• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.1
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• pp.52-55
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• 2002

Flow cytometric techniques were used to investigate cell size, protein content and cell cycle behavior of recombinant Saccharomyces cerevisiae strains producing human lysozyme (HLZ). Two different signal sequences, the native yeast $MF\alpha1$ signal sequence and the rat $\alpha-amylase$ signal sequence, were used for secretion of HLZ. The strain containing the rat $\alpha-amylase$ signal sequence showed a higher level of internal lysozyme and lower specific growth rates. Flow cytometric analysis of the total protein content and cell size showed the strain harboring the native yeast signal sequence had a higher total protein content than the strain containing the rat $\alpha-amylase$ signal sequence. Cell cycle analysis indicated that the two lysozyme producing recombinant strains had an increased number of cells in the $G_2+M$ phase of the yeast cell cycle compared with the host strain SEY2102.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.11
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• pp.1204-1212
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• 2008

The paper proposes a new signal model which is revised from the commonly used signal model. Recently, many research institutions had a research about CW bio-radar for detecting he heartbeat and respiration. However, when the bio-radar detects the heartbeat using the previous signal model, the bio-radar has a disadvantage of weakness about he residual phase and AWGN. Also, the model is inappropriate in ergonomics because this signal model supposes hat the heart and lung are located at a same place. In this paper, the modified signal model, which is appropriate n ergonomics, is proposed. This paper analyzes and compares with the performance for detecting the heartbeat and respiration using the previous model and revised model in AWGN and multi-path environment.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.146-154
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• 2020

Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.895-896
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• 2008

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.267-268
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• 2002

We developed nausea, caused by disorder of autonomic nervous system, detection system using bio-signal analysis and artificial neural network in virtual reality enironment. We used 16 bio-signals, 9 EEGs, EOG, ECG, SKT, PPG, GSR, RSP, EGC, which has own analysis methods. We estimated nausea level by artificial neural network.

• Journal of Institute of Convergence Technology
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• v.6 no.1
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• pp.7-11
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• 2016

An experiment was performed to show the bio-signal characteristics of the Stuckyi when was heated. To measure the bio-signal of the plant, this study used the electrode with copper(+) and constantan(-). The electrode was directly inserted into the Stuckyi stems. And used T-type thermocouple to measure the inside temperature of the Stuckyi. During the experiments, room temperature also measured with T-type thermocouple. Heating was made with hair dryer periodically that 3 times turned on for heating 5 min and off for non-heating 25 min. Under the situation, the responses of the Stuckyi including voltage potential and inside temperature were measured using data logger(HP-34970A) according to the distances(15cm, 30cm, 45cm) between hair dryer and the plant. Through the experiments, We knew that the inside temperature has similar level and behavior with the room temperature at normal state. And during heating period, the inside temperature and the potential were also simultaneously increased sharply. From the experiments, we revealed that the measuring system of bio-signal of the plant being proposed in this study can be useful to show the Characteristics of the plants.

• Progress in Superconductivity
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• v.8 no.1
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• pp.33-39
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• 2006

We have developed a second-order double relaxation oscillation SQUID(DROS) gradiometer with a baseline of 35 mm, and constructed a poorly magnetically-shielded room(MSR) with an aluminum layer and permalloy layers for magnetocardiography(MCG). The 2nd-order DROS gradiometer has a noise level of 20 $fT/{\surd}Hz$ at 1 Hz and 8 $fT/{\surd}Hz$ at 200 Hz inside the heavily-shielded MSR with a shielding factor of $10^3$ at 1 Hz and $10^4-10^5$ at 100 Hz. The poorly-shielded MSR, built of a 12-mm-thick aluminum layer and 4-6 permalloy layers of 0.35 mm thickness, is 2.4mx2.4mx2.4m in size, and has a shielding factor of 40 at 1 Hz, $10^4$ at 100 Hz. Our 64-channel second-order gradiometer MCG system consists of 64 2nd-order DROS gradiometers, flux-locked loop electronics, and analog signal processors. With the 2nd-order DROS gradiometers and flux-locked loop electronics installed inside the poorly-shielded MSR, and with the analog signal processor installed outside it, the noise level was measured to be 20 $fT/{\surd}Hz$ at 1 Hz and 8 $fT/{\surd}Hz$ at 200 Hz on the average even though the MSR door is open. This result leads to a low noise level, low enough to obtain a human MCG at the same level as that measured in the heavily-shielded MSR. However, filters or active shielding is needed fur clear MCG when there is large low-frequency noise from heavy air conditioning or large ac power consumption near the poorly-shielded MSR.

• Progress in Superconductivity
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• v.8 no.2
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• pp.164-168
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• 2007

We developed a SQUID magnetometer based on Double Relaxation Oscillation SQUID(DROS) for measuring magnetocardiography(MCG). Since DROS provides a 10 times larger flux-to-voltage transfer coefficient than the conventional DC-SQUID, simple flux-locked loop electronics could be used for SQUID operation. Especially, we adopted an external feedback to eliminate the magnetic coupling with adjacent channels. When the DROS magnetometer was operated inside a magnetically shielded room, average magnetic field noise was about 5 $fT/^{\surd}Hz$ at 100 Hz. Using the DROS magnetometer, we constructed a multichannel MCG system. The system consisted of 61 magnetometers are arranged in a hexagonal structure and measures a vertical magnetic-field component to the chest surface. The distance between adjacent channels is 26 mm and the magnetometers cover a circular area with a diameter of 208 mm. We recorded the MCG signals with this system and confirmed the magnetic field distribution and the myocardinal current distribution.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.65-67
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• 2017

Due to the development of mobile computing and wearable technology, various wearable devices for measuring bio-signals in everyday life have been developed and popularized, and healthcare services utilizing bio-signals are attracting attention. In recent years, healthcare services have been developed and studied using various bio-signal analysis tools. Most bio-signal analysis studies utilize Matlab. However, in order to apply the algorithm developed in Matlab to the system, it is necessary to convert the source. We want to provide a smart interface that can skip source conversion. In this paper, we develop an interface to run the source file itself in the system by omitting the conversion technique for applying the algorithm developed in Matlab to the system.

• Journal of the Ergonomics Society of Korea
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• v.18 no.1
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• pp.121-131
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• 1999

There have been many questionnaires, catecholeamins analysis and bio-signal analysis to analyze human stress condition through out the years, and especially researches in bio-signal analysis have been actively increasing. The purpose of our research is Quantitative analysis of stress with synthesis of bio-signals. The stress status was estimated using the bio-signals and fuzzy theory which combines these signals and physiological knowledge. Stress was estimated by a 'coin-stacking' experiment with two type-relax and stress status. To do the experiment EMG, respiration, periphery temperature, heart rate and skin conductances were used to evaluate human stress stages. The system was tested to 10 healthy persons and achieved a template of a stress progress and stress variations were classified to 4 steps by continuous or rising status of stress progress.