• Korean Journal of Fisheries and Aquatic Sciences
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• v.48 no.4
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• pp.489-497
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• 2015

To reveal the spatial and temporal variability in the distribution, growth, and maturation of skipjack tuna Katsuwonus pelamis in the western tropical Pacific, we compared two El $Ni{\tilde{n}}o$-Southern Oscillation (ENSO) signals and the sea surface temperature (SST) in the main fishing area with fishery and biological data. An index of skipjack tuna distribution was calculated using Korean purse seine fishery data from 1985 to 2003. Biological data for skipjack tuna were collected monthly from Korean catches during the 1994-2003 period. The catch was more closely related to the SST in the main fishing area than to the ENSO signals. However, cross-correlated function analysis showed delayed interactions between abiotic and biotic factors. The El $Ni{\tilde{n}}o$ events preceded the eastward movement of the fishing center by 2-3 months. El $Ni{\tilde{n}}o$ had a positive effect on the skipjack tuna catch, and the change in the Southern Oscillation Index (SOI) preceded the catch fluctuation by ca. 5-7 months. In addition, negative El $Ni{\tilde{n}}o$ effects on gonad maturation and the mean length of skipjack tuna were detected with time lags of 12 and 7 months, respectively. The length frequency indicated that the regime-specific growth pattern at each discrete period seemed to be related to the ENSO.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.129-135
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• 2004

In the home health management system, we often face the situation to handle biological signals that are frequently measured from normal subjects. In such a case, it is necessary to decide whether the signal at a certain moment is normal or abnormal. Since ECC is one of the most frequently measured biological signals, we describe algorithms that detect QRS-complex and decide whether it is normal or abnormal. The developed QRS detection algorithm is a simplified version of the conventional algorithm providing enough performance for the proposed application. The developed classification algorithm that detects abnormal from mostly normal beats is based on QRS width, R-R interval and QRS shape parameter using Karhunen-Loeve transformation. The simplified QRS detector correctly detected about 99% of all beats in the MTT/BIH ECG database. The classification algorithm correctly classified about 96% of beats as normal or abnormal. The QRS detection and classification algorithm described in this paper could be used in home health management system.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.191-198
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• 1999

Biofeedback training is one of physiological self control methods for patients who has psychological problem and rehabilitational problem. It has been used to control blood pressure, heart rate, peripheral temperature, respiration, electromyography (ENG), and other biological signals-ENG, respiration, heat rate, peripheral temperature, skin conductance level-was developed in house. We applied this system to alcohol dependent patients to perform biofeedback training. In this experiment, the relaxation biofeedback training for alcohol dependent patient was carried out and the tension state for the change of biological signals were estimated using the fuzzy theory after relaxation biofeenback training. Eight alcohol dependent patients were agreed to participate in this experiment. Result showed that 1) the tension degree of patients were higher than the tension degree of normal subject. 2) The tension degree of patients were decreased as the training numbers were increased.

• Korean Chemical Engineering Research
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• v.59 no.3
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• pp.366-372
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• 2021

Cadherin-Catenin complex is thought to play an essential role in the transmission of force at adherens junction. Due to the lack of proper tools to visualize and detect mechanical force signals, the underlying mechanism by which the cadherin-catenin complex regulates force transmission at intercellular junctions remains elusive. In this study, we visualize cadherin-mediated force transmission using an engineered α-Catenin sensor based on fluorescence resonance energy transfer. Our results reveal that α-catenin is a key force transducer in cadherin-mediated mechanotransduction at cell-cell junctions. Thus, our finding will provide important insights for studying the effects of chemical and physical signals on cell-cell communication and the relationship between physiological and pathological phenomena.

• Journal of the Korean society of biological therapies in psychiatry
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• v.24 no.3
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• pp.129-141
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• 2018

Suicide is a behavior that is intended to cause death by itself and requires medical treatment, resulting in suicidal attempt or completion. Suicide causes loss of life, damages the body, costs a lot of medical expenses, and causes families to fall into sorrow and suffering therefore this suicide is a huge loss to family and society. There have been attempts to reduce and prevent suicide by understanding the mechanism of suicide. The mechanism of suicide can be thought of as psychological mechanism and biological mechanism. In the past, if we considered the psychological and biological mechanisms separately, the development of neuroscience now connects and integrates these two. Psychological factors affect biological factors and biological temperaments also affect perception or thinking about the situation and increase psychological vulnerability. Distant factors in suicidal behavior-such as childhood adversity and family and genetic predisposition-increase the lifetime risk of suicide. They alter the response to stress and other processes through changes in gene expression and regulation of emotional and behavioral characteristics. Distant factors affect the biological system and consequently changes in these systems can increase the risk of suicide. In other words, the distal factor does not directly induce suicidal behavior but rather acts indirectly through developmental or mediating factors. These mediating factors are impulsive aggressive and anxious trait, and chronic use of substances. The mechanism of this disorder is the abnormality of the serotonin system and the abnormality of the lipid level. Proximal factors are associated with the onset of suicide events and include changes in the major neurotransmitter systems, inflammatory changes, and dysfunction of glial cells in the brain. A series of studies, including a variety of research methods and postmortem and in-vivo imaging studies, show the impairment of the serotonergic neurotransmitter system and hypothalamic-pituitary-adrenal axis stress response system for suicidal behavior. These disorders lead to suicidal behavior due to difficulty in cognitive control of mood, pessimism, reactive aggression, abnormality in problem solving abilities, excessive response to negative social signals, severe emotional distress, and cognitive dysregulation of suicidal ideation.

• Journal of Biomedical Engineering Research
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• v.23 no.5
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• pp.333-340
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• 2002

Powerline interference in bioelectric recordings is a common source of noise. IEF(Incremental Estimation Filter) has been used to eliminate powerline interferences in biosignals, especially in ECG(Electrocadiogram) signals. The constant incremental factor in the IEF filter, which affects the performance of noise rejection, is usually determined empirically or experimentally based on the input signals. This paper presents the design of neural network based IEF filter for time-varying control of the incremental factor. The proposed IEF filter is evaluated by applying to artificial signals as well as ECG signals of MIT-BIH database. For the relative comparison of noise-rejection performance, it is compared with adaptive noise canceler and conventional IEF filter. Simulation results show that the neural network based IEF filter outperforms these adaptive filters with respect to convergence speed and noise rejection is specific frequencies.

• Korean Journal of Environment and Ecology
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• v.24 no.2
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• pp.202-208
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• 2010

This study reports developmental changes in the vocal signals produced by infant or young Pipistrellus abramus. In contrast to adult bats, infant bats of the P. abramus emitted calls characterized by multi harmonics and variable signal patterns. Infants at two day of age emitted a irregular signal and showed gentle FM signals between 10 and 20 days. After about 40 days of age, the young bats emitted similar signals to the echolocation calls of adults. As the infant bats growing up, vocal signals trend showen a decrease in pulse duration(p<0.001), pulse interval(p<0.001) and number of harmonic(p<0.001). And the peak frequency(p<0.001), starting frequency(p<0.001) and ending frequency(p<0.001) in infant bats increased with the age. The largest change in pulse interval occurred in 5 days. And the pulse duration, peak frequency, starting frequency and ending frequency of the bat's sounds changed most dramatically in 25 days of age.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.11
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• pp.596-602
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• 2015

In this study, experiments using bioelectronic signals and questionnaire surveys were carried out in learning conditions when temperatures changed from low- and high-uncomfortable to comfortable. As a result, the stress factor Photoplethysmography (PPG) decreased, while the Root Mean Square of Standard Deviation (RMSSD) of PPG increased when the indoor temperature was changed from low- or high-uncomfortable to comfortable. Additionally, the absolute power of the ${\alpha}$-wave in the brain increased. According to the analysis of the association between the questionnaire and bioelectronic signals, the standard deviation of the stress factor as measured by pulse was closely related to the result of the thermal sensation questionnaire. In addition, it was found that the concentration on studying improved under comfortable temperatures when compared to uncomfortable temperatures.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.294-299
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• 2007

For efficient and accurate diagnosis of ultrasound images, appropriate time gain compensation(TGC) and dynamic range(DR) control of ultrasound echo signals are important. TGC is used for compensating the attenuation of ultrasound echo signals along the depth, and DR controls the image contrast. In recent ultrasound systems, these two factors are automatically set by a system and/or manually adjusted by an operator to obtain the desired image quality on the screen. In this paper, we propose an algorithm to find the optimized parameter values far TGC and DR automatically. In TGC optimization, we determine the degree of attenuation compensation along the depth by dividing an image into vertical strips and reliably estimating the attenuation characteristic of ultrasound signals. For DR optimization, we define a novel cost function by properly using the characteristics of ultrasound images. We obtain experimental results by applying the proposed algorithm to a real ultrasound(US) imaging system. The results verify that the proposed algorithm automatically sets values of TGC and DR in real-time such that the subjective quality of the enhanced ultrasound images may be sufficiently high for efficient and accurate diagnosis.

• Journal of Biomedical Engineering Research
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• v.29 no.4
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.