• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.81-86
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• 2007

Spectral features of the seismic wave propagation from Odaesan Earthquake were evaluated based on the commonly treated random error between the observed data and the prediction values by the stochastic point-source ground-motion spectral model regarding the source, path and site effects. Radiation pattern of the error according to azimuth angle was found to be similar to the theoretical estimate. It was also observed that the spatial distribution of the errors was correlated with the geological map and the Q0 map which are indicatives of seismic boundaries.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.1-8
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• 2008

The belief propagation method that has been studied recently yields good performance in disparity extraction. The method in which a target function is modeled as an energy function based on Markov random field(MRF), solves the stereo matching problem by finding the disparity to minimize the energy function. MRF models provide robust and unified framework for vision problem such as stereo and image restoration. the belief propagation method produces quite correct results, but it has difficulty in real time implementation because of higher computational complexity than other stereo methods. To relieve this problem, in this paper, we propose a fast algorithm of the belief propagation method. Energy function consists of a data term and a smoothness tern. The data term usually corresponds to the difference in brightness between correspondences, and smoothness term indicates the continuity of adjacent pixels. Smoothness information is created from messages, which are assigned using four different message arrays for the pixel positions adjacent in four directions. The processing time for four message arrays dominates 80 percent of the whole program execution time. In the proposed method, we propose an algorithm that dramatically reduces the processing time require in message calculation, since the message.; are not produced in four arrays but in a single array. Tn the last step of disparity extraction process, the messages are called in the single integrated array and this algorithm requires 1/4 computational complexity of the conventional method. Our method is evaluated by comparing the disparity error rates of our method and the conventional method. Experimental results show that the proposed method remarkably reduces the execution time while it rarely increases disparity error.

• Journal of the Korean Society of Industry Convergence
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• v.8 no.2
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• pp.97-104
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• 2005

Usually, propagation attenuation of millimeter wave occurs by rainfall, snowfall, temperature, effect of pressure of air. In 60GHz wave band wireless communication network, temperature change becomes big factor of propagation loss department. Also, temperature change causes disturbance of 60GHz frequency at transceiver. In this study, we used 60GHz transceiver and found propagation loss of wireless path and operating frequency disturbance characteristics. In transceiver that there is no temperature compensated device, operating frequency of TX changed by 60.865GHz at temperature of $-5^{\circ}C$, and appeared by 60.730GHz when is $50^{\circ}C$. Therefore, operating frequency change width by temperature change are about 100MHz, greatly. But, in transceiver that there is temperature compensated device, operating frequency of TX changed by 60.830GHz at temperature of $-5^{\circ}C$, and appeared by 60.710GHz when is $50^{\circ}C$. Therefore, operating frequency change width by temperature change are about 20MHz. According to these result, we constructed between buildings examination wireless site for point to point wireless communication using 60GHz band transceivers who have do temperature compensated device, and investigated data transmission characteristics about ambient temperature change. Therefore, if use transceiver that have temperature compensated device, may overcome the wireless transmission error in 60GHz band wireless communication LAN networks despite of ambient temperature change.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.798-800
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• 2003

The time shift processing of ground measured surface temperature with the meteorological variables has no evaluated function. We introduce new evaluating function. To use this evaluating function, the algorithm of time-shift processing will be able to be reliable and get error-bar for all moving measured point's data. We will finally obtain the area averaged surface temperature by land observation.

• Journal of IKEEE
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• v.9 no.2 s.17
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• pp.170-178
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• 2005

The video data corrupted by the transmission error due to packet loss induce error propagation in decoded video data, and cause poor video quality. To remedy these corrupted video data, there have been introduced two types of error concealment techniques: spatial or temporal error concealment algorithm. Computational overhead by using spatial error concealment algorithm is a serious disadvantage in mobile video data streaming environment. In this paper, we propose hybrid type error concealment technique recovering video quality of mobile device using MPEG-4 video streaming on error-prone wireless network. Our algorithm is implemented in MPEG-4 decoder. The algorithm adopts Intel Wireless MMX technology to provide high performance of portable embedded multimedia mobile device. It is proven that the proposed algorithm shows expected performance for a mobile streaming system(PDA) on IP channels. Our approach showed better processing speed and better video quality comparing with traditional error concealment algorithm.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.9
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• pp.1040-1047
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• 2011

This paper proposes a novel method based on error back propagation neural networks to fuse laser sensor data and ultrasonic sensor data for enhancing the accuracy of mapping. For navigation of single robot, the robot has to know its initial position and accurate environment information around it. However, due to the inherent properties of sensors, each sensor has its own advantages and drawbacks. In our system, the robot equipped with seven ultrasonic sensors and a laser sensor navigates to map two different corridor environments. The experimental results show the effectiveness of the heterogeneous sensor fusion using an error backpropagation algorithm for mapping.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.3
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• pp.231-238
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• 1992

Error vector propagation method is applied to the elliptic mild slope equation in order to reduce the computation time. Results from the elliptic, parabolic, and hyperbolic models are compared with experimental data for an elliptic shoal. Also, results of the elliptic and hyperbolic models are compared with experimental data for a detached breakwater. As a result of applying this model. it is concluded that the present model satisfactorily reduces the computation time compared with other numerical models. In the accuracy of solutions, there are some oscillations but the trend compares well with other models.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1342-1350
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• 1999

A new design procedure for micro cellular coverage prediction is presented here on this paper, which contains a new propagation analysis algorithm based on processing of vector data representing roads and buildings which mainly affect the propagation phenomena in micro-cell environments. The propagation analysis algorithm presented here has been developed to aim at the practical application for micro-cellular systems such as PCS or CE-2. As all the vectors used here are of closed poly lines, i.e., polygons, a simplified ray path search technique can be developed not only to determine if the calculation points are on the road polygons and but also to calculate the amount of blockage by buildings. The result shows a capability of predicting path loss with an RMS error of 5dB or lower.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.101-104
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• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.