• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1714-1725
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• 1993

This paper analyzes the properties of such algorithm that corresponds to the nonlinear adaptive algorithm with additional update terms, parameterized by the scalar factors a1, a2, a3 and Presents its structure. The analysis of convergence leads to eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The time constant is derived and the computational complexities of MLMS algorithms are compared with those of the conventional LMS, sign, LFG, and QFG algorithms. The properties of convergence in the mean square are analyzed and the expressions of the mean square recursion and the excess mean square error are derived. The necessary condition for the CFG algorithm to be stable is attained. In the computer simulation applied to the system identification the CFG algorithm has the more computation complexities but the faster convergence speed than LMS, LFG and QFG algorithms.

• Proceedings of the KIEE Conference
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• summer
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• pp.695-697
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• 2004

현재 전력시장에서 발생하는 게이밍을 반영하기 위한 수리적 모델로서 가장 보편적으로 사용되는 이론 중의 하나가 쿠르노 모델이다. 쿠르노 모델을 실제전력시장에 적용할 때 가장 어려운 점 중의 하나는 정화한 해당 모델에 사용되는 수요와 시장가격간의 관계를 정식화한 수요반웅함수(혹은 역수요함수)를 구하는 것이 다. 기존 모델의 경우 장기간에 걸친 탐문조사나 데이터를 바탕으로 가격탄력성을 구하는 방식을 취하고 있다. 그러나 수요는 전기설비의 교체 소비자의 기호 등 여러가지 변수로 지속적으로 변할 수 있기 때문에 이러한 고정적인 가격탄력성을 적용하는 것은 문제점이 될 수 있기 때문에 본 논문에서는 이러한 가격탄력성을 일정 거래주기 마다 갱신해줄 수 있는 방법을 제안하고자 한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.36-41
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• 2016

LDPC code has beend applied in recent communication standards, such as Wi-Fi, WiGig, 10GBased-T Ethernet as a forward error correction code. However, LDPC code is required a large amount of computational complexity due to large iterations and block lengths for high performances. To solve this problem, various research has been continously performed for reducing computational complexity. In this paper, we propose AFC algorithm to deactive the variable and check node for reduce the computational complexity.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.199-208
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• 2011

The objective of this study is to develop real-time river flow forecast model by linking continuous rainfall-runoff model with ensemble Kalman filter technique. Andong dam basin is selected as study area and the model performance is evaluated for two periods, 2006. 7.1~8.18 and 2007. 8.1~9.30. The model state variables for data assimilation are defined as soil water content, basin storage and channel storage. This model is designed so as to be updated the state variables using measured inflow data at Andong dam. The analysing result from the behavior of the state variables, predicted state variable as simulated discharge is updated 74% toward measured one. Under the condition of assuming that the forecasted rainfall is equal to the measured one, the model accuracy with and without data assimilation is analyzed. The model performance of the former is better than that of the latter as much as 49.6% and 33.1% for 1 h-lead time during the evaluation period, 2006 and 2007. The real-time river flow forecast model using rainfall-runoff model linking with data assimilation process can show better forecasting result than the existing methods using rainfall-runoff model only in view of the results so far achieved.

• The Journal of the Acoustical Society of Korea
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• v.21 no.5
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• pp.430-437
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• 2002

Having a variety of good characteristics against other pattern recognition techniques, the multilayer perceptron (MLP) has been widely used in speech recognition and speaker recognition. But, it is known that the error backpropagation (EBP) algorithm that MLP uses in learning has the defect that requires restricts long learning time, and it restricts severely the applications like speaker recognition and speaker adaptation requiring real time processing. Because the learning data for pattern recognition contain high redundancy, in order to increase the learning speed it is very effective to use the online-based learning methods, which update the weight vector of the MLP by the pattern. A typical online EBP algorithm applies the fixed learning rate for each update of the weight vector. Though a large amount of speedup with the online EBP can be obtained by choosing the appropriate fixed rate, firing the rate leads to the problem that the algorithm cannot respond effectively to different learning phases as the phases change and the number of patterns contributing to learning decreases. To solve this problem, this paper proposes a Changing rate and Omitting patterns in Instant Learning (COIL) method to apply the variable rate and the only patterns necessary to the learning phase when the phases come to change. In this paper, experimentations are conducted for speaker verification and speech recognition, and results are presented to verify the performance of the COIL.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.42-51
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• 2011

This study was conducted to analyze correlation of above- and below-ground biomass and to drive regression equation suitable for estimating standing tree biomass between natural and planted stands of Pinus densiflora for. erecta of one age-class in Gangwon province, Republic of Korea. Total 40 trees, 10 from the naturally regenerated and 10 from the planted stands in each of two studied sites, were uprooted to measure height, diameter at root color (DRC), and the dry weights of stem, branches, and needles. The length, weight, and volume of the main and horizontal roots were also measured. Most of the above-ground traits except height were highly correlated with most of the other above-ground traits and the below-ground traits except the length of roots (p < 0.05). Especially, the DRC, which is measured easily on the standing tree, was highly correlated with most of the traits in all studied stands (p < 0.01). Thus, the DRC would be the most desirable trait to estimate not only above-ground biomass but also below-ground biomass. However, height was not a good variable to estimate standing tree biomass of Pinus densiflora for. erecta of one age-class in Gangwon province because it was not correlated with most of other traits. Regression equations derived from the current study could be used effectively as a basic data for estimating above-ground and below-ground biomass using DRC.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.29-39
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• 2017

Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.147-152
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• 2005

In this paper, we proposed a method for compressing the static video test patterns in high quality to test the picture quality in DTV. In our method, we use the fact that the generated bits and average quantization value have almost identical distribution characteristics per each GOP and we propose a new target bit allocation method suitable for compressing the static test pattern while the target bit allocation method in MPEG-2 TM5 is suitable for the moving picture. The proposed target bit allocation method is to maintain the high quality video continuously by using the normalized complexities which are updated or maintained by means of picture qualities at each GOP. Experiment result showed that the test pattern stream encoded by MPEG-2 software with the proposed algorithm had a stable bit rate and good video quality during the decoding process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.1
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• pp.1-10
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• 2011

An optimization method is proposed for preform and billet shape designs in the forging process by using the Equivalent Static Loads (ESLs). The preform shape is an important factor in the forging process because the quality of the final forging is significantly influenced by it. The ESLSO is used to determine the shape of the preform. In the ESLSO, nonlinear dynamic loads are transformed to the ESLs and linear response optimization is performed using the ESLs. The design is updated in linear response optimization and nonlinear analysis is performed with the updated design. The examples in this paper show that optimization using the ESLs is useful and the design results are satisfactory. Consequently, the optimal preform and billet shapes which produce the desired final shape have been obtained. Nonlinear analysis and linear response optimization of the forging process are performed using the commercial software LS-DYNA and NASTRAN, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.1 s.2
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• pp.75-84
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• 2003

Current techniques for road-traffic monitoring rely on sensors which have limited capabilities, are costly and disruptive to install. The use of video cameras coupled with computer vision techniques offers an attractive alternative to current sensors. Video based traffic monitoring systems are now being considered key points of advanced traffic management systems. In this paper, we propose the new method which extract the traffic information using video camera. The proposed method uses an adaptive updating scheme for background in order to reduce the false alarm rate due to various noises in images. also, the proposed extraction method of traffic information calculates the traffic volume ratio of vehicles passing through predefined detection area, which is defined by the length of profile occupied by cars over that of overall detection area. Then the ratio is used to define 8 different states of traffic and to interpret the state of vehicle flows. The proposed method is verified by an experiment using CCTV traffic data from urban area.