• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.346-354
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• 1992

In the power-system, the active and reactive power levels of load bus randomly vary over days, months, and years which are stochastic in nature. This paper presents an algorithm for optimal Var planning considering the uncertainties of loads. The optimization problem is solved by a stochastic linear programming technique which can handle stochastic constraints to evaluate optimal Var requirement at load bus to maintain the voltage profile which results in probabilistic density function by stochastic Load Flow analysis within admissible range. The effectiveness of the proposed algorithm has been verified by the test on the IEEE-30 bus system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.280-285
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• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself. Through this process, we comfirmed advantages/disadvantages of four algorithms and identified application methods of four algorithms.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.863-865
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• 1988

This paper presents a optimal Var allocation algorithm for minimizing transmission line losses and improving voltage profile in a given system. In this paper, nodal input data is considered as Gaussian distribution with their mean value and their variance. A Stocastic Linear programming technique based on chance constrained method is applied, to solve the var allocation problem with probabilistic constraint. The test result in 6-Bus Model system showes that the voltage distribution of load buses is improved and the power loss is more reduced than before var allocation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.1
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• pp.31-38
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• 2018

Earthquake disasters that exceed the design criteria can pose significant threats to nuclear facilities. Seismic probabilistic safety assessment(PSA) is a probabilistic way to quantify such risks. Accordingly, seismic PSA has been applied to domestic and overseas nuclear power plants, and the safety of nuclear power plants was evaluated and prepared against earthquake hazards. However, there were few examples where seismic PSA was applied in case of a research reactor with a relatively small size compared to nuclear power plants. Therefore, in this study, seismic PSA technique was applied to actually completed research reactor to analyze its safety. Also, based on these results, the optimization study on the seismic capacity of the system constituting the research reactor was carried out. As a result, the possibility of damage to the core caused by the earthquake hazard was quantified in the research reactor and its safety was confirmed. The optimization study showed that the optimal seismic capacity distribution was obtained to ensure maximum safety at a low cost compared with the current design. These results, in the future, can expect to be used as a quantitative indicator to effectively improve the safety of the research reactor with respect to earthquakes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2895-2912
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• 2014

Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.

• Journal of the Korea Concrete Institute
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• v.18 no.1 s.91
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• pp.101-108
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• 2006

Accurate prediction of time-dependent column shortening is essential for tall buildings in both strength and serviceability aspects. The uncertainty associated with assumed values for concrete properties such as strength, creep, and shrinkage coefficients should be considered for the prediction of time-dependent column shortening of tall concrete buildings. In this study, the column shortenings of 41-story tall concrete building are predicted using monte carlo simulation technique based on the probabilistic analysis. The probabilistic column shortenings considering confidence intervals are compared with the actual column shortenings by field measurement. The time-dependent strains measured at tall bearing wall building were generally lower than the predicted strains and the measured values fell within a range ${\mu}-1.64$, confidence level 90%.

• Toxicological Research
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• v.28 no.2
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• pp.129-137
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• 2012

Probabilistic ecological risk assessment (PERA) for deriving ecological protective concentration (EPC) was previously suggested in USA, Australia, New Zealand, Canada, and Netherland. This study suggested the EPC of cadmium (Cd) based on the PERA to be suitable to Korean aquatic ecosystem. First, we collected reliable ecotoxicity data from reliable data without restriction and reliable data with restrictions. Next, we sorted the ecotoxicity data based on the site-specific locations, exposure duration, and water hardness. To correct toxicity by the water hardness, EU's hardness corrected algorithm was used with slope factor 0.89 and a benchmark of water hardness 100. EPC was calculated according to statistical extrapolation method (SEM), statistical extrapolation $method_{Acute\;to\;chronic\;ratio}$ ($SEM_{ACR}$), and assessment factor method (AFM). As a result, aquatic toxicity data of Cd were collected from 43 acute toxicity data (4 Actinopterygill, 29 Branchiopoda, 1 Polychaeta, 2 Bryozoa, 6 Chlorophyceae, 1 Chanophyceae) and 40 chronic toxicity data (2 Actinopterygill, 23 Branchiopoda, 9 Chlorophyceae, 6 Macrophytes). Because toxicity data of Cd belongs to 4 classes in taxonomical classification, acute and chronic EPC (11.07 ${\mu}g/l$ and 0.034 ${\mu}g/l$, respectively) was calculated according to SEM technique. These values were included in the range of international EPCs. This study would be useful to establish the ecological standard for the protection of aquatic ecosystem in Korea.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.257-267
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• 2000

Mostly, the economic analyses for replacement of major components of nuclear power Plants(NPPs) have been performed in deterministic ways. However, the analysis results are more or less affected by the uncertainties associated with input variables. Therefore, it is desirable to use a probabilistic economic analysis method to properly consider uncertainty of real problem. In this paper, the probabilistic economic analysis method and decision analysis technique are briefly described. The probabilistic economy analysis method using decision analysis will provide efficient and accurate way of economic analysis for the repair and/or replace mai or components of NPPs.

• Journal of the Korean Geotechnical Society
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• v.32 no.11
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• pp.31-42
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• 2016

Usually the seismic stability analysis of slope uses the pseudostatic analysis considering the inertial force by the earthquake as a static load. Geostructures such as slope include the uncertainty of soil properties. Therefore, it is necessary to consider probabilistic method for stability analysis. In this study, the probabilistic stability analysis of slope considering the uncertainty of soil properties has been performed. The fragility curve that represents the probability of exceeding limit state of slope as a function of the ground motion has been established. The Monte Carlo Simulation (MCS) has been implemented to perform the probabilistic stability analysis of slope with pseudostatic analysis. A procedure to develop the fragility curve by the pseudostatic horizontal acceleration has been presented by calculating the probability of failure based on the Artificial Neural Network (ANN) based response surface technique that reduces the required time of MCS. The results showed that the proposed method can get the fragility curve that is similar to the direct MCS-based fragility curve, and can be efficiently used to reduce the analysis time.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.799-811
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• 2006

As an alternative measure to replace reactive stance with proactive one, a risk based management scheme has been commonly applied to enhance public satisfaction on water service by providing a higher creditable solution to handle a rehabilitation problem of pipe having high potential risk of leaks. This study intended to examine the feasibility of a simulation model to predict a recurrence probability of pipe leaks. As a branch of the data mining technique, probabilistic neural network (PNN) algorithm was applied to infer the extent of leaking recurrence probability of water network. PNN model could classify the leaking level of each unit segment of the pipe network. Pipe material, diameter, C value, road width, pressure, installation age as input variable and 5 classes by pipe leaking probability as output variable were built in PNN model. The study results indicated that it is important to pay higher attention to the pipe segment with the leak record. By increase the hydraulic pipe pressure to meet the required water demand from each node, simulation results indicated that about 6.9% of total number of pipe would additionally be classified into higher class of recurrence risk than present as the reference year. Consequently, it was convinced that the application of PNN model incorporated with a data base management system of pipe network to manage municipal water distribution network could make a promise to enhance the management efficiency by providing the essential knowledge for decision making rehabilitation of network.