• 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.

• ETRI Journal
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• 제33권6호
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• pp.924-934
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• 2011

Sensor networks play an important role in making the dream of ubiquitous computing a reality. With a variety of applications, sensor networks have the potential to influence everyone's life in the near future. However, there are a number of issues in deployment and exploitation of these networks that must be dealt with for sensor network applications to realize such potential. Localization of the sensor nodes, which is the subject of this paper, is one of the basic problems that must be solved for sensor networks to be effectively used. This paper proposes a probabilistic support vector machine (SVM)-based method to gain a fairly accurate localization of sensor nodes. As opposed to many existing methods, our method assumes almost no extra equipment on the sensor nodes. Our experiments demonstrate that the probabilistic SVM method (PSVM) provides a significant improvement over existing localization methods, particularly in sparse networks and rough environments. In addition, a post processing step for PSVM, called attractive/repulsive potential field localization, is proposed, which provides even more improvement on the accuracy of the sensor node locations.

• 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.

• 한국지진공학회논문집
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• 제8권6호통권40호
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• pp.23-29
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• 2004

원전 구조물 및 기기의 내진설계를 위한 설계지진의 설정에는 결정론적 방법이나 확률론적 방법이 사용되어 왔다. 최근에는 확률론적 지진재해도 분석이 일반화 되면서 확률론적으로 설계지진 및 평가용 지진의 설정 방법이 합리적인 방법으로서 인식되어 많이 사용되고 있다. 우리나라의 경우 원전부지에 대한 확률론적 지진재해도 분석이 확률론적 지진위험도 평가의 일환으로 대부분 완료되어 있다. 본 연구에서는 확률론적 지진재해도의 재분해를 통하여 확률론적 시나리오 지진을 산정할 수 있는 기법을 확립하고 국내 원전 부지에 대한 확률론적 지진재해도 분석 결과를 이용하여 계산 예를 수행하였다. 이 기법을 사용하면 내진설계 및 내진안전성 평가에 활용할 수 있는 확률론적 시나리오 지진을 설정할 수 있어 매우 유용한 것으로 판단되며 합리적인 시나리오 지진의 산정을 위해서는 합리적인 지진구역도 및 감쇄식의 개발이 필요하다.

• 전기학회논문지
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• 제59권8호
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• pp.1366-1373
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• 2010

Purpose of the electric power system planning and operation is to supply the electric energy to customers continuously and economically. With the mutual exclusive laws of nature between reliability and economic, finding the meeting point is very important but not easy. Commonly the probabilistic reliability indices of the electric power systems are represented with negatively. And the effectiveness of FACTS on the probabilistic reliability could not be reflected with common methods. In this paper, a method to evaluate the probabilistic arrival power at each load point is presented. With this new proposed method, probabilistic reserve margin at load points can be calculated and which can be used with positive reliability index also. Using the P-V analysis, the voltage stability is considered in reliability evaluation. It is expected that the proposed method will be useful expecially in reliability evaluation of electric power system which has voltage restriction.

• 한국음향학회지
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• 제25권8호
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• pp.375-382
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• 2006

본 논문에서는 음성신호에 포함된 감정정보를 자동으로 식별하는 방법과 특정 감정을 검출하는 방법에 대해 다룬다. 자동 감정식별 및 검출을 위해 장구간 (long-term) 음향 특징을 사용하였고, F-score 기반의 특징선택 기법을 적용하여 최적의 특징 파라미터들을 선정하였다. 기존의 일반적인 SVM을 확률출력 SVM으로 변환하여 감정식별 및 감정검출 시스템을 구축하였으며, 가설검정에 기반한 감정검출을 위해 세 가지의 대수 우도비 (log-likelihood) 근사법을 제안하여 그 성능을 비교하였다. SUSAS 데이터베이스를 사용한 실험 결과, F-score를 이용한 특징선택 기법에 의해 감정식별 성능이 향상되었으며, 확률출력 SVM의 유효성을 검증할 수 있었다. 감정검출의 경우, 제안한 방법에 의해 91.3%의 정확도로 화난 감정을 검출할 수 있었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• 한국압력기기공학회 논문집
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• 제12권1호
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• pp.56-61
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• 2016

After Japanese Fukushima nuclear power plant accident caused by the beyond design basis earthquake and tsunami, it has turned to be a major challenge for nuclear safety. IAEA, US NRC and EU have provided new safety design standards for beyond design basis event, Domestic regulatory bodies have also enacted guidances for licensees and applicants on additional methods related to beyond design basis events. This paper describes several evaluation methods for applying to nuclear power plants piping for beyond design basis earthquake. As a results, energy method based on the absorbed energy on nuclear power plant, deterministic method following design code and theory, experience method considering past earthquake data and information and probabilistic methods similar to probabilistic risk assessment were reviewed.

• 대한전기학회논문지:전력기술부문A
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• 제51권9호
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• pp.425-432
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• 2002

This Paper illustrates a method for evaluating nodal probabilistic production cost using the CMELDC. A new method for constructing CMELDC(CoMposite Power System Equivalent Load Duration Curve) has been developed by authors. The CMELDC can be obtained by convolution integral processing between the probability distribution functions of the fictitious generators outage capacity and the load duration curves at each load point. In general, if complex operating conditions are involved and/or the number of severe events is relatively large, Monte Carlo methods are more efficient. Because of that reason, Monte Carlo Methods are applied for the construction of CMELDC in this study. And IEEE-RTS 24 buses model is used as our case study with satisfactory results.

• Smart Structures and Systems
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• 제23권2호
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• pp.185-194
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• 2019

The probabilistic tsunami risk assessment of large coastal areas is challenging because the inland propagation of a tsunami wave requires an accurate numerical model that takes into account the interaction between the ground, the infrastructures, and the wave itself. Classic mesh-based methods face many challenges in the propagation of a tsunami wave inland due to their ever-moving boundary conditions. In alternative, mesh-less based methods can be used, but they require too much computational power in the far-field. This study proposes a hybrid approach. A mesh-based method propagates the tsunami wave from the far-field to the near-field, where the influence of the sea floor is negligible, and a mesh-less based method, smooth particle hydrodynamic, propagates the wave onto the coast and inland, and takes into account the wave structure interaction. Nowadays, this can be done because the advent of general purpose GPUs made mesh-less methods computationally affordable. The method is used to simulate the inland propagation of the 2004 Indian Ocean tsunami off the coast of Indonesia.