• Journal of Korean Society of Water and Wastewater
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• v.29 no.2
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• pp.243-249
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• 2015

To improve stability of the water resources that were seriously affected by climate change and various environmental effects and to supply the clean water always, continuous efforts are essential. Provision of measures with respect of hardware is basically essential to improve the water resources stability due to the topographic characteristic in Korea. However, building a new dam becomes gradually very difficult because of a hardship in selecting right places, opposition forces such as environment and local residents, negative publicity for large civil engineering projects, and so on. The present study, therefore, proposes the Blue dam as an alternative for securing the water resources of a new concept considering domestic conditions. To evaluate the effect of the Blue dam, the Hec-ResSim model is used and the probabilistic discharge flow rate is applied. As a result, when Dam Yeongcheon is applied as a study area, securing water resources of 14 million tons are predicted be secured and the flood control of 15.4 million tons is expected, in comparison with operation of the existing dam only. Consequently, Blue dams are supposed to carry out the function of securing water resources, controling flood, maintaining eco-environmental instream flow, generating hydroelectric power, and providing spaces for recreational activities.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.836-850
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• 2006

Nowadays, due to the rapidly increasing number of package software products, quality test has been emphasized for package software products. When testing software products, one of the most important factors is to select metrics which form the bases for tests. In this paper, the types of package software are represented as characteristic vectors having probabilistic relationships with metrics. The characteristic vectors could be regarded as indicators of software type. To assign the metrics for each software type, the past test metrics are collected and analyzed. Using Bayesian belief network, the dependency relationship network of the characteristic vectors and metrics is constructed. The dependency relationship network is then used to find the proper metrics for the test of new package software products.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.4
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• pp.99-106
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• 2014

In order to study the reasonable design thermal loads, the steel box girder bridge specimen which have no concrete slab was manufactured with the real size dimension. The temperature data were measured for 5 month at the 18 thermo gauges which were attached according to height. The temperature differences between the top and bottom flange in steel box girder specimen were calculated and the temperature gradient models were proposed by the probabilistic method. This proposed model showed a correlation of approximately 97% when compared with the similar model of Euro Code. Thus, the temperature gradient models which were suggested in this study may be used as the basis data in calculating the design load temperature.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1307-1312
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• 2015

The recent adoption of Bluetooth LE technology in smart phones triggered commercial interest in RSSI-based positioning technology. Estimation error in RSSI measurement due to the antenna pattern, multipath fading, environmental noise has to be considered for designing beacon systems. The paper proposes an analysis method and beacon planning rules for a small and isolated indoor service area, based on probabilistic model of RSSI estimation error. As an practically important guide, the beacons have to be installed at the boundary of the service area to minimize the maximum position error, whereas the beacons have to be evenly distributed in the service space to minimize the average estimation error.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.367-376
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• 2015

Today, big data has become a hot keyword in that big data may be defined as collection of data sets so huge and complex that it becomes difficult to process by traditional methods. Clustering method is to identify the information in a big database by assigning a set of objects into the clusters so that the objects in the same cluster are more similar to each other clusters. The similarity measures being used in the cluster analysis may be classified into various types depending on the nature of the data. In this paper, we computed upper and lower limits for probability interestingness measure based similarity measures without marginal probability such as Yule I and II, Michael, Digby, Baulieu, and Dispersion measure. And we compared these measures by real data and simulated experiment. By Warrens (2008), Coefficients with the same quantities in the numerator and denominator, that are bounded, and are close to each other in the ordering, are likely to be more similar. Thus, results on bounds provide means of classifying various measures. Also, knowing which coefficients are similar provides insight into the stability of a given algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.289-293
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• 2005

Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.

• Computers and Concrete
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• v.15 no.2
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• pp.259-277
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• 2015

The lack of experimental studies on the mechanical behavior of reinforced concrete (RC) haunched beams leads to difficulties in statistical and reliability analyses. This study performs stochastic and reliability analyses of the ultimate shear capacity of RC haunched beams based on nonlinear finite element analysis. The main aim of this study is to investigate the influence of uncertainty in material properties and geometry parameters on the mechanical performance and shear capacity of RC haunched beams. Firstly, 65 experimentally tested RC haunched beams and prismatic beams are analyzed via deterministic nonlinear finite element method by a special program (ATENA) to verify the efficiency of utilized numerical models, the shear capacity and the crack pattern. The accuracy of nonlinear finite element analyses is verified by comparing the results of nonlinear finite element and experiments and both results are found to be in a good agreement. Afterwards, stochastic analyses are performed for each beam where the RC material properties and geometry parameters are assigned to take probabilistic values using an advanced simulating procedure. As a result of stochastic analysis, statistical parameters are determined. The statistical parameters are obtained for resistance bias factor and the coefficient of variation which were found to be equal to 1.053 and 0.137 respectively. Finally, reliability analyses are accomplished using the limit state functions of ACI-318 and ASCE-7 depending on the calculated statistical parameters. The results show that the RC haunched beams have higher sensitivity and riskiness than the RC prismatic beams.

• Journal of the Korea Society for Simulation
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• v.23 no.2
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• pp.65-72
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• 2014

Sensor nodes are easily compromised by attacker when which are divided in open environment. The attacker may inject false report and false vote attack through compromised sensor node. These attacks interrupt to transmission legitimate report or the energy of sensor node is exhausted. PVFS are proposed by Li and Wu for countermeasure in two attacks. The scheme use inefficiency to energy of sensor node as fixed report threshold and verification node. In this paper, our propose the next neighbor node selection scheme based on fuzzy logic system for energy improvement of PVFS. The parameter of fuzzy logic system are energy, hops, verification success count, CH select high the next neighbor node among neighbor nodes of two as deduction based on fuzzy logic system. In the experimental, our proposed scheme was improvement to energy of about 9% compare to PVFS.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.421-428
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• 2012

Analog instrument and control systems in nuclear power plants have recently been replaced with digital systems for safer and more efficient operation. Digital instrument and control systems have adopted various fault-tolerant techniques that help the system correctly and safely perform the specific required functions regardless of the presence of faults. Each fault-tolerant technique has a different inspection period, from real-time monitoring to monthly testing. The range covered by each faulttolerant technique is also different. The digital instrument and control system, therefore, adopts multiple barriers consisting of various fault-tolerant techniques to increase the total fault detection coverage. Even though these fault-tolerant techniques are adopted to ensure and improve the safety of a system, their effects on the system safety have not yet been properly considered in most probabilistic safety analysis models. Therefore, it is necessary to develop an evaluation method that can describe these features of digital instrument and control systems. Several issues must be considered in the fault coverage estimation of a digital instrument and control system, and two of these are addressed in this work. The first is to quantify the fault coverage of each fault-tolerant technique implemented in the system, and the second is to exclude the duplicated effect of fault-tolerant techniques implemented simultaneously at each level of the system's hierarchy, as a fault occurring in a system might be detected by one or more fault-tolerant techniques. For this work, a fault injection experiment was used to obtain the exact relations between faults and multiple barriers of faulttolerant techniques. This experiment was applied to a bistable processor of a reactor protection system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.3
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• pp.267-275
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• 2012

In this study, crack growth in a center-cracked plate is predicted under mode I variable amplitude loading, and the result is validated by experiment. Huang's model is employed to describe crack growth with acceleration and retardation due to the variable loading effect. Experiment is conducted with Al6016-T6 plate, in which the load is applied, and crack length is measured periodically. Particle Filter algorithm, which is based on the Bayesian approach, is used to estimate model parameters from the experimental data, and predict the crack growth of the future in the probabilistic way. The prediction is validated by the run-to-failure results, from which it is observed that the method predicts well the unique behavior of crack retardation and the more data are used, the closer prediction we get to the actual run-to-failure data.