• Journal of Information Processing Systems
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• 제7권4호
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• Transactions on Electrical and Electronic Materials
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• 제14권2호
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• pp.63-66
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• 2013

This paper describes the highly productive process technologies of microprobe arrays, which were used for a probe card to test a Dynamic Random Access Memory (DRAM) chip with fine pitch pads. Cantilever-type microprobe arrays were fabricated using conventional micro-electro-mechanical system (MEMS) process technologies. Bonding material, gold-tin (Au-Sn) paste, was used to bond the Ni-Co alloy microprobes to the ceramic space transformer. The electrical and mechanical characteristics of a probe card with fabricated microprobes were measured by a conventional probe card tester. A probe card assembled with the fabricated microprobes showed good x-y alignment and planarity errors within ${\pm}5{\mu}m$ and ${\pm}10{\mu}m$, respectively. In addition, the average leakage current and contact resistance were approximately 1.04 nA and 0.054 ohm, respectively. The proposed highly productive microprobes can be applied to a MEMS probe card, to test a DRAM chip with fine pitch pads.

• 한국멀티미디어학회논문지
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• 제5권1호
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• pp.38-46
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• 2002

본 논문은 디스플레이상에서 동영상 화질 향상을 위한 적응형 콘트라스트 조절장치를 설계하고 이를 구현하였다. 제안한 방식은 입력되는 영상 신호의 중간 값을 이용함으로써 화면의 중간 자기 에 따라 적응형으로 콘트라스트를 향상시키는 기법이다. 또한 프레임 메모리를 사용하는 대신에 입력 화소들을 실시간으로 처리함으로써 기존의 방식에 비해 하드웨어 구성이 간단하여 실시간 처리를 요하는 분야에 쉽게 적용 가능하다. 기존 방식들이 정지영상을 기준으로 콘트라스트를 향상시킨 것에 반해 본 논문에서 제안한 방식은 정지영상 뿐만 아니라 동화상에서도 효과적으로 콘트라스트 향상이 가능하다. 제안한 알고리즘은 VHDL을 이용하여 설계하고, FPGA를 통하여 구현하였다. 인터페이스 시스템을 제작하여 테스트한 결과, 콘트라스트가 효과적으로 향상되었음을 확인하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• 품질경영학회지
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• 제49권2호
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.37-37
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• 2023

There is currently a high-accuracy modern forecasting method that uses machine learning algorithms or artificial neural network models to forecast river water levels or flowrate. As a result, this study aims to develop a mathematical model based on artificial neural networks to effectively forecast river water levels upstream of Tranh Culvert in North Vietnam's Bac Hung Hai irrigation system. The mathematical model was thoroughly studied and evaluated by using hydrological data from six gauge stations over a period of twenty-two years between 2000 and 2022. Furthermore, the results of the developed model were also compared to those of the long-short-term memory neural networks model. This study performs four predictions, with a forecast time ranging from 6 to 24 hours and a time step of 6 hours. To validate and test the model's performance, the Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error, and root mean squared error were calculated. During the testing phase, the NSE of the model varies from 0.981 to 0.879, corresponding to forecast cases from one to four time steps ahead. The forecast results from the model are very reasonable, indicating that the model performed excellently. Therefore, the proposed model can be used to forecast water levels in North Vietnam's irrigation system or rivers impacted by tides.

• 대한임상독성학회지
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• 제2권1호
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• pp.31-36
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• 2004

Three workers, field operators in lubricating oil processing of petroleum refinery industry were found unconscious by other worker. One of them who were exposed to an high concentration of H2S was presented with Glasgow Coma Score of 5, severe hypoxemia on arterial blood gas analysis, normal chest radiography, and normal blood pressure. On hospital day 7, his mental state became clear, and neurologic examination showed quadriparesis, profound spasticity, increased tendon reflexes, abnormal Babinski response, and bradykinesia. He was also found to have decreased memory, attention deficits and blunted affect which suggest general cognitive dysfunction, which improved soon. MRI scan showed abnormal signals in both basal ganglia and motor cortex, compatible with clinical findings of motor dysfunction. Neuropsychologic testing showed deficits of cognitive functions. SPECT showed markedly decreased cortical perfusion in frontotemporoparietal area with deep white matter. Another case was recovered completely, but the other expired the next day.

• 한국농공학회논문집
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• 제66권4호
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• pp.51-57
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• 2024

This study was conducted on how to simulate runoff, which was done using existing physical models, using an LSTM (Long Short-Term Memory) model based on deep learning. Tancheon, the first tributary of the Han River, was selected as the target area for the model application. To apply the model, one water level observatory and four rainfall observatories were selected, and hourly data from 2020 to 2023 were collected to apply the model. River water level of the outlet of the Tancheon basin was simulated by inputting precipitation data from four rainfall observation stations in the basin and average preceding 72-hour precipitation data for each hour. As a result of water level simulation using 2021 to 2023 data for learning and testing with 2020 data, it was confirmed that reliable simulation results were produced through appropriate learning steps, reaching a certain mean absolute error in a short period time. Despite the short data period, it was found that the mean absolute percentage error was 0.5544~0.6226%, showing an accuracy of over 99.4%. As a result of comparing the simulated and observed values of the rapidly changing river water level during a specific heavy rain period, the coefficient of determination was found to be 0.9754 and 0.9884. It was determined that the performance of LSTM, which aims to simulate river water levels, could be improved by including preceding precipitation in the input data and using precipitation data from various rainfall observation stations within the basin.

• 생물정신의학
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• 제14권1호
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• pp.61-67
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• 2007

Objectives : The purpose of this study was to test stage model in Traumatic Brain Injury(TBI) patients. According to the stage model, attention deficits which is basic stage in information processing lead to memory disturbance and subsequently affect higher-order cognitive function such as memory, decision-making, abstract thinking, and judgement related to executive function. Therefore, it was hypothesized that attention affect recall(retrieval efficacy) related to executive function mostly relative to other cognitive function, in TBI patients with low executive function. Methods : Participants were referred to a TBI clinic and then was rated on K-WAIS and Executive Intelligence Test(EXIT). Participants were divided into two groups according to Executive IQ(EIQ) score, which of high function group(N=67) was more than 80(above low average) and of low function group(N=52) was under 80 (under borderline). To test the stage model, using hierarchical regression analysis, recall(retrieval efficacy) was regressed on 3 subscales(attention, verbal, visuospatial scale) after controlling for IQ according to each group. Furthermore, the mediation effect of attention between retrieval efficacy and verbal, visuospatial score was analyzed. Results : In the low function group, only attention area predicted significantly recall(retrieval efficacy), indicating that lower attention were related to lower EIQ after controlling for IQ. In the high function group, no area predicted significantly retrieval efficacy. In the low function group, verbal and visuospatial scale did not predicted significantly retrieval efficacy, indicating that there was no evidences supporting the mediation model. Conclusion : Only attention affect retrieval efficacy in TBI patients with low executive function. But, the mediation effect of attention between retrieval efficacy and verbal and visuospatial scale was not tested in the low function group. These results implied that stage model was tested partially. In treating cognitive deficit in TBI patients, it is necessary to develop cognitive rehabilitation program based on stage model. Furthermore, it is necessary to necessary to test mediation model in the future study.

• 대한약침학회지
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• 제22권3호
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• pp.166-170
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• 2019

Objectives: Attentional and memory functions are important aspects of neural plasticity that, theoretically, should be amenable to pharmacopuncture treatments. A previous study from our laboratory suggested that quantitative electroencephalographic (qEEG) measurements of theta/beta ratio (TBR), an index of attentional control, may be indicative of academic performance in a first-semester medical school course. The present study expands our prior report by extracting and analyzing data on frontal theta and beta asymmetries. We test the hypothesis that the amount of frontal theta and beta asymmetries (fTA, fBA), are correlated with TBR and academic performance, thereby providing novel targets for pharmacopuncture treatments to improve cognitive performance. Methods: Ten healthy male volunteers were subjected to 5-10 min of qEEG measurements under eyes-closed conditions. The qEEG measurements were performed 3 days before each of first two block examinations in anatomy-physiology, separated by five weeks. Amplitudes of the theta and beta waveforms, expressed in ${\mu}V$, were used to compute TBR, fTA and fBA. Significance of changes in theta and beta EEG wave amplitude was assessed by ANOVA with post-hoc t-testing. Correlations between TBR, fTA, fBA and the raw examination scores were evaluated by Pearson's product-moment coefficients and linear regression analysis. Results: fTA and fBA were found to be negatively correlated with TBR (P<0.03, P<0.05, respectively) and were positively correlated with the second examination score (P<0.03, P=0.1, respectively). Conclusion: Smaller fTA and fBA were associated with lower academic performance in the second of two first-semester medical school anatomy-physiology block examination. Future studies should determine whether these qEEG metrics are useful for monitoring changes associated with the brain's cognitive adaptations to academic challenges, for predicting academic performance and for targeting phamacopuncture treatments to improve cognitive performance.