• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1749-1758
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• 1997

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal's GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps %3A SOFM and Learning Vector Quantization %3A LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal's GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. $4^{th},\;Jun.\;4^{th},\;Jul.\;4^{th},\;Sep.\;4^{th},\;Nov.\;4^{th},$ 1995, after having trained the data for the days from Mar. $1^{th}\;to\;3^{th},\;from\;Jun.\;1^{th}\;to\;3^{th},\;from\;Jul.\;1^{th}\;to\;3^{th},\;from\;Sep.\;1^{th}\;to\;3^{th},\;and\;from\;Nov.\;1^{th}\;to\;3^{th},$ 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.

• Journal of the Korean Physical Society
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• v.73 no.12
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• pp.1821-1826
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• 2018

Uniformity is a key feature of state-of-the-art infrared focal planed array (IRFPA) and infrared imaging system. Unlike traditional infrared telescope facility, a ground-based infrared radiant characteristics measurement system with an IRFPA not only provides a series of high signal-to-noise ratio (SNR) infrared image but also ensures the validity of radiant measurement data. Normally, a long integration time tends to produce a high SNR infrared image for infrared radiant characteristics radiometry system. In view of the variability of and uncertainty in the measured target's energy, the operation of switching the integration time and attenuators usually guarantees the guality of the infrared radiation measurement data obtainted during the infrared radiant characteristics radiometry process. Non-uniformity correction (NUC) coefficients in a given integration time are often applied to a specified integration time. If the integration time is switched, the SNR for the infrared imaging will degenerate rapidly. Considering the effect of the SNR for the infrared image and the infrared radiant characteristics radiometry above, we propose a-wide-dynamic-range NUC algorithm. In addition, this essasy derives and establishes the mathematical modal of the algorithm in detail. Then, we conduct verification experiments by using a ground-based MWIR(Mid-wave Infared) radiant characteristics radiometry system with an Ø400 mm aperture. The experimental results obtained using the proposed algorithm and the traditional algorithm for different integration time are compared. The statistical data shows that the average non-uniformity for the proposed algorithm decreased from 0.77% to 0.21% at 2.5 ms and from 1.33% to 0.26% at 5.5 ms. The testing results demonstrate that the usage of suggested algorithm can improve infrared imaging quality and radiation measurement accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1986-1989
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• 2003

This paper proposes the information retrieval improvement for the Web using the structure and hyperlinks of HTML documents along with user profile. The method bases on the rationale that terms appearing in different structure of documents may have different significance in identifying the documents. The method partitions the occurrence of terms in a document collection into six classes according to the tags in which particular terms occurred (such as Title, H1-H6 and Anchor). We use genetic algorithm to determine class importance values and expand user query. We also use this value in similarity computation and update user profile. Then a genetic algorithm is used again to select some terms from user profile to expand the original query. Lastly, the search engine uses the expanded query for searching and the results of the search engine are scored by similarity values between each result and the user profile. Vector space model is used and the weighting schemes of traditional information retrieval were extended to include class importance values. The tested results show that precision is up to 81.5%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.504-507
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• 2005

In this paper, we propose a calibration technique of 3D coordinates using orthogonal stereo vision. First, we acquire front- image and upper- image from stereo cameras with real time and extract each coordinates of a moving object using differential operation and ART2 clustering algorithm. Then, we can generate 3D coordinates of that moving object through combining these two coordinates. Finally, we calibrate 3D coordinates using orthogonal stereo vision since 3D coordinates are not accurate due to perspective. Experimental results show that accurate 3D coordinates of a moving object can be generated by the proposed calibration technique.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2771-2782
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• 2022

When assessing the radiological consequences of postulated accident scenarios, it is of primary interest to determine the amount of radioactive fission gas accumulated in the fuel rod free volume. The state-of-the-art semi-empirical approach (ANS 5.4-2010) is reviewed and compared with a mechanistic approach to evaluate the release of radioactive fission gases. At the intra-granular level, the diffusion-decay equation is handled by a spectral diffusion algorithm. At the inter-granular level, a mechanistic description of the grain boundary is considered: bubble growth and coalescence are treated as interrelated phenomena, resulting in the grain-boundary venting as the onset for the release from the fuel pellets. The outcome is a kinetic description of the release of radioactive fission gases, of interest when assessing normal and off-normal conditions. We implement the model in SCIANTIX and reproduce the release of short-lived fission gases, during the CONTACT 1 experiments. The results show a satisfactory agreement with the measurement and with the state-of-the-art methodology, demonstrating the model soundness. A second work will follow, providing integral fuel rod analysis by coupling the code SCIANTIX with the thermo-mechanical code TRANSURANUS.

• Advances in Computational Design
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• v.1 no.4
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• pp.315-327
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• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• Journal of Electrical Engineering and Technology
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• v.8 no.1
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• pp.80-89
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• 2013

This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using a 'Mean-Variance Optimization (MVO)' algorithm with Kuhn-Tucker condition and swap process. The aim of the ED problem, one of the most important activities in power system operation and planning, is to determine the optimal combination of power outputs of all generating units so as to meet the required load demand at minimum operating cost while satisfying system equality and inequality constraints. This paper applies Kuhn-Tucker condition and swap process to a MVO algorithm to improve a global minimum searching capability. The proposed MVO is applied to three different nonconvex ED problems with valve-point effects, prohibited operating zones, transmission network losses, and multi-fuels with valve-point effects. Additionally, it is applied to the large-scale power system of Korea. The results are compared with those of the state-of-the-art methods as well.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.9
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• pp.1090-1097
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• 2007

The multibeam surveillance radar is a state-of art of 3D radar technology. It applies the stacked beam-on-received realized by a digital beamformer. In this paper, a design concept of beamformer and a method of target altitude extraction for multibeam surveillance radar in the normal environmental condition considering no multipath situations are proposed and investigated. The extraction algorithm based on antenna sine space coordinated system in a FFT digital beamformer is described. The proposed algorithm is simulated by 1 look-up table data and confirmed to have consistent results in accordance with a variety of target altitudes and a full radar frequency range.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.813-821
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• 2022

Intelligent monitoring, life entertainment, medical rehabilitation, and other fields are only a few examples where visual image technology is becoming increasingly sophisticated and playing a significant role. Recognizing Wushu, or martial arts, movements through the use of visual image technology helps promote and develop Wushu. In order to segment and extract the signals of Wushu movements, this study analyzes the denoising of the original data using the wavelet transform and provides a sliding window data segmentation technique. Wushu movement The Wushu movement recognition model is built based on the hidden Markov model (HMM). The HMM model is trained and taught with the help of the Baum-Welch algorithm, which is then enhanced using the frequency weighted training approach and the mean training method. To identify the dynamic Wushu movement, the Viterbi algorithm is used to determine the probability of the optimal state sequence for each Wushu movement model. In light of the foregoing, an HMM-based martial arts movements recognition model is developed. The recognition accuracy of the HMM model increases to 99.60% when the number of samples is 4,000, which is greater than the accuracy of the SVM (by 0.94%), the CNN (by 1.12%), and the BP (by 1.14%). From what has been discussed, it appears that the suggested system for detecting martial arts acts is trustworthy and effective, and that it may contribute to the growth of martial arts.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.1-9
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• 2003

The CNN-UM algorithm which performs the analog parallel subtraction of images has been developed and its application study to the moving target detection has been done. The CNN-UM is the state of the art computation architecture with high computational potential of analog parallel processing. It is one of the strong candidates for the next generation of computing system which fulfills requirement of the real-time image processing. One weakness of the CNN-UM is that its analog parallel processing function is not fully utilized for the inter frame processing. If two subsequent image frames are superimposed with opposite signs on identical capacitors for short time period, the analog subtraction between them is achieved. The Principle of such temporal inter-frame processing algorithm has been described and its mathematical analysis has been done. Practical usefulness of the proposed algorithm has also been verified through the application for moving target detection.