• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.163-174
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• 2010

As pointed out in the OECD BEMUSE Program, when a high computation time is taken to obtain the relevant output values of a complex physical model (or code), the number of statistical samples that must be evaluated through it is a critical factor for the sampling-based uncertainty analysis. Two alternative methods have been utilized to avoid the problem associated with the size of these statistical samples: one is based on Wilks' formula, which is based on simple random sampling, and the other is based on the conventional nonlinear regression approach. While both approaches provide a useful means for drawing conclusions on the resultant uncertainty with a limited number of code runs, there are also some unique corresponding limitations. For example, a conclusion based on the Wilks' formula can be highly affected by the sampled values themselves, while the conventional regression approach requires an a priori estimate on the functional forms of a regression model. The main objective of this paper is to assess the feasibility of the ACE-RSM approach as a complementary method to the Wilks' formula and the conventional regression-based uncertainty analysis. This feasibility was assessed through a practical application of the ACE-RSM approach to the LOFT L2-5 LBLOCA PCT uncertainty analysis, which was implemented as a part of the OECD BEMUSE Phase III program.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.279-285
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• 2007

The objective of the research is two fold. The first is to design and propose a stable and robust learning control algorithm. The controller is CMAC Learning Controller which consists of a model-based controller, such as LQR or PID, as a reference control and a CMAC. The second objective is to implement a reference control and CMAC at two different sampling rates. Generally, a conventional controller is designed based on a mathematical plant model. However, increasing complexity of the plant and accuracy requirement on mathematical models nearly prohibits the application of the conventional controller design approach. To avoid inherent complexity and unavoidable uncertainty in modeling, biology mimetic methods have been developed. One of such attempts is Cerebellar Model Articulation Computer(CMAC) developed by Albus. CMAC has two main disadvantages. The first disadvantage of CMAC is increasing memory requirement with increasing number of input variables and with increasing accuracy demand. The memory needs can be solved with cheap memories due to recent development of new memory technology. The second disadvantage is a demand for processing powers which could be an obstacle especially when CMAC should be implemented in real-time. To overcome the disadvantages of CMAC, we propose CMAC learning controller with multiple sampling rates. With this approach a conventional controller which is a reference to CMAC at high enough sampling rate but CMAC runs at the processor's unoccupied time. To show efficiency of the proposed method, an inverted pendulum controller is designed and implemented. We also demonstrate it's possibility as an industrial control solution and robustness against a modeling uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.76.1-76
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• 2002

This paper addresses a method for nonlinear controller construction for a general nonlinear system with the separation of controller construction and manipulated values generation. The nonlinear system model is firstly expressed with the coefficients of state-depended representation. The nonlinear control is designed without any approximation based on the model with state-depended representation. At the stage of controller implementation for the nonlinear system, the manipulated values are calculated accurately by use of an algorithm of the numerical analysis. The numerical error for calculating the manipulated value can be reduced to zero by selecting the sampling interval being a small val...

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.35-42
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• 1995

One of the main objectives of this study is to propose a realistic limit state model for reliability-based safety assessment of precast segmental prestressed concrete box girder bridges, considering 1) combined effects of bending, shear and torsional forces, and 2) the difference between transverse reinforcments of box girder. A improved limit state model is derived from a modified interaction equation compared with the Bruno's equation. A Drectional sampling algorithm is used for reliability analysis of the proposed model.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.12
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• pp.716-722
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• 2005

In this paper, we propose the audio watermarking algorithm based on the critical band of HAS(human auditory system) without audibly affecting the quality of the watermarked audio and implement the detecting algorithm on the BSS(broadcast synchronizing system) for testing the proposed algorithm. According to the audio quality test, the SNR(signal to noise ratio) of the watermarked audio objectively is 66dB above. In the robustness test, the proposed algorithm can detect the watermark more than $90\%$ from various compression(MP3, AAC), A/D and D/A conversions, sampling rate conversions and especially asynchronizing attacks. The BSS automatically switches the programs between the key station and the local station in broadcasting system. The result of reliability test of implemented system by using the real broadcasting audio has no false positive error during 30 days. Because of detecting once processing per 0.5 second, we can judge that the false positive error does not occur.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1114-1116
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• 1999

This paper presents a new algorithm which evaluates production cost and reliability indices under various constraints of the thermal generation system. In order to consider the operational constraints of thermal units effectively, the proposed algorithm is based on Monte Carlo techniques instead of analytical ones which have difficulty in modelling the units with additional constraints. At that point, generating units are modelled into two types, base load units and peaking units. These generating unit models are used in state duration sampling simulation for which approach can readily consider the peaking unit operating cycles and easily calculates frequency-duration indices. The proposed production simulation algorithm is applied to the IEEE Reliability Test System, and performs the production simulation under the given constraints. The results show that the proposed algorithm is accurate, reliable and useful.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.131-136
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• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.194-202
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• 2008

This paper describes a new method for indoor environment mapping and localization with stereo camera. For environmental modeling, we directly use the depth and color information in image pixels as visual features. Furthermore, only the depth and color information at horizontal centerline in image is used, where optical axis passes through. The usefulness of this method is that we can easily build a measure between modeling and sensing data only on the horizontal centerline. That is because vertical working volume between model and sensing data can be changed according to robot motion. Therefore, we can build a map about indoor environment as compact and efficient representation. Also, based on such nodes and sensing data, we suggest a method for estimating mobile robot positioning with random sampling stochastic algorithm. With basic real experiments, we show that the proposed method can be an effective visual navigation algorithm.

• Journal of information and communication convergence engineering
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• v.2 no.4
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• pp.205-208
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• 2004

In this paper, we analyzed the performance of SDR-based dual-band CDMA system in wideband multipath channel employing RAKE receiver with MRC diversity. For the simulation of SDR-based dual-band CDMA system, we used digital IF techniques, polyphase analysis filter bank as channelizer. Also, Remez exchange algorithm is employed in the realization of the digital filter in the polyphase analysis filter bank. For performance analysis of dual-band CDMA system, we consider power loss of IF signal after passing through channel and the filter. Also, the performance for different sampling rates is analyzed.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.51-60
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• 2004

K-means clustering is an iterative algorithm in which items are moved among sets of clusters until the desired set is reached. K-means clustering has been widely used in many applications, such as market research, pattern analysis or recognition, image processing, etc. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using a center of gravity for grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.