• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1481-1490
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• 2019

Arduino makes it easy to connect objects and computers. As a result, programming learning using physical computing has been proposed as an effective alternative to SW training for beginners. In this paper, we propose an Arduino-based physical computing education method that can be applied to basic programming subjects. To this end, we propose a basic programming training method based on Arduino analog signals. Currently, physical computing courses focus on digital control when connecting input sensors and output devices in Arduino. However, the contents of programming education using analog signals of Arduino boards are insufficient. In this paper, we proposed and tested the teaching method used for programming education using low-cost materials used for Arduino analog signal-based computing.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1202-1209
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• 2011

Neuro feedback training using ElectroEncephalo Grams (EEGs) is commonly utilized in the treatment of Alzheimer's disease, and Attention Deficit Hyperactivity Disorder (ADHD). Recently, BCI (Brain-computer Interface) contents have developed, not for the purpose of treatment, but for concentration improvement or brain relaxation training. However, as each user has different wave forms, it is hard to develop contents controlled by such different wave. Therefore, an EEG process that allows the ability to transform the variety of wave forms into one standard signal and use it without taking a user's characteristic of EEG into account, is required. In this paper, a framework that can reduce users' characteristics by normalizing and converting measured EEGs is proposed for contents. This framework also contains the process that controls different brainwave measuring devices. In experiment a handling process applying the proposed framework to the developed BCI contents is introduced.

• JSTS:Journal of Semiconductor Technology and Science
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• v.6 no.3
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• pp.182-188
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• 2006

An artificial neural network model for the microwave characteristics of an InGaAs/InP hemt for 70 nm gate length has been developed. The small-signal microwave parameters have been evaluated to determine the transconductance and drain-conductance. We have further investigated the frequency characteristics of the device. The neural network training have been done using the three layer architecture using Levenberg-Marqaurdt Backpropagation algorithm. The results have been compared with the experimental data, which shows a close agreement and the validity of our proposed model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.165-169
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• 2007

Due to the complexity of underwater acoustic channel, signal estimation in underwater acoustic communication field is considerably affected from time-varying multipath fading channels. On this reason, the original signals should have many long training signals to estimate the channel and the purposed signals, and the bit rate of signals having information may have small rate. In order to avoid this loss of efficiency in underwater communication, this paper employed a blind channel identification method which don't use training signals. Simulations have predicted performance of the employed method in multipath environment and an aquatic plant experiment has verified the simulation results.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.47-50
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• 1990

Deafs cannot speak straight voice as normal people in lack of feedback of their pronunciation, therefore speech training is required. In this study, fundamental frequency, intensity, formant frequencies, vocal tract graphic and vocal tract area function, extracted from speech signal, are used as feature parameter. AR model, whose coefficients are extracted using inverse filtering. is used as speech generation model. In connect ion between vocal tract graphic and speech parameter, articulation distances and articulation distance functions in selected 15-intervals are determined by extracted vocal tract areas and formant frequencies.

• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.211-220
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• 2019

Surface electromyogram (sEMG), which is a bio-electrical signal originated from action potentials of nerves and muscle fibers activated by motor neurons, has been widely used for recognizing motion intention of robotic prosthesis for amputees because it enables a device to be operated intuitively by users without any artificial and additional work. In this paper, we propose a training-free unsupervised sEMG pattern recognition algorithm. It is useful for the gesture recognition for the amputees from whom we cannot achieve motion labels for the previous supervised pattern recognition algorithms. Using the proposed algorithm, we can classify the sEMG signals for gesture recognition and the calculated threshold probability value can be used as a sensitivity parameter for pattern registration. The proposed algorithm was verified by a case study of a patient with partial-hand amputation.

• Journal of the Korea Convergence Society
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• v.11 no.2
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• pp.17-22
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• 2020

In this paper, we measured the user's EEG signal and classified the EEG signal using the Support Vector Machine algorithm and measured the accuracy of the signal. An experiment was conducted to measure the user's EEG signals by separating men and women, and a single channel EEG device was used for EEG signal measurements. The results of measuring users' EEG signals using EEG devices were analyzed using R. In addition, data in the study was predicted using a 80:20 ratio between training data and test data by applying a combination of specific vectors with the highest classifying performance of the SVM, and thus the predicted accuracy of 93.2% of the recognition rate. This paper suggested that the user's EEG signal could be recognized at about 93.2 percent, and that it can be performed only by simple linear classification of the SVM algorithm, which can be used variously for biometrics using EEG signals.

• Nuclear Engineering and Technology
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• v.20 no.1
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• pp.47-53
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• 1988

The objective of this study is to evaluate the reliability of the High Head Safety Injection System (HHIS) of KNU 5, 6, 7 and 8 following the removal of safety injection signal (s-signal) from the mini-flow bypass line valves of charging/safety injection pumps. The unavailability of HHSIS and the rupture probability of a charging/safety injection pump have been computed for two different cases; with s-signal on and removed. The results show that when the s-signal is removed from the mini-flow bypass line valves, the unavailability of HHSIS slightly increases while the rupture probability of a charging/safety injection pump is significantly reduced. Hence, based upon the results of this study we conclude that it is more reasonable to remove the s-signal from the mini-flow bypass line valves of KNU 5, 6, 7 and 8 in the normal plant operation. And to improve the availability of HHSIS, the modification of operational procedures and the emphasis on operator training are recommended.

• Journal of Life Science
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• v.21 no.7
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• pp.985-991
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• 2011

The purpose of this study was to investigate the biological effect of obesity-induced oxidative damage on neurogenesis and early protein expression. Obesity was induced I thirty 4-week old male Sprague-Dawley rats through a high fat diet for 15 weeks. After one week of environmental adaptation, the rats were divided into 2 groups: high fat diet sedentary group (HDS, n=15) and high fat diet training group (HDT, n=15). Exercise training was performed 5 times a week for 8 weeks, with mild-intensity treadmill running for weeks 1-4 and moderate-intensity treadmill running for weeks 5-8. After the 8 week training period, we analyzed lipid profiles, serum 8-hydroxyguanosine (8-OHdG), liver tissue malondialdehyde (MDA) related to oxidative damage factors, nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), c-fos, c-jun, and extracellular signal regulated kinase (Erk) in the hippocampus. The results of this study are as follows. There were differences between HDS and HDT in triglyceride (TG) and total cholesterol (TC) (p<0.05). In high density lipoprotein (HDL-c), the HDT was higher than HDS after treadmill training (p<0.05). In 8-OHdG, the HDT was lower than HDS after treadmill training (p<0.05). Genetic expressions of c-jun, BDNF and MDA in the HDT were higher than in the HDS after treadmill training in hippocampus (p<0.05). Therefore, we conclude that 8 weeks of treadmill training can improve imbalanced lipid profiles, reduce oxidative damage, and activate neurogenesis in obese rats.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.205-208
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• 2005

본 논문은 특징 가중치 벡터를 적용하여 능동형태 모델(Active Shape Model)기반에서 눈동자의 움직임 추적 속도를 향상시키는 방법을 제안한다. 일반적인 능동형태 모델에서는 객체 추적을 위한 PDM 구성을 위해 각 특징점 구성 벡터의 유클리디안 거리의 최소 값으로 Training Set정렬 과정을 수행한다. 이 과정에서 적절하지 못한 샘플 영상으로 인해 안정된 PDM을 구성하지 못하는 문제점이 발생한다. 이러한 문제점을 해결하기 위하여 본 논문에 서는 형태를 구성하는 특징점마다 가중치를 부여하는 벡터를 작성하고, 최소자승근사법으로 최유사 특징점 벡터를 산출하기 위한 선형방정식을 구상하였다. 이로 인해 안정된 PDM을 구성할 수 있었으며, 눈동자 추적실험을 통해 형태적 움직임을 보정하는 실험을 수행하였다. 실험결과 기존의 능동형태 모델에 비해 반복연산의 횟수가 줄어들고, 다양한 형태로 나타나는 눈동자의 움직임 추적에 보다 안정적인 결과를 얻을 수 있었다.