• 대한의용생체공학회:의공학회지
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• 제10권3호
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• pp.343-350
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• 1989

A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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• pp.59-64
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• 2001

The objectives of the thesis are to propose a pattern classification method for remote sensing data using artificial neural network. First, we apply the error back propagation algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. Using the training data set and the error back propagation algorithm, a layered neural network is trained such that the training pattern are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of Landsat TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method.

• 한국농공학회지
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• 제18권1호
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• pp.4021-4037
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• 1976

This paper describes the actual experiments of the error propagation and studies of analytical photogrammetry using the simulation method in which we can find the causes of the errors. These studies and the results give the valuable data which are very effective for systematically controlling the errors in aerial triangulation. The main contents in my paper are as follows: 1. Dose the scale errors in the successive models in the form of normal distribution appear when the observation errors propagate in the form of normal distribution\ulcorner 2. In what form does this scale error propagation in the actual model appear\ulcorner 3. When the causes of the scale error propagation are found, is the evaluation standard determined normally\ulcorner 4. What degree of influence is there form the constant errors\ulcorner I have done several experiments using the simulation method technique to solve the complicated error propgation of aerial triangulation which is the effective means to research the relations between cause and effect. In this paper, the studies have concentrated on the following points of simulation experiments. (1) The first part descries how we can produce the soft program of the simulation experiment. (2) The second part is the method propagating the errors in the simulation models and the kinds of errors. (3) The final part is the most important; that is the analyzing and evaluation of control during actual work. From the above-mentioned points, it is said that these studies are a very important development in the field of controlling and managing aerial photogrammetry and especially in the case of error propagation, we can clearly find the causes of the errors and steps and parts of errors generated when we use these techniques.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.92-95
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• 2008

The Geostationary Ocean Color Imager (GOCI) is under development to provide a monitoring of ocean-color around the Korean Peninsula from geostationary platforms. It is planned to be loaded on Communication, Ocean, and Meteorological Satellite (COMS) of Korea. The GOCI has been designed to provide multi-spectral data to detect, monitor, quantify, and predict short term changes of coastal ocean environment for marine science research and application purpose. The target area of GOCI observation covers sea area around the Korean Peninsula. Based on the nonlinear radiometric model, the GOCI calibration method has been derived. The radiometric model of GOCI has been validated through radiometric ground test. From this ground test result, GOCI radiometric model has been changed from second order to third order. In this paper, the radiometric test performed to evaluate the radiometric nonlinearity is described and the GOCI radiometric error propagation is analyzed. The GOCI radiometric calibration is based on onboard calibration devices; solar diffuser, DAMD (Diffuser Aging Monitoring Device). The radiometric model error due to the dark current nonlinearity is considered as a systematic error. Also the offset correction error due to gain/offset instability is considered. The radiometric accuracy depends mainly on the ground characterization accuracies of solar diffuser and DAMD.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.187-190
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• 2007

The Geostationary Ocean Color Imager (GOCI) is under development to provide a monitoring of ocean-color around the Korean Peninsula from geostationary platforms. It is planned to be loaded on Communication, Ocean, and Meteorological Satellite (COMS) of Korea. The GOCI has been designed to provide multi-spectral data to detect, monitor, quantify, and predict short term changes of coastal ocean environment for marine science research and application purpose. The target area of GOCI observation covers sea area around the Korean Peninsula. Based on the nonlinear radiometric model, the GOCI calibration method has been derived. The nonlinear radiometric model for GOCI will be validated through ground test. The GOCI radiometric calibration is based on on-board calibration devices; solar diffuser, DAMD (Diffuser Aging Monitoring Device). In this paper, the GOCI radiometric error propagation is analyzed. The radiometric model error due to the dark current nonlinearity is analyzed as a systematic error. Also the offset correction error due to gain/offset instability is considered. The radiometric accuracy depends mainly on the ground characterization accuracies of solar diffuser and DAMD.

• 한국음향학회지
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• 제30권2호
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• pp.86-91
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• 2011

In the underwater communications, the acoustic channel is in poor communication conditions, such as long propagation delay, narrow bandwidth, and high bit-error rate. For these bad acoustic channels, we propose an efficient automatic repeat request (ARQ) for multi-hop underwater network by using the concepts of concurrent bi-directional transmission, multiple sub-packets, and overhearing data packet instead of the acknowledgement signal. Our results show that the proposed ARQ significantly reduces the transmission latency especially in high BER compared with the existing Stop and Wait ARQ.

• Journal of Advanced Marine Engineering and Technology
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• 제21권1호
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• pp.66-70
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• 1997

In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

• Journal of Communications and Networks
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• 제8권3호
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• pp.267-274
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• 2006

Self-spreading (SSP) is a spread spectrum technique where the spreading sequence is generated from data bits. Although SSP allows communications with low probability of interception by unintended receivers, despreading by the intended receiver is prone to error propagation. In this paper, we propose both a new transmitter and a new receiver based on SSP with the aim to a) reduce error propagation and b) increase the concealment of the transmission. We first describe a new technique for the generation of SSP spreading sequence, which generalizes SSPs of existing literature. We include also coding at the transmitter, in order to further reduce the effects of error propagation at the receiver. For the receiver, we propose a turbo architecture based on the exchange of information between a soft despreader and a soft-input soft-output decoder. We design the despreader in order to fully exploit the information provided by the decoder. Lastly, we propose a chip decoder that extracts the information on data bits contained in the spreading sequence from the received signal. The performance of the proposed scheme is evaluated and compared with existing spread-spectrum systems.

• International Journal of Contents
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• 제7권3호
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• pp.1-5
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• 2011

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.

• International Journal of Contents
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• 제12권1호
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• pp.49-53
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• 2016

"Hahoe Village" in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since "Hahoe Village" is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near "Hahoe Village". To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.