• 제목/요약/키워드: Probabilistic modeling

검색결과 230건 처리시간 0.025초

Obstacle Modeling for Environment Recognition of Mobile Robots Using Growing Neural Gas Network

  • Kim, Min-Young;Hyungsuck Cho;Kim, Jae-Hoon
    • International Journal of Control, Automation, and Systems
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    • 제1권1호
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    • pp.134-141
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    • 2003
  • A major research issue associated with service robots is the creation of an environment recognition system for mobile robot navigation that is robust and efficient on various environment situations. In recent years, intelligent autonomous mobile robots have received much attention as the types of service robots for serving people and industrial robots for replacing human. To help people, robots must be able to sense and recognize three dimensional space where they live or work. In this paper, we propose a three dimensional environmental modeling method based on an edge enhancement technique using a planar fitting method and a neural network technique called "Growing Neural Gas Network." Input data pre-processing provides probabilistic density to the input data of the neural network, and the neural network generates a graphical structure that reflects the topology of the input space. Using these methods, robot's surroundings are autonomously clustered into isolated objects and modeled as polygon patches with the user-selected resolution. Through a series of simulations and experiments, the proposed method is tested to recognize the environments surrounding the robot. From the experimental results, the usefulness and robustness of the proposed method are investigated and discussed in detail.in detail.

Reliability-based assessment of American and European specifications for square CFT stub columns

  • Lu, Zhao-Hui;Zhao, Yan-Gang;Yu, Zhi-Wu;Chen, Cheng
    • Steel and Composite Structures
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    • 제19권4호
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    • pp.811-827
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    • 2015
  • This paper presents a probabilistic investigation of American and European specifications (i.e., AISC and Eurocode 4) for square concrete-filled steel tubular (CFT) stub columns. The study is based on experimental results of 100 axially loaded square CFT stub columns from the literature. By comparing experimental results for ultimate loads with code-predicted column resistances, the uncertainty of resistance models is analyzed and it is found that the modeling uncertainty parameter can be described using random variables of lognormal distribution. Reliability analyses were then performed with/without considering the modeling uncertainty parameter and the safety level of the specifications is evaluated in terms of sufficient and uniform reliability criteria. Results show that: (1) The AISC design code provided slightly conservative results of square CFT stub columns with reliability indices larger than 3.25 and the uniformness of reliability indices is no better because of the quality of the resistance model; (2) The uniformness of reliability indices for the Eurocode 4 was better than that of AISC, but the reliability indices of columns designed following the Eurocode 4 were found to be quite below the target reliability level of Eurocode 4.

기하정보 기반 이상탐지분석을 이용한 BIM 개별 부재 IFC 분류 무결성 검토에 관한 연구 (Using Geometry based Anomaly Detection to check the Integrity of IFC classifications in BIM Models)

  • 구본상;신병진
    • 한국BIM학회 논문집
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    • 제7권1호
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    • pp.18-27
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    • 2017
  • Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and thus compromise the validity of IFC. This research explored precedent work by Krijnen and Tamke, who suggested ways to automate the mapping of IFC classes using a machine learning technique, namely anomaly detection. The technique incorporates geometric features of individual components to find outliers among entities in identical IFC classes. This research primarily focused on applying this approach on two architectural BIM models and determining its feasibility as well as limitations. Results indicated that the approach, while effective, misclassified outliers when an IFC class had several dissimilar entities. Another issue was the lack of entities for some specific IFC classes that prohibited the anomaly detection from comparing differences. Future research to improve these issues include the addition of geometric features, using novelty detection and the inclusion of a probabilistic graph model, to improve classification accuracy.

순산 모델링 기법을 이용한 단열크기 추정방법 고찰 (Verification on the Fracture Size Estimation Using Forward Modeling Approach)

  • 김경수;김천수;배대석;정지곤
    • 지질공학
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    • 제8권1호
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    • pp.1-12
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    • 1998
  • 단열의 기하학적 인자 중에서 단역크기는 지반공학적 및 수리지질학적으로 중요한 인자임에도 불구하고 측정과 분석단계에서 불확실성이 가장 크게 작용한는 요소이다. 본 연구는 단열크기 추정을 위한 연구동향을 소개하고, 최근 제시된 순산 모델링(forword modeling) 기법을 이용한 추정방법에 대하여 조가 단면적과 단열 단면적과의 차이에 따른 쿄차확률관계 및 단열조와 조사면이 이루는 방향에 따른 교차확률의 변화에 대하여 모사적 검증을 시도하였다. 검증 결과 순산 모델링 기법을 이용한 단열크기 추정방법은 동굴 벽면이나 시추공,노두면에서 단열조별로 확률을 알고, 단열길이의 확률분포특성을 근사하게 추정할 수 있다면 실제 단열크기를 알아 낼 수 있는 방법으로 적용 가능하다고 인정된다.

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Reliability Analysis for Structure Design of Automatic Ocean Salt Collector Using Sampling Method of Monte Carlo Simulation

  • Song, Chang Yong
    • 한국해양공학회지
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    • 제34권5호
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    • pp.316-324
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    • 2020
  • This paper presents comparative studies of reliability analysis and meta-modeling using the sampling method of Monte Carlo simulation for the structure design of an automatic ocean salt collector (AOSC). The thickness sizing variables of structure members are considered as random variables. Probabilistic performance functions are selected from strength performances evaluated via the finite element analysis of an AOSC. The sampling methods used in the comparative studies are simple random sampling and Sobol sequences with varied numbers of sampling. Approximation methods such as the Kriging model is applied to the meta-model generation. Reliability performances such as the probability failure and distribution are compared based on the variation of the sampling method of Monte Carlo simulation. The meta-modeling accuracy is evaluated for the Kriging model generated from the Monte Carlo simulation and Sobol sequence results. It is discovered that the Sobol sequence method is applicable to not only to the reliability analysis for the structural design of marine equipment such as the AOSC, but also to Kriging meta-modeling owing to its high numerical efficiency.

화원 위치 추정을 위한 베이시안 추정 기반의 모델링 및 시뮬레이션 연구 (Study on Modeling and Simulation for Fire Localization Using Bayesian Estimation)

  • 김태완;김수찬;김종환
    • 대한조선학회논문집
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    • 제58권6호
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    • pp.424-430
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    • 2021
  • Fire localization is a key mission that must be preceded for an autonomous fire suppression system. Although studies using a variety of sensors for the localization are actively being conducted, the fire localization is still unfinished due to the high cost and low performance. This paper presents the modeling and simulation of the fire localization estimation using Bayesian estimation to determine the probabilistic location of the fire. To minimize the risk of fire accidents as well as the time and cost of preparing and executing live fire tests, a 40m × 40m-virtual space is created, where two ultraviolet sensors are simulated to rotate horizontally to collect ultraviolet signals. In addition, Bayesian estimation is executed to compute the probability of the fire location by considering both sensor errors and uncertainty under fire environments. For the validation of the proposed method, sixteen fires were simulated in different locations and evaluated by calculating the difference in distance between simulated and estimated fire locations. As a result, the proposed method demonstrates reliable outputs, showing that the error distribution tendency widens as the radial distance between the sensor and the fire increases.

마코프 체인 프로세스를 적용한 해양사고 발생 예측 (Prediction of Marine Accident Frequency Using Markov Chain Process)

  • 장은진;임정빈
    • 한국항해항만학회:학술대회논문집
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    • 한국항해항만학회 2019년도 추계학술대회
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    • pp.266-266
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    • 2019
  • 해마다 증가하고 있는 해양사고는 기관고장, 충돌, 좌초, 화재 등 다양하게 발생하고 있다. 이러한 해양사고는 대형 인명사고의 위험이 있어 사전에 사고를 예방 하는 게 무엇보다 중요하다. 이를 위해서는 해양사고 발생을 사전에 예측하고 이에 대응할 수 있는 예측 체계가 요구된다. 본 연구에서는 과거에 발생한 데이터를 근거로 미래를 예측할 수 있는 마코프 체인 프로세스(Markov Chain Process)를 적용하여 해양사고 발생을 사전에 예측하기 위한 모델링을 제안한다. 제시된 모델링을 적용하여 미래 발생 가능한 해양사고 발생 확률을 산출하고 실제 발생한 빈도와 비교하였다. 또한 많이 사용되는 다른 예측 분석 방법과 비교하여 예측의 정확성을 측정하였다. 이를 통해 해양사고 발생에 관한 예측 체계를 마련하는데 하나의 확률 모형을 제안하였으며, 나아가 다양한 해양사고의 문제를 예측하는데 기여할 것으로 기대된다.

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Multi-hazard vulnerability modeling: an example of wind and rain vulnerability of mid/high-rise buildings during hurricane events

  • Zhuoxuan Wei;Jean-Paul Pinelli;Kurtis Gurley;Shahid Hamid
    • Wind and Structures
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    • 제38권5호
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    • pp.355-366
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    • 2024
  • Severe natural multi-hazard events can cause damage to infrastructure and economic losses of billions of dollars. The challenges of modeling these losses include dependency between hazards, cause and sequence of loss, and lack of available data. This paper presents and explores multi-hazard loss modeling in the context of the combined wind and rain vulnerability of mid/high-rise buildings during hurricane events. A component-based probabilistic vulnerability model provides the framework to test and contrast two different approaches to treat the multi-hazards: In one, the wind and rain hazard models are both decoupled from the vulnerability model. In the other, only the wind hazard is decoupled, while the rain hazard model is embedded into the vulnerability model. The paper presents the mathematical and conceptual development of each approach, example outputs from each for the same scenario, and a discussion of weaknesses and strengths of each approach.

고준위 방사성 폐기물 처분장 확률론적 안전성평가 신뢰도 제고를 위한 입력 파라미터 연속 베이지안 업데이팅 모듈 개발 (Sequential Bayesian Updating Module of Input Parameter Distributions for More Reliable Probabilistic Safety Assessment of HLW Radioactive Repository)

  • 이연명;조동건
    • 방사성폐기물학회지
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    • 제18권2호
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    • pp.179-194
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    • 2020
  • 기존의 확률론적 안전성 평가의 신뢰도 제고를 위하여 잘 알려진 입력 파라미터의 일반적인 분포에 새롭게 측정된 신뢰도 있는 데이터를 결합하여 사후분포를 구할 수 있는 베이지안 업데이팅 방법론을 제안하였다. 마코프체인 몬테 칼로 샘플링 기법의 알고리듬을 통한 GoldSim 모듈도 개발하였다. 복수의 입력 파라미터의 사전분포에 대해 연속적으로 사후분포를 구해낼 수 있는 베이지안 업데이팅이 가능하도록 개발된 이 모듈을 GoldSim 템플릿 형태의 기존의 GSTSPA 프로그램으로 이행하여 보다 신뢰도 있는 확률론적 방사성폐기물 처분 시스템 안전성 평가가 가능하도록 하였다. 이는 기존에 존재하는 사전분포의 일반적인 형태는 취하되 새롭게 얻어지는 실제 측정치나 전문가들의 의견을 기존의 분포에 적용하여 보다 더 높은 믿음을 갖는 입력 파라미터의 사후분포를 얻을 수 있게 한다. 균열암반 내 핵종 이동에 관련된 몇 개의 입력 파라미터의 사전분포의 세차례의 연속적 업데이팅을 통해 프로그램의 유용성도 예시하였다. 이 연구를 통하여 처분시스템과 같이 장기적 평가가 필요하고 넓은 모델링 지역을 가지며 측정된 입력자료가 부족한 경우 보다 더 믿음직한 방법으로 안전성 평가를 수행할 수 있는 것을 보였다.

Probabilistic sensitivity analysis of multi-span highway bridges

  • Bayat, M.;Daneshjoo, F.;Nistico, N.
    • Steel and Composite Structures
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    • 제19권1호
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    • pp.237-262
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    • 2015
  • In this study, we try to compare different intensity measures for evaluating nonlinear response of bridge structure. This paper presents seismic analytic fragility of a three-span concrete girder highway bridge. A complete detail of bridge modeling parameters and also its verification has been presented. Fragility function considers the relationship of intensities of the ground motion and probability of exceeding certain state of damage. Incremental dynamic analysis (IDA) has been subjected to the bridge from medium to strong ground motions. A suite of 20 earthquake ground motions with different range of PGAs are used in nonlinear dynamic analysis of the bridge. Complete sensitive analyses have been done on the response of bridge and also efficiency and practically of them are studied to obtain a proficient intensity measure for these types of structure by considering its sensitivity to the period of the bridge. Three dimensional finite element (FE) model of the bridge is developed and analyzed. The numerical results show that the bridge response is very sensitive to the earthquake ground motions when PGA and Sa (Ti, 5%) are used as intensity measure (IM) and also indicated that the failure probability of the bridge system is dominated by the bridge piers.