• 한국초등과학교육학회지:초등과학교육
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• 제26권1호
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• pp.49-62
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• 2007

The higher cognitive functions of the human brain including teaming are hypothesized to be selectively distributed across large-scale neural networks interconnected to the cortical and subcortical areas. Recently, advances in functional imaging have made it possible to visualize the brain areas activated by certain cognitive activities in vivo. Neural substrates for teaming and motivation have also begun to be revealed. Functional magnetic resonance imaging (fMRI) provides a non-invasive indirect mapping of cerebral activity, based on the blood- oxygen level dependent (BOLD) contrast which is based on the localized hemodynamic changes following neural activities in certain areas of the brain. The fMRI method is now becoming an essential tool used to define the neuro-functional mechanisms of higher brain functions such as memory, language, attention, learning, plasticity and emotion. Further research in the field of education will accelerate the verification of the effects on loaming or help in the selection of model teaching strategies. Thus, the purpose of this study was to review brain study methods using fMRI in science education. In conclusion, a number of possible strategies using fMRI for the study of elementary science education were suggested.

• The Korean Journal of Physiology and Pharmacology
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• 제13권3호
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• pp.221-227
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• 2009

For successful restoration of visual function by a visual neural prosthesis such as retinal implant, electrical stimulation should evoke neural responses so that the informat.ion on visual input is properly represented. A stimulation strategy, which means a method for generating stimulation waveforms based on visual input, should be developed for this purpose. We proposed to use the decoding of visual input from retinal ganglion cell (RGC) responses for the evaluation of stimulus encoding strategy. This is based on the assumption that reliable encoding of visual information in RGC responses is required to enable successful visual perception. The main purpose of this study was to determine the influence of inter-dependence among stimulated RGCs activities on decoding accuracy. Light intensity variations were decoded from multiunit RGC spike trains using an optimal linear filter. More accurate decoding was possible when different types of RGCs were used together as input. Decoding accuracy was enhanced with independently firing RGCs compared to synchronously firing RGCs. This implies that stimulation of independently-firing RGCs and RGCs of different types may be beneficial for visual function restoration by retinal prosthesis.

• 전기학회논문지
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• 제56권11호
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• pp.1998-2003
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• 2007

A flexible but implantable polyimide-based neural implant was fabricated for reliable and stable long-term monitoring of neural activities from brain. The developed neural implant provides 3-dimensional (3D) $3{\times}3$ structure, avoids any hand handling, and makes the insertion more efficient and reliable. Any film curvature caused by residual stress was not observed in the electrode. The 3D flexible polyimide electrode penetrated a dense gel whose stiffness is close to live brain tissue, because a ${\sim}1{\mu}m$ thick nickel was electroplated along the edge of the shank in order to improve the stiffness. The recording sites were positioned at both side of the shank to increase the probability of recording neural signals from a target volume of tissue. Impedance remained stable over 72 hours because of extremely low moisture uptake in the polyimide dielectric layers. At electrical recording test in vitro, the fabricated electrode showed excellent recording performance, suggesting that this electrode has the potential for great recording from neuron firing and long-term implant performance.

• 국제물리치료학회지
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• 제9권2호
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• pp.1447-1454
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• 2018

The objective of this study was to investigate the dynamic neural mobilization program on the changes in muscle activity and nerve conduction velocity (NVC) in stroke patients. The participants were sampled and randomly divided into experimental group I (n=12) who underwent arm neural mobilization and experimental group II (n=13) who underwent arm dynamic neural mobilization. As the pretest, peripheral NVC of the radial, median, and ulnar nerves were measured using the Viking Quest; the biceps brachii, brachioradialis, flexor carpi radialis, and extensor carpi radialis activities were measured with sEMG. Each intervention program consisted of 10 trials per set and three sets per session. The intervention programs were performed once daily for four weeks (four days/week). Posttest measurements were taken equally as the pretest measurements. Significant differences in peripheral NVC in all sections of the radial and median nerves and wristbelow elbow and below elbow-above elbow areas of the ulnar nerve, as well as in muscle activity of all muscles except the biceps brachii. These findings indicate that dynamic neural mobilization was effective in increasing peripheral NVC and altering the muscle activity.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.542-544
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• 2019

Changes in a person's health affect one's lifestyle and work activities. According to the World Health Organization (WHO), abnormal activity is growing faster in people aged 60 or more than any other age group in almost every country. This trend steadily continues and expected to increase further in the near future. Abnormal activity put these people at high risk of expected incidents since most of these people live alone. Human abnormal activity analysis is a challenging, useful and interesting problem among the researchers and its particularly crucial task in life and health care areas. In this paper, we discuss the problem of abnormal activities of old people lives alone at home. We propose Convolutional Neural Network (CNN) based model to detect the abnormal behaviors of elderlies by utilizing six simulated action data from daily life actions.

• Environmental Engineering Research
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• 제18권4호
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• pp.211-219
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• 2013

Rare-earth manganites have a great potential for environmental applications based on their chemical and physical properties. The use of rare-earth manganites as catalysts for environmentally essential reactions was reviewed. Artificial neural networks were used to assess the catalytic activity in oxidation reactions. Relative catalytic activities of the catalysts were further discussed. We concluded that cerium manganite is the most practicable catalyst for technological purposes.

• 대한의용생체공학회:의공학회지
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• 제44권2호
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• pp.147-156
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• 2023

Efforts to employ smart home sensors to monitor the indoor activities of elderly single residents have been made to assess the feasibility of a safe and healthy lifestyle. However, the bathroom remains an area of blind spot. In this study, we have developed and evaluated a new edge computer device that can automatically detect water usage activities in the bathroom and record the activity log on a cloud server. Three kinds of sound as flushing, showering, and washing using wash basin generated during water usage were recorded and cut into 1-second scenes. These sound clips were then converted into a 2-dimensional image using MEL-spectrogram. Sound data augmentation techniques were adopted to obtain better learning effect from smaller number of data sets. These techniques, some of which are applied in time domain and others in frequency domain, increased the number of training data set by 30 times. A deep learning model, called CRNN, combining Convolutional Neural Network and Recurrent Neural Network was employed. The edge device was implemented using Raspberry Pi 4 and was equipped with a condenser microphone and amplifier to run the pre-trained model in real-time. The detected activities were recorded as text-based activity logs on a Firebase server. Performance was evaluated in two bathrooms for the three water usage activities, resulting in an accuracy of 96.1% and 88.2%, and F1 Score of 96.1% and 87.8%, respectively. Most of the classification errors were observed in the water sound from washing. In conclusion, this system demonstrates the potential for use in recording the activities as a lifelog of elderly single residents to a cloud server over the long-term.

• The Korean Journal of Pain
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• 제36권1호
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• pp.72-83
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• 2023

Background: Globally, spinal cord injury (SCI) results in a big burden, including 90% suffering permanent disability, and 60%-69% experiencing neuropathic pain. The main causes are oxidative stress, inflammation, and degeneration. The efficacy of the stem cell secretome is promising, but the role of human neural stem cell (HNSC)-secretome in neuropathic pain is unclear. This study evaluated how the mechanism of HNSC-secretome improves neuropathic pain and locomotor function in SCI rat models through antioxidant, anti-inflammatory, anti-matrix degradation, and neurotrophic activities. Methods: A proper experimental study investigated 15 Rattus norvegicus divided into normal, control, and treatment groups (30 µL HNSC-secretome, intrathecal in the level of T10, three days post-traumatic SCI). Twenty-eight days post-injury, specimens were collected, and matrix metalloproteinase (MMP)-9, F2-Isoprostanes, tumor necrosis factor (TNF)-α, transforming growth factor (TGF)-β, and brain derived neurotrophic factor (BDNF) were analyzed. Locomotor recovery was evaluated via Basso, Beattie, and Bresnahan scores. Neuropathic pain was evaluated using the Rat Grimace Scale. Results: The HNSC-secretome could improve locomotor recovery and neuropathic pain, decrease F2-Isoprostane (antioxidant), decrease MMP-9 and TNF-α (anti-inflammatory), as well as modulate TGF-β and BDNF (neurotrophic factor). Moreover, HNSC-secretomes maintain the extracellular matrix of SCI by reducing the matrix degradation effect of MMP-9 and increasing the collagen formation effect of TGF-β as a resistor of glial scar formation. Conclusions: The present study demonstrated the mechanism of HNSC-secretome in improving neuropathic pain and locomotor function in SCI through antioxidant, anti-inflammatory, anti-matrix degradation, and neurotrophic activities.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.455-462
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• 2020

Noise which is unwanted sound is a serious pollutant that can affect human health, as well as the working and living environment if exposed to humans. However, current noise management on the construction project is generally conducted after the noise exceeds the regulation standard, which increases the conflicts with inhabitants near the construction site and threats to the safety and productivity of construction workers. To overcome the limitations of the current noise management methods, the activities of construction equipment which is the main source of construction noise need to be managed throughout the construction period in real-time. Therefore, this paper proposed a framework for automatically detecting noise sources in construction sites in real-time based on convolutional neural networks (CNNs) according to the following four steps: (i) Step 1: Definition of the noise sources; (ii) Step 2: Data preparation; (iii) Step 3: Noise source classification using the audio CNN; and (iv) Step 4: Noise source detection using the visual CNN. The short-time Fourier transform (STFT) and temporal image processing are used to contain temporal features of the audio and visual data. In addition, the AlexNet and You Only Look Once v3 (YOLOv3) algorithms have been adopted to classify and detect the noise sources in real-time. As a result, the proposed framework is expected to immediately find construction activities as current noise sources on the video of the construction site. The proposed framework could be helpful for environmental construction managers to efficiently identify and control the noise by automatically detecting the noise sources among many activities carried out by various types of construction equipment. Thereby, not only conflicts between inhabitants and construction companies caused by construction noise can be prevented, but also the noise-related health risks and productivity degradation for construction workers and inhabitants near the construction site can be minimized.

• The Korean Journal of Physiology and Pharmacology
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• 제13권6호
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• pp.443-448
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• 2009

For successful visual perception by visual prosthesis using electrical stimulation, it is essential to develop an effective stimulation strategy based on understanding of retinal ganglion cell (RGC) responses to electrical stimulation. We studied RGC responses to repetitive electrical stimulation pulses to develop a stimulation strategy using stimulation pulse frequency modulation. Retinal patches of photoreceptor-degenerated retinas from rd1 mice were attached to a planar multi-electrode array (MEA) and RGC spike trains responding to electrical stimulation pulse trains with various pulse frequencies were observed. RGC responses were strongly dependent on inter-pulse interval when it was varied from 500 to 10 ms. Although the evoked spikes were suppressed with increasing pulse rate, the number of evoked spikes were >60% of the maximal responses when the inter-pulse intervals exceeded 100 ms. Based on this, we investigated the modulation of evoked RGC firing rates while increasing the pulse frequency from 1 to 10 pulses per second (or Hz) to deduce the optimal pulse frequency range for modulation of RGC response strength. RGC response strength monotonically and linearly increased within the stimulation frequency of 1~9 Hz. The results suggest that the evoked neural activities of RGCs in degenerated retina can be reliably controlled by pulse frequency modulation, and may be used as a stimulation strategy for visual neural prosthesis.