• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.2
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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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• v.33 no.6
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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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• v.17 no.1
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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.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.6 s.40
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• pp.23-29
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• 2004

The design earthquake used for the seismic analysis and design of NPP (Nuclear Power Plant) is determined by the deterministic or probabilistic methods. The probabilistic seismic hazard analysis(PSHA) for the nuclear power plant sites was performed for the probabilistic seismic risk assessment. The probabilistic seismic hazard analysis for the nuclear power plant site had been completed as a part of the probabilistic seismic risk assessment. The probabilistic method become a resonable method to determine the design earthquakes for NPPs. In this study, the defining method of the probability based scenario earthquake was established, and as a sample calculation, the probability based scenario earthquakes were estimated by the de-aggregation of the probabilistic seismic hazard. By using this method, it is possible to define the probability based scenario earthquakes for the seismic design and seismic safety evaluation of structures. It is necessary to develop the rational seismic source map and the attenuation equations for the development of reasonable scenario earthquakes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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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.

• The Journal of the Acoustical Society of Korea
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• v.25 no.8
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• pp.375-382
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• 2006

This paper is about how to identify emotional information and how to detect a specific emotion from speech signals. For emotion identification and detection task. we use long-term acoustic feature parameters and select the optimal Parameters using the feature selection technique based on F-score. We transform the conventional SVM into probabilistic output SVM for our emotion identification and detection system. In this paper we propose three approximation methods for log-likelihoods in a hypothesis test and compare the performance of those three methods. Experimental results using the SUSAS database showed the effectiveness of both feature selection and Probabilistic output SVM in the emotion identification task. The proposed methods could detect anger emotion with 91.3% correctness.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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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.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.12 no.1
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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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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• v.23 no.2
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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.