp.62-67
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• 2013

본 논문에서는 전자기 관련 제품의 효율적인 신뢰도 기반 최적설계를 위해 확률구속조건을 평가하는 기법으로 해의 안정성과 효율성이 우수한 목표 성능치법을 제시 하였다. 목표 성능치법을 적용한 신뢰도 기반 최적설계의 효율성 검증을 위하여 스피커 모델과 초전도 자기에너지 저장장치 모델에 대한 최적설계를 수행하였고, 이를 기존 신뢰도 지수법을 적용한 최적설계 결과와 비교하였다. 또한 몬테카를로 수치모사기법을 이용하여 도출된 최적해의 신뢰도를 재 계산 후 비교함으로써 제안된 기법의 신뢰도 평가 결과의 정밀도를 검증하였다.

• International Journal of Railway
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• 제6권2호
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• pp.45-52
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• 2013

Railway construction needs vast soil investigation for its infrastructure foundation designs along the planned railway path to identify the design parameters for stability and serviceability checks. The soil investigation data are usually classified and grouped to decide design input parameters per each construction section and budget estimates. Deterministic design method which most civil engineer and practitioner are familiar with has a clear limitation in construction/maintenance budget control, and occasionally produced overdesigned or unsafe design problems. Instead of using a batch type analysis with predetermined input parameters, data population collected from site soil investigation and design load condition can be statistically estimated for the mean and variance to present the feature of data distribution and optimized with a best fitting probability function. Probabilistic approach using entire feature of design input data enables to predict the worst, best and most probable cases based on identified ranges of soil and load data, which will help railway designer select construction method to save the time and cost. This paper introduces two Monte Carlo simulations actually applied on estimation of retaining wall external stability and long term settlement of organic soil in soil investigation area for a recent high speed railway project.

• Geomechanics and Engineering
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• 제18권6호
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• pp.595-603
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• 2019

A modified model combining Kriging and Monte Carlo method (MC) is proposed for probabilistic estimation of tunnel face stability in this paper. In the model, a novel uniform design is adopted to train the Kriging, instead of the existing active learning function. It has advantage of avoiding addition of new training points iteratively, and greatly saves the computational time in model training. The kinematic approach of limit analysis is employed to define the deterministic computational model of face failure, in which the Hoek-Brown failure criterion is introduced to account for the nonlinear behaviors of rock mass. The trained Kriging is used as a surrogate model to perform MC with dramatic reduction of calls to actual limit state function. The parameters in Hoek-Brown failure criterion are considered as random variables in the analysis. The failure probability is estimated by direct MC to test the accuracy and efficiency of the proposed probabilistic model. The influences of uncertainty level, correlation relationship and distribution type of random variables are further discussed using the proposed approach. In summary, the probabilistic model is an accurate and economical alternative to perform probabilistic stability analysis of tunnel face excavated in spatially random Hoek- Brown media.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권3호
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• pp.271-279
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• 2018

Purpose: Probabilistic safety analysis was performed to enhance the safety and reliability of nuclear power plants because traditional deterministic approach has limitations in predicting the risk of failure by crack growth. The study introduces a probabilistic approach to establish a basis for probabilistic safety assessment of passive components. Methods: For probabilistic modeling of fatigue crack growth rate (FCGR), various FCGR tests were performed either under constant load amplitude or constant ${\Delta}K$ conditions by using heat treated X-750 at low temperature with adequate cathodic polarization. Bayesian inference was employed to update uncertainties of the FCGR model using additional information obtained from constant ${\Delta}K$ tests. Results: Four steps of Bayesian parameter updating were performed using constant ${\Delta}K$ test results. The standard deviation of the final posterior distribution was decreased by a factor of 10 comparing with that of the prior distribution. Conclusion: The method for developing a probabilistic crack growth model has been designed and demonstrated, in the paper. Alloy X-750 has been used for corrosion fatigue crack growth experiments and modeling. The uncertainties of parameters in the FCGR model were successfully reduced using the Bayesian inference whenever the updating was performed.

• 한국지반공학회논문집
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• 제25권12호
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• pp.13-25
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• 2009

본 연구에서는 확률론적 해석에 지반의 공간적 변동성을 고려하기 위한 해석 절차를 제시하였다. 제안된 방법은 한계평형법을 이용하는 결정론적 해석방법을 지반정수의 불확실성과 공간적 변동성을 고려할 수 있도록 확률론적 사면안정 해석으로 확장한다. 개발된 방법은 랜덤유한요소해석법과 같이 미리 임계파괴면을 가정하지 않으면서도 계산시간을 단축할 수 있다는 장점이 있다. 지정된 입력 확률분포함수와 자기상관함수를 따르는 2차원의 랜덤필드를 생성하기 위하여 Karhunen-Lo$\grave{e}$ve 전개법을 사용하였으며, 생성된 랜덤필드를 이용하여 확률론적 응답을 얻기 위해 Monte Carlo 시뮬레이션을 수행하였다. 개발된 해석기법의 적용성을 검토하고 지반정수의 공간적 변화가 확률론적 안정해석에 미치는 영향을 검토하기 위해 예제해석을 수행하였으며, 해석결과는 제안된 방법이 지반물성의 공간적 변동성에 따른 다양한 사면파괴 형태를 확률론적 사면안정 해석에 효과적으로 고려할 수 있음을 보여준다.

• Geomechanics and Engineering
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• 제10권6호
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• pp.709-726
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• 2016

Corrugated-core sandwich panels are prevalent for many applications in industries. The researches performed with the aim of optimization of such structures in the literature have considered a deterministic approach. However, it is believed that deterministic optimum points may lead to high-risk designs instead of optimum ones. In this paper, an effort has been made to provide a reliable and robust design of corrugated-core sandwich structures through stochastic and probabilistic multi-objective optimization approach. The optimization is performed using a coupling between genetic algorithm (GA), Monte Carlo simulation (MCS) and finite element method (FEM). To this aim, Prob. Design module in ANSYS is employed and using a coupling between optimization codes in MATLAB and ANSYS, a connection has been made between numerical results and optimization process. Results in both cases of deterministic and probabilistic multi-objective optimizations are illustrated and compared together to gain a better understanding of the best sandwich panel design by taking into account reliability and robustness. Comparison of results with a similar deterministic optimization study demonstrated better reliability and robustness of optimum point of this study.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.63-72
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• 2021

Mobile crowd-sensing (MCS) is a promising sensing paradigm that leverages mobile users with smart devices to perform large-scale sensing tasks in order to provide services to specific applications in various domains. However, MCS sensing tasks may not always be successfully completed or timely completed for various reasons, such as accidentally leaving the tasks incomplete by the users, asynchronous transmission, or connection errors. This results in missing sensing data at specific locations and times, which can degrade the performance of the applications and lead to serious casualties. Therefore, in this paper, we propose a missing data inference approach, called missing data approximation with probabilistic tensor factorization (MDI-PTF), to approximate the missing values as closely as possible to the actual values while taking asynchronous data transmission time and different sensing locations of the mobile users into account. The proposed method first normalizes the data to limit the range of the possible values. Next, a probabilistic model of tensor factorization is formulated, and finally, the data are approximated using the gradient descent method. The performance of the proposed algorithm is verified by conducting simulations under various situations using different datasets.

• 한국해양공학회지
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• 제32권4호
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• pp.215-221
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• 2018

In this study, the optimal design of a mooring system was carried out. Unlike almost all design methods, which are based on the deterministic method, this study focused on the probabilistic method. The probabilistic method, especially the design of experiment (DOE), could be a good way to cover some of the drawbacks of the deterministic approach. There various parameters for a mooring system, as widely known, including the weight, length, and stiffness of line. Scenarios for the mooring system parameters were produced using the Latin Hypercube Sampling method of the probabilistic approach. Next, a vessel-mooring system coupled analysis was performed in Orcaflex. A total of 50 scenarios were used in this study to optimize the initial design by means of a genetic algorithm. Finally, after determining the optimal process, a reliability analysis was performed to understand the system validity.

• 제어로봇시스템학회논문지
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• 제21권4호
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• pp.367-371
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• 2015

In this paper, a novel localization method for a monocular camera is proposed by using a feature-based probabilistic map. The localization of a camera is generally estimated from 3D-to-2D correspondences between a 3D map and an image plane through the PnP algorithm. In the computer vision communities, an accurate 3D map is generated by optimization using a large number of image dataset for camera pose estimation. In robotics communities, a camera pose is estimated by probabilistic approaches with lack of feature. Thus, it needs an extra system because the camera system cannot estimate a full state of the robot pose. Therefore, we propose an accurate localization method for a monocular camera using a probabilistic approach in the case of an insufficient image dataset without any extra system. In our system, features from a probabilistic map are projected into an image plane using linear approximation. By minimizing Mahalanobis distance between the projected features from the probabilistic map and extracted features from a query image, the accurate pose of the monocular camera is estimated from an initial pose obtained by the PnP algorithm. The proposed algorithm is demonstrated through simulations in a 3D space.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.2005-2010
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• 2004

As robots become more prevalent in human daily life, situations requiring interaction between humans and robots will occur more frequently. Therefore, human-robot interaction (HRI) is becoming increasingly important. Although robotics researchers have made many technical developments in their field, intuitive and easy ways for most common users to interact with robots are still lacking. This paper introduces a new approach to enhance human-robot interaction using a semantic symbol language and proposes a method to acquire the intentions of robot users. In the proposed approach, each semantic symbol represents knowledge about either the environment or an action that a robot can perform. Users'intentions are expressed by symbolized multimodal information. To interpret a users'command, a probabilistic approach is used, which is appropriate for interpreting a freestyle user expression or insufficient input information. Therefore, a first-order Markov model is constructed as a probabilistic model, and a questionnaire is conducted to obtain state transition probabilities for this Markov model. Finally, we evaluated our model to show how well it interprets users'commands.