• 대한전기학회논문지:전력기술부문A
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• 제50권2호
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• pp.53-57
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• 2001

This paper presents the new methods of fault diagnosis through multiple alarm processing of protective relays and circuit breakers in power systems using probabilistic neural networks. In this paper, fault section detection neural network (FSDNN) for fault diagnosis is designed using the alarm information of relays or circuit breakers. In contrast to conventional methods, the proposed FSDNN determines the fault section directly and fast. To show the possibility of the proposed method, it is simulated through simulation panel for Sinyangsan substation system in KEPCO (Korea Electric Power Corporation) and the case studies show the effectiveness of the probabilistic neural network mehtod for the fault diagnosis.

• International Journal of Advanced Culture Technology
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• 제5권1호
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• pp.76-82
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• 2017

An image hash, a discriminative and robust summary of an image, should be robust against quality-preserving signal processing steps, while being pairwise independent for perceptually different inputs. In order to improve the hash matching performance, this paper proposes a probabilistic dissimilarity matching. Instead of extracting the binary hash from the query image, we compute the probability that the intermediate hash vector of the query image belongs to each quantization bin, which is referred to as soft quantization binning. The probability is used as a weight in comparing the binary hash of the query with that stored in a database. A performance evaluation over sets of image distortions shows that the proposed probabilistic matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.

• Earthquakes and Structures
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• 제17권1호
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• pp.91-99
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• 2019

Fragility analysis is an effective tool that is frequently used for seismic risk assessment of bridges. There are three different approaches to derive a fragility curve: experimental, empirical and analytical. Both experimental and empirical methods to derive fragility curve are based on past earthquake reports and expert opinions which are not suitable for all bridges. Therefore, analytical fragility analysis becomes important. Nonlinear time history analysis is commonly used which is the most reliable method for determining probabilistic demand models. In this study, to determine the probabilistic demand models of bridges, time history analyses were performed considering both material and geometrical nonlinearities. Serviceability limit states for three different service velocities were considered as a performance goal. Also, support displacements, component yielding and collapse limits were taken into account. Both serviceability and component fragility were derived by using maximum likely hood methods. Finally, the seismic performance and critical members of the bridge were probabilistically determined and clearly presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Smart Structures and Systems
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• 제32권3호
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• pp.167-177
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• 2023

A probabilistic micromechanical framework is proposed to quantify numerically the self-healing capabilities of polymers containing microcapsules. A two-step self-healing process is designed in this study: A probabilistic micromechanical framework based on the ensemble volume-averaging method is derived for the polymers, and a hitting probability model combined with a crack nucleation model is then utilized for encountering microcapsules and microcracks. Using this framework, a series of parametric investigations are performed to examine the influence of various model parameters (e.g., the volume fraction of microcapsules, microcapsule radius, radius ratio of microcracks to microcapsules, microcrack aspect ratio, and scale parameter) on the self-healing capabilities of the polymers. The proposed framework is also implemented into a finite element code to solve the self-healing behavior of tapered double cantilever beam specimens.

• 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.

• 한국물환경학회지
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• 제25권4호
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• pp.494-501
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• 2009

Probabilistic Ecological risk assessment (PERA) is extensive approach to qualify and quantify risk on the multi species based on species sensitivity distribution (SSD). As a while, deterministic ecological risk assessment (DERA) considers the comparison of predicted no-effect concentration (PNEC) and predicted exposure concentration (PEC). DERA is used to determine if there is potential risk or no risk, and it doesn't consider the nature variability and the species sensitivity. But PERA can be more realistic and reasonable approach to estimate likelihood or risk. In this study, we compared PERA used in developed countries, and proposed PERA applicable for the Korean water environment. Taxonomic groups were classified as "class" level including Actinopterygill, Branchiopoda, Chlorophyceae, Maxillapoda, Insects, Bivalvia, Gastropoda, Secernentea, Polychaeta, Monocotyldoneae, and Chanophyceae in this study. Statistical extrapolation method (SEM), statistical extrapolation method $_{acutechronicratio}$ ($SEM_{ACR}$) and assessment factor method (AFM) were used to calculate the ecological protective concentration based on qualitative and quantitative levels of taxonomic toxicity data. This study would be useful to establish the PERA for the protection of aquatic ecosystem in Korea.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1990년도 가을 학술발표회 논문집
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• pp.22-27
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• 1990

Since an eigenvalue problem in structural analysis has been recognized as an important process for the assessment of structural strength, it is usually to be carried out the eigenvalue analysis or buckling analysis of structures when the compression behabiour of the member is dorminant. In general, various variables involved in the eigenvalue problem have also shown their variability. So it is natural to apply the probabilistic analysis into such problem. Since the limit state equation for the eigenvalue analysis or buckling reliability analysis is expressed implicitly in terms of random variables involved, the probabilistic finite element method is combined with the conventional reliability method such as MVFOSM and AFOSM for the determination of probability of failure due to buckling. The accuracy of the results obtained by this method is compared with results from the Monte Carlo simulations. Importance sampling method is specially chosen for overcomming the difficulty in a large simulation number needed for appropriate accurate result. From the results of the case study, it is found that the method developed here has shown good performance for the calculation of probability of buckling failure and could be used for checking the safety of the calculation of probability of buckling failure and could be used for checking the safely of frame structure which might be collapsed by either yielding or buckling.

• 대한전기학회논문지:전력기술부문A
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• 제51권3호
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• pp.153-161
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• 2002

In this paper, we propose a Probabilistic Tabu Search(PTS) method for hydro-thermal scheduling. Hydro scheduling has many constraints and very difficult to solve the optical schedule because it has many local minima. To solve the problem effectively, the proposed method uses two procedures, one is Tabu search procedure that plays a role in local search, and the other is Restarting procedure that enables to diversify its search region. To adjust Parameters such as a reducing rate and initial searching region, search strategy is selected according to its probability after restarting procedure. Dynamic decoding method was also used to restrict a search region and to handle water balance constraints. In order to show the usefulness of the proposed method, the PTS is applied on two cases which have independent or dependent hydro plants and compared to those of other method. The simulation results show it is very efficient and useful algorithm to solve the hydro-thermal scheduling problem.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 추계 종합학술대회 논문집
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• pp.16-20
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• 2006

웹문서에 대한 콘텐츠 마이닝에서 레이블이 없는 엔티티 인식과 하위정보 및 추출결과의 정보통합은 중요하다. 본 논문에서는 레이블이 없는 엔티티를 인식하기 위해 베이지언 모델에 기반한 확률 기반 인식 방법을 제안한다. 또한 웹문서에 존재하는 하위링크정보를 이용하고, 추출한 중복된 결과를 통합할 수 있는 방안을 제시한다. 실험결과, 확률기반 엔티티인식과 정보통합을 수행한 방법이 가장 우수한 성능을 보임을 알 수 있다.