• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.129-131
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• 2009

Hearing loss is one of the most common birth defects among infants. Most hearing-impaired children are not diagnosed until one to three years of age, which is too late to treat for normal speech and language development. If a hearing impairment is identified and treated in its early stage, child's speech and language skills could be comparable to his or her normal-hearing peers. In this study, we applied the 'Fsp' method to distinguish between normal and impaired hearing. We have developed a battery-operated portable A - ABR(automated auditory brain stem response) system that automatically detects hearing impairment for neonates or infants in a nursery room, as well as in a sound-proof room. We partially validated the accuracy of the system in five normal-hearing adults.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1377-1382
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• 1990

In Multi-Layer Perceptron (MLP) which realizes continuous mappings, the output errors is directly affected by the weight errors which may be caused by the limited precision of digital or analog hardware in implementations. So, it is important to study the sensitivity due to the perturbation of connection weights between neurons. In this paper, we derive a sensitivity function to the statistical weight perturbations in MLP with differentiable activation functions. This sensitivity function can be regarded as an ensemble average of deterministic sensitivity measures due to the perturbations of weights. Hence, this sensitivity function can be used as the criteria for selecting weights with the minimum sensitivity among possible sets of connection weights in MLP. For the verification of the validity of the proposed sensitivity function, computer simulations have been performed and through the simulations we find good agreement between the theoretical and simulation results.

• Genomics & Informatics
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• v.5 no.1
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• pp.24-29
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• 2007

Computational virtual screening has become an essential platform of drug discovery for the efficient identification of active candidates. Moleculardocking, a key technology of receptor-centric virtual screening, is commonly used to predict the binding affinities of chemical compounds on target receptors. Despite the advancement and extensive application of these methods, substantial improvement is still required to increase their accuracy and time-efficiency. Here, we evaluate several advanced structure-based virtual screening approaches for elucidating the rank-order activity of chemical libraries, and the quantitative structureactivity relationship (QSAR). Our results show that the ensemble-average free energy estimation, including implicit solvation energy terms, significantly improves the hit enrichment of the virtual screening. We also demonstrate that the assignment of quantum mechanical-polarized (QM-polarized) partial charges to docked ligands contributes to the reproduction of the crystal pose of ligands in the docking and scoring procedure.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.1027-1035
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• 1994

This paper aims to make an artificial arm control strategy. For this, we propose a new feature extraction method and design artificial neural network for the functional classification of myoelectric signal(MES). We first transform the two channel myoelectric signals (MES) for biceps and triceps into frequency domain using fast Fourier transform (FFT). And features were obtained by comparing the magnitudes of ensemble spectrum data and used as inputs to the three-layer neural network for the learning. By changing the number of units in hidden layer of neural network we observed the improvement of classification performance. To observe the effeciency of the proposed scheme we performed experiments for classification of six arm functions to the three subjects. And we obtained on average 94[%] the ratio of classification.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.11
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• pp.151-155
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• 1993

Evoked potentials(EP) measured with scalp electrodes are often described as a deterministic process corrupted by uncorrelated electrical activities occuring in the brain and These electrical activities(ongoing EEG) refer to noise in EP recording. The Conventional method to determine the EP waveform requires long recording time. Unfortunately most of algorithm developed are too complicated for implementation in real time. Thus, conner EP recording devices use Ensemble average for real time processing. In this paper introduce EP recording hardware for processing advanced algorithm in real tlne. This hardware is composed of DSP chip(TMS320c25) and microcomputer.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.98-105
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• 2021

As the average life expectancy is rising, the population is aging and the number of chronic diseases is increasing. This has increased the importance of healthy life and health management, and interest in mobile health services is on the rise thanks to the development of ICT(Information and communication technologies) and the smartphone use expansion. In order to meet these interests, many mobile services related to daily health are being launched in the market. Therefore, in this study, the characteristics of users who actually use mobile health services were analyzed and a predictive model applied with machine learning modeling was developed. As a result of the study, we developed a prediction model to which the decision tree and ensemble methods were applied. And it was found that the mobile health service users' continued use can be induced by providing features that require frequent visit, suggesting achievable activity missions, and guiding the sensor connection for user's activity measurement.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.11
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• pp.723-729
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• 2014

In this paper, we derive the average repair bandwidth and/or read cost for arbitrary repairable linear code ensembles. The repair bandwidth and read cost are the required amount of data and access number of nodes to restore a failed node, respectively. Here, the repairable linear code ensemble is given by such parameters as the number k of data symbols, the number m of parity symbols, and their degree distributions. We further assume that the code is systematic, and no other constraint is assumed, except possibly that the exact repair could be done by the parity check-sum relation with fully connected n=k+m storages. This enables one to apply the result of this paper directly to any randomly constructed codes with the above parameters, such as linear fountain codes. The final expression of the average repair read cost shows that it is highly dependent on the degree distribution of parity symbols, and also the values n and k.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.

• Proceedings of the Korea Water Resources Association Conference
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• 1998.05b
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• pp.3-3
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• 1998

In this pressentation it is argued that the heterogeneity of a hydrologic attribute which may seem to be nonstationary at one scale, may become stationary at a larger scale. The fundamental reason for transformation from nonstationarity to stationarity whith the increase in scale is the phenomenon of coarse-graining of the hydrologic processes with increasing scale. Due to the phenomenon of aliasing, a particular scale hydrologic process heterogeneity which is observed as a nonstationary process at that scale, may be observed as a stationary process at a higher(larger) scale whose size is bigger than the stationary extent of the lower scale heterogeneity. As one goes through a hierarchical sequence of larger and larger scales for observations, one would eliminate nonstationarities which emerge at some lower scales at the expense of losing information on the high frequency fluctuations of the lower scale heterogeneities which will no longer be observed at the larger sampling scales. We call this phenimenon as the "coarse-graining in hydrologic observations". In this presentation, it is also argued that by the coarse-graining of hydrologic processes due to the averaging and aliasing operations at increasing scales, the conservation laws corresponging to these scales may still be quite parsimonious, and need not be more complicated as the scales get larger. It is shown that shen a higher(larger) scale process is formed by averaging a lower(smaller) scale process in time or space, the high frequency components of the lower scale process will be eliminated by the averaging operation. Thereby, the resuliiting average hydrologic dynamics, free from the effects of the high frequency components of the lower scale process, can still be quite simple in form. This is demonstrated by means of some recent upscaling work on the solute teansport conservation equation for hetergeneous aquifers. By means of this solute transport example, it is also shown that for the ensemble average form of a hydrologic conservation equation to be equivalent to its volume-average form at any scale, the parameter functions of that conservation equation at the immediately lower scale must be ergodic.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.