• Annals of Clinical Neurophysiology
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• v.1 no.2
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• pp.91-98
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• 1999

Background : The pathogenesis of acute inflammatory demyelinating polyradiculoneuropathy (AIDP), Guillain Barre syndrome (GBS) is not clear, but it has been known that the immune mechanisms play an important role. Authors performed this study to establish an animal model of experimental allergic neuritis (EAN) by immunizing the myelin components of peripheral nerves and to understand the electrophysiological and histopathological features as well as the ${CD_5}^+$ B-lymphocyte changes in peripheral bloods in the EAN models. Methods : Lewis rats weighing 150-200 gm were injected subcutaneously in soles two times with total myelin, P0, P1, or P2 proteins purified from the bovine cauda eguina. The EAN induction was assessed by evaluating clinical manifestations. The electrophysiological and histopathological features were studied as routine methods. The ${CD_5}^+$ Blymphocytes were double stained using monoclonal FITC conjugated anti-rat CD45RA and R-PE conjugated anti-rat ${CD_5}^+$ antibodies and calculated using a fluorescence activated cell sorter (FACS). Results : The EAN animal models were established. In two out of five, in one out of two, in none out of three, and in none out of one Lewis rats injected with purified total myelin, P0, P1, P2 proteins respectively, They showed slow spontaneous motor activity and weak resistance against pulling back by tails. The typical electrophysiological and histologic findings in total protein and P0 induced EAN animal models were the decreased conduction velocity, the decreased compound muscle action potential (CMAP) amplitude and the dispersion phenomenon. The perivascular infiltrates of lymphocytes with focal demyelinating process were found in light microscopy. The ${CD_5}^+$ B-lymphocyte expression in three EANs were 2.38%, 3.50% 2.50%, which were not significantly increased, compared with those in normal controls. Conclusion : The EAN animal models were successfully established by injecting the total myelin and P0 myelin and they showed electrophysiological and histological features typical of demyelinating process. However they did not show an increased expression of ${CD_5}^+$ B-lymphocyte in peripheral bloods which could be indirect evidence of humoral autoimmunity.

• Science of Emotion and Sensibility
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• v.8 no.4
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• pp.365-374
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• 2005

Pain has at least two dimensions such as somatosensory qualities and affect and patients are frequently asked to score the intensity of their pain on a numerical pain rating scale. However, the use of a undimensional scale is questionable in view of the belief, overwhelmingly supported by clinical experience as well as by empirical evidence from multidimensional scaling and other sources, that pain has multidimensions such as sensory-discrimitive, motivational-affective and cognitive-evaluative The study of pain has recently received much attention, especially in understanding its neurophysiology by using new brain imaging techniques, such as positron emission tomography(PET) and functional magnetic resonance imaging (fMRI), both of which allow us to visualize brain function in vivo. Also the new brainimaging devices allow us to evaluate the patients pain status and plan To treat patients objectively. Base4 on our findings we presented what are the new brain imaging devices and the results of study by using brain imaging devices.

• Korean Journal of Environment and Ecology
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• v.28 no.6
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• pp.705-714
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• 2014

This text was analyzed and investigated the aerial part plants and buried seed plants at coniferous forest and broadleaved forest in Yesan-gun, in order to offer the basic data of potential natural vegetation change on secondary forest. Plants of buried seed germination were consisted of 29 taxa in coniferous forest (28 species, 1 varieties, of 27 genus, 20 families) and 36 taxa in broadleaved forest (34 species, 2 varieties, of 32 genus, 18 families). Family classification of buried seed plant was the most in Compositae, and emergent plot frequency was the highest of Cyperus amuricus in coniferous forest and Crepidiastrum sonchifolium in broadleaved forest. The soil depth of the most plants appearance was 0~10 cm in coniferous forest and 0~5 cm in broadleaved forest, and the soil depth of the most population appearance was 0~2 cm in coniferous forest and broadleaved forest. Population of buried seed germination was decreased according as soil is deep. Crepidiastrum sonchifolium was a plant that population of buried seed germination is the most. Similarity index of the aerial part plants and buried seed plants was low as 0.22, and coniferous forest and broadleaved forest was 0.40.

• Journal of the Korean Recycled Construction Resources Institute
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• v.8 no.2
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• pp.167-174
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• 2020

Many researches have been conducted to utilize recycled aggregates in Korea, but since most sources of recycled aggregates are not clear, there is a lot of uncertainty in applying the existing research results on recycle of aggregates generated from nuclear power plants. In this study, therefore, in order to investigate the possibility of recycling coarse aggregates generated through dismantling of nuclear power plants in Korea, recycled coarse aggregates were produced from concrete simulating nuclear power plants in Korea. Using the recycled coarse aggregates, concrete was mixed in consideration of the mixing ratio of the recycled coarse aggregates, and the mechanical properties were experimentally investigated. From the test results, as the mixing ratio of recycled coarse aggregates increased. concrete compressive strength, tensile strength, and elastic modulus generally decreased up to 36, 37, and 27% from the mechanical properties of normal concrete, respectively. Therefore, it can be concluded that limitation on the mixing ratio of recycled coarse aggregates is necessary when coarse aggregates are recycled through dismantling of nuclear power plants.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.34-43
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• 2019

The maintenance cost for road pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance preventive maintenance requires the establishment of a strategic plan through accurate prediction of road pavement. Hence, In this study, the deep neural network(DNN) and the recurrent neural network(RNN) were used in order to develop the expressway pavement damage prediction model. A superior model among these two network models was then suggested by comparing and analyzing their performance. In order to solve the RNN's vanishing gradient problem, the LSTM (Long short-term memory) circuits which are a more complicated form of the RNN structure were used. The learning result showed that the RMSE value of the RNN-LSTM model was 0.102 which was lower than the RMSE value of the DNN model, indicating that the performance of the RNN-LSTM model was superior. In addition, high accuracy of the RNN-LSTM model was verified through the comparison between the estimated average road pavement condition and the actually measured road pavement condition of the target section over time.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.89-97
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• 2021

Fault detection in seismic data is well suited to the application of machine learning algorithms. Accordingly, various machine learning techniques are being developed. In recent studies, machine learning models, which utilize synthetic data, are the particular focus when training with deep learning. The use of synthetic training data has many advantages; Securing massive data for training becomes easy and generating exact fault labels is possible with the help of synthetic training data. To interpret real data with the model trained by synthetic data, the synthetic data used for training should be geologically realistic. In this study, we introduce a method to generate realistic synthetic seismic data. Initially, reflectivity models are generated to include realistic fault structures, and then, a one-way wave equation is applied to efficiently generate seismic stack sections. Next, a migration algorithm is used to remove diffraction artifacts and random noise is added to mimic actual field data. A convolutional neural network model based on the U-Net structure is used to verify the generated synthetic data set. From the results of the experiment, we confirm that realistic synthetic data effectively creates a deep learning model that can be applied to field data.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.221-239
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• 2022

Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.

• Journal of the Korean Orthopaedic Association
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• v.56 no.4
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• pp.283-293
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• 2021

Degenerative disc disease has traditionally been thought of as low back pain caused by changes in the nucleus pulposus and annulus fibrous, in recent studies, however, changes in the upper and lower endplates cause degeneration of the disc, resulting in mechanical pressure, inflammatory reactions and low back pain. Recently, the bone marrow of the vertebral body-endplate-nucleus pulposus and annulus fibrous were considered as a single unit, and the relationship was explained. Once the endplate is damaged, it eventually aggravates the degeneration of the bone marrow, nucleus pulposus, and annulus fibrosus. In this process, the compression force of the annulus fibrosus increases, and an inflammatory reaction occurs due to inflammatory mediators. Hence, the sinuvertebral nerves and the basivertebral nerves are stimulated to cause back pain. If these changes become chronic, degenerative changes such as Modic changes occur in the bone marrow in the vertebrae. Finally, in the case of degenerative intervertebral disc disease, the bone marrow of the vertebral body-endplate-nucleus pulposus and annulus fibrous need to be considered as a single unit. Therefore, when treating patients with chronic low back pain, it is necessary to consider the changes in the nucleus pulposus and annulus fibrosus and a lesion of the endplate.