• Journal of Bio-Environment Control
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• v.24 no.2
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• pp.85-92
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• 2015

This study examined the settlement of a 1-2W type greenhouse using a timber pile, which was recently established on Gyehwa-do reclaimed land, in order to obtain base data for the construction of a greenhouse on reclaimed land. The results of this study are as follows. foundation and timber pile increased over time, irrespective of the interior and exterior of the upon investigation of the ground, it was confirmed that there was no soft rock stratum (bedrock), and that a sedimentary stratum existed under the fill deposit, which is estimated to have been reclaimed during the site renovation. It was also found that a weathered zone was located under the fill deposit and sedimentary stratum, and that the soil texture of the entire ground floor consisted of clay mixed with sand, silty clay, and granite gneiss, in that order, regardless of boreholes. In addition, the underground water level was 0.3m below ground, regardless of boreholes. Despite a slight difference, the settlement of the greenhouse or measurement sites (channels). With regard to the pillar inside the greenhouse, except in the case of CH-2, the data at a site located on the side wall of the greenhouse (wind barrier side) indicated vibrations of relatively larger amplitude. Moreover, the settlement showed a significant increase during a certain period, which was subsequently somewhat reversed. Based on these phenomena, it was verified that the settlement range of each site in the interior and exterior of the greenhouse was between 1.0 and 7.5mm at this time, except in the case of CH-1. The results of the regression analysis indicated good correlation, with the coefficient of determination by site ranging between 0.6362 and 0.9340. Furthermore, the coefficient of determination ranged between 0.6046 and 0.8822 on the exterior of the greenhouse, which is lower than inside the greenhouse, but still indicates significant correlation.

• Proceedings of the Ginseng society Conference
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• 2007.05a
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• pp.23-24
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• 2007

Purpose: 인삼이 항당뇨 활성을 가진다는 연구가 많은 연구자들에 의해 진행되었고, 이는 인삼의 구성 성분 중 ginsenoside에 기인한다는 보고가 있다. 본 연구는 ginsenoside의 항당뇨 작용기전을 in vitro에서 알아보고자 3T3-L1 지방세포에서 glucose uptake와 췌장 베타세포인 HIT-T15 세포에서 insulin 분비 효과를 확인하였다. 이를 위하여 인삼을 식초로 처리한 긴삼의 70% MeOH 분획으로부터 protopanaxadiol 계인 ginsenoside $Rb_2$, $Rg_3$ 그리고 protopanaxtriol 계인 $Rg_2$를 분리하여 본 실험에 사용하였다. Method: Ginsenoside $Rb_2$, $Rg_2$, $Rg_3$가 지방 세포에서 glucose uptake에 미치는 효과를 확인하기 위하여 3T3-L1 세포를 DMEM (Dulbecco's Modified Eagle's Medium) 배지에서 분화 유도시켰으며 3T3-L1 preadipocyte가 80% 정도 자라면 분화 유도 배지 (5% fetal bovine serum (FBS), 0.5 mM isobutylmethylxanthine (IBMX), 1 mM dexamethasone 그리고 $10{\mu}g/ml$ insulin가 포함된 DMEM)로 4일, $10{\mu}g/ml$ insulin가 포함된 DMEM으로 2일, FBS만 포함된 DMEM으로 2일 배양하여 총 8일 동안 분화를 유도하였다. 분화 유도된 3T3-L1 adipocytes 에 각각 $Rb_2$, $Rg_2$, $Rg_3$를 $20{\mu}M$로 처리하여 16시간 배양하여 low glucose DMEM에서 3시간 배양한 후에 $37^{\circ}C$에서 insulin 10 ng/ml 과 각각 $Rb_2$, $Rg_2$, $Rg_3$가 포함된 Krebs Ringer Hepes buffer(KRP buffer)에서 20분간 배양하였다. 2-deoxy-D-[$^3H$]-glucose를 넣고 10분 후에 차가운 PBS로 반응을 종결시켜 lysis buffer로 cell을 모은 후 scintillation counter를 이용하여 glucose를 측정하였다. Insulin 분비 효과는 HIT-T15 세포와 일차 배양한 흰쥐 소도세포(islets)를 사용하여 확인하였다. HIT-T15 세포는 24 well plate에 well 당 $2{\times}10^5$ 개씩 분주하여 24시간 동안 배양한 후 시료를 처리하였으며 소도 세포는 Sprague-Dawley rat의 췌장에 collagenase가 포함된 Hanks' Balanced Salt Solution(HBSS)을 주입하여 분리하고 islets을 얻었다. 분리한 소도세포를 $1{\sim}2$일 동안 배양하여 $Rb_2$, $Rg_2$, $Rg_3$가 각각 $20{\mu}M$의 농도로 첨가된 insulin 측정용 buffer인 Krebs-Ringer buffer (KRB+0.3% BSA, KRBB)에 $37^{\circ}C$에서 1시간 incubation 시킨 후 배양액으로 분비된 인슐린의 양을 측정하였다. 한편 ginsenoside의 인슐린 분비 촉진 기전을 알아보기 위한 실험에서는 ATP-sensitive $K^+$ channel opener인 diazoxide (0.5 mM)가 ginsenoside에 의해 촉진된 인슐린 분비를 억제하는지 살펴보았다. Result: glucose uptake assay 에서는 $Rg_2$가 가장 크게 glucose uptake를 증가시켰고 $Rb_2$, $Rg_3$는 그 활성이 크지 않았다. 한편 Insulin 분비 효과는 diol계인 $Rg_3$에서 용량 의존적으로 인슐린의 분비를 촉진시켰으며 $20{\mu}M$ 농도에서 대조군과 비교해 1.5배 이상의 분비 촉진 효과를 보였고 triol계인 $Rg_2$ 에서는 이러한 효과가 나타나지 않았다. $Rg_3$의 인슐린 분비 촉진 기전을 0.5 mM 의 diazoxide를 이용하여 확인한 결과 $Rg_3$에 의해 촉진된 인슐린 분비를 감소시켰다. 이로 미루어보아 $Rg_3$의 인슐린 분비 촉진 기전은 ATP-sensitive $K^+$ 채널의 봉쇄에 의한 것임을 확인할 수 있었다.

• Development and Reproduction
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• v.10 no.3
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• pp.159-168
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• 2006

Rodents and many other mammals have two chemosensory systems that mediate responses to pheromones, the main and accessory olfactory system, MOS and AOS, respectively. The chemosensory neurons associated with the MOS are located in the main olfactory epithelium, while those associated with the AOS are located in the vomeronasal organ(VNO). Pheromonal odorants access the lumen of the VNO via canals in the roof of the mouth, and are largely thought to be nonvolatile. The main pheromone receptor proteins consist of two superfamilies, V1Rs and V2Rs, that are structurally distinct and unrelated to the olfactory receptors expressed in the main olfactory epithelium. These two type of receptors are seven transmembrane domain G-protein coupled proteins(V1R with $G_{{\alpha}i2}$, V2R with $G_{0\;{\alpha}}$). V2Rs are co-expressed with nonclassical MHC Ib genes(M10 and other 8 M1 family proteins). Other important molecular component of VNO neuron is a TrpC2, a cation channel protein of transient receptor potential(TRP) family and thought to have a crucial role in signal transduction. There are four types of pheromones in mammalian chemical communication - primers, signalers, modulators and releasers. Responses to these chemosignals can vary substantially within and between individuals. This variability can stem from the modulating effects of steroid hormones and/or non-steroid factors such as neurotransmitters on olfactory processing. Such modulation frequently augments or facilitates the effects that prevailing social and environmental conditions have on the reproductive axis. The best example is the pregnancy block effect(Bruce effect), caused by testosterone-dependent major urinary proteins(MUPs) in male mouse urine. Intriguingly, mouse GnRH neurons receive pheromone signals from both odor and pheromone relays in the brain and may also receive common odor signals. Though it is quite controversial, recent studies reveal a complex interplay between reproduction and other functions in which GnRH neurons appear to integrate information from multiple sources and modulate a variety of brain functions.

• Geophysics and Geophysical Exploration
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• v.1 no.1
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• pp.25-30
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• 1998

Time-lapse crosswell seismic data, recorded before and after the cavity filling, showed that the filling increased the velocity at a known cavity zone in an old mine site in Inchon area. The seismic response depicted on the tomogram and in conjunction with the geologic data from drillings imply that the size of the cavity may be either small or filled by debris. In this study, I attempted to evaluate the filling effect by analyzing velocity measured from the time-lapse tomograms. The data acquired by a downhole airgun and 24-channel hydrophone system revealed that there exists measurable amounts of source statics. I presented a methodology to estimate the source statics. The procedure for this method is: 1) examine the source firing-time for each source, and remove the effect of irregular firing time, and 2) estimate the residual statics caused by inaccurate source positioning. This proposed multi-step inversion may reduce high frequency numerical noise and enhance the resolution at the zone of interest. The multi-step inversion with different starting models successfully shows the subtle velocity changes at the small cavity zone. The inversion procedure is: 1) conduct an inversion using regular sized cells, and generate an image of gross velocity structure by applying a 2-D median filter on the resulting tomogram, and 2) construct the starting velocity model by modifying the final velocity model from the first phase. The model was modified so that the zone of interest consists of small-sized grids. The final velocity model developed from the baseline survey was as a starting velocity model on the monitor inversion. Since we expected a velocity change only in the cavity zone, in the monitor inversion, we can significantly reduce the number of model parameters by fixing the model out-side the cavity zone equal to the baseline model.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Journal of IKEEE
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• v.25 no.1
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• pp.156-167
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• 2021

In this paper, we propose a digital transceiver unit design for in-building of 5G optical repeaters that extends the coverage of 5G mobile communication network services and connects to a stable wireless network in a building. The digital transceiver unit for driving the proposed 5G optical repeater is composed of 4 blocks: a signal processing unit, an RF transceiver unit, an optical input/output unit, and a clock generation unit. The signal processing unit plays an important role, such as a combination of a basic operation of the CPRI interface, a 4-channel antenna signal, and response to external control commands. It also transmits and receives high-quality IQ data through the JESD204B interface. CFR and DPD blocks operate to protect the power amplifier. The RF transmitter/receiver converts the RF signal received from the antenna to AD, is transmitted to the signal processing unit through the JESD204B interface, and DA converts the digital signal transmitted from the signal processing unit to the JESD204B interface and transmits the RF signal to the antenna. The optical input/output unit converts an electric signal into an optical signal and transmits it, and converts the optical signal into an electric signal and receives it. The clock generator suppresses jitter of the synchronous clock supplied from the CPRI interface of the optical input/output unit, and supplies a stable synchronous clock to the signal processing unit and the RF transceiver. Before CPRI connection, a local clock is supplied to operate in a CPRI connection ready state. XCZU9CG-2FFVC900I of Xilinx's MPSoC series was used to evaluate the accuracy of the digital transceiver unit for driving the 5G optical repeater proposed in this paper, and Vivado 2018.3 was used as the design tool. The 5G optical repeater digital transceiver unit proposed in this paper converts the 5G RF signal input to the ADC into digital and transmits it to the JIG through CPRI and outputs the downlink data signal received from the JIG through the CPRI to the DAC. And evaluated the performance. The experimental results showed that flatness, Return Loss, Channel Power, ACLR, EVM, Frequency Error, etc. exceeded the target set value.

• Journal of IKEEE
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• v.26 no.3
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• pp.408-415
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• 2022

In this paper, we propose a 3D point cloud reconstruction technique from 2D images using efficient feature map extraction network. The originality of the method proposed in this paper is as follows. First, we use a new feature map extraction network that is about 27% efficient than existing techniques in terms of memory. The proposed network does not reduce the size to the middle of the deep learning network, so important information required for 3D point cloud reconstruction is not lost. We solved the memory increase problem caused by the non-reduced image size by reducing the number of channels and by efficiently configuring the deep learning network to be shallow. Second, by preserving the high-resolution features of the 2D image, the accuracy can be further improved than that of the conventional technique. The feature map extracted from the non-reduced image contains more detailed information than the existing method, which can further improve the reconstruction accuracy of the 3D point cloud. Third, we use a divergence loss that does not require shooting information. The fact that not only the 2D image but also the shooting angle is required for learning, the dataset must contain detailed information and it is a disadvantage that makes it difficult to construct the dataset. In this paper, the accuracy of the reconstruction of the 3D point cloud can be increased by increasing the diversity of information through randomness without additional shooting information. In order to objectively evaluate the performance of the proposed method, using the ShapeNet dataset and using the same method as in the comparative papers, the CD value of the method proposed in this paper is 5.87, the EMD value is 5.81, and the FLOPs value is 2.9G. It was calculated. On the other hand, the lower the CD and EMD values, the better the accuracy of the reconstructed 3D point cloud approaches the original. In addition, the lower the number of FLOPs, the less memory is required for the deep learning network. Therefore, the CD, EMD, and FLOPs performance evaluation results of the proposed method showed about 27% improvement in memory and 6.3% in terms of accuracy compared to the methods in other papers, demonstrating objective performance.

• Journal of Practical Agriculture & Fisheries Research
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• v.16 no.1
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• pp.37-53
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• 2014

The purpose of this research is to find what difficulties the agricultural successors, the Korea National College of Agriculture and Fisheries (KNCAF) graduates, face with in implementing eco-friendly agriculture in Jeju, and what solutions they can be provided with. This research, a case study on the basis of open-ended survey questions, has 6 cases out of 8 graduates who have or had implemented eco-friendly citrus farming. In Jeju, 24 graduates have involved in citrus farming. According to the case study, only one case was environment-friendly farming method at the pesticide-free level, and the others at organic farming level. All the cases have tried to alter main crops or to diversify management for coping with global climate change and market-opening. On analyzing operating cost to gain product of merchantable quality, it revealed that the environment-friendly farming method needs much more managing efforts than the conventional farming does. But to the contrary, the materials cost in the environment-friendly farming method was lower than in the conventional farming method. In the total production and the price, the environment-friendly farming was 20~50% lower and 10~50% higher than the conventional farming, respectively. Difficulties which the graduates confronted with in implementing the environment-friendly agriculture are as below. Firstly, many of the difficulties have resulted from lack of the environment-friendly farming techniques, and the high cost of farm scale improvement due to high price of land and topographical features of Jeju. Secondly, the agricultural successors, the KNCAF graduates, have trouble in obtaining approval of their parents to changeover from the conventional farming to the environment-friendly farming. Lastly, there is no advisory organizations and experts for environment-friendly farming in the given area. For shift to the environment-friendly farming, followings are needed. Agricultural Technology & Extension center, with cooperation of leading farms in environment-friendly farming, should have a key role in offering education and consults on the environment-friendly farming techniques. Also, this organization should inform rapidly the research results to the farmers, and their feed-back should be involved in the next research. Therefore, it is suggested that the forum called 'Environment-friendly Organic Farming Forum in Jeju' tentatively is organized.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.827-833
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• 2023

CT(Computed Tomography) contrast agents are commonly used in general hospitals and university hospitals when taking radiographic examinations. The CT contrast medium contains a mixture of a substance called "Iodine", which absorbs radiation energy and makes it appear white in the CT image, further improving the image quality. In addition, the CT contrast agent, which moves like blood in the blood vessels, clearly differentiates it from muscle and water, so CT contrast agents are widely used in hospitals. These CT contrast agents absorb X-rays, but in order to absorb X-rays, they must have a high density or a high radiation absorption coefficient. Since the CT contrast agent is injected into the blood vessels, if the density is high, the blood vessels are strained and the patient is in shock. For this reason, it is necessary to match the density similar to that of water and always pay attention to side effects. In addition, the amount of CT contrast medium is adjusted according to the patient's body shape, and the remaining contrast medium is discarded. However, This study tried to find out the idea of recycling it as a radiation shielding material. Since the CT contrast medium has a high radiation absorption coefficient at a density similar to that of water, the amount to absorb radiation is adjusted, the amount of contrast medium and the amount of water are adjusted, and the amount of radiation absorbed is determined by mixing with water. In addition, a study was conducted to find out the result of the difference in radiation absorption in various ways by comparing the radiation quality coefficient and absorption coefficient with other substances or materials in an environmentally friendly method harmless to the human body by mixing CT contrast medium and water.