• Korean Journal of Food Science and Technology
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• v.25 no.1
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• pp.15-21
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• 1993

Crude ${\beta}-glucan$ extracted from Barley was purified by stepwise enzyme treatment (Thermostable ${\alpha}-amylase$, amyloglucosidase, protease). The Intrinsic Viscosity $[{\eta}]$ of the purified ${\beta}-glucan$ was determined by Cannon Fenske Capillary Viscometer (size 50, Cannon Instruments, State, College pa.) at different pH (2, 4, 7, 9, 11) and various salt concentration (0.01 M, 0.03 M, 0.05 M, 0.07 M, 0.1 M and 0.2 M). The $[{\eta}]$ of purified ${\beta}-glucan$ was ranged from $0.997{\sim}2.290\;dl/g$. The $[{\eta}]$ of purified ${\beta}-glucan$ at both alkali, acid condition were lower than those at pH 7. However, the alkali condition of puified ${\beta}-glucan$ solution showed less $[{\eta}]$ than the acid condition of this solution. From 0 M to 0.2 M salt concentration, the $[{\eta}]$ of purified ${\beta}-glucan$ solution was decreased to 0.03 M then increased to 0.05 M NaCl and remained constant to 0.2 M NaCl. The chain stiffness parameter of purified ${\beta}-glucan$ was not affected by temperature from $15^{\circ}C$ to $65^{\circ}C$. The shear rates of various ${\beta}-glucan$ conditions were determined by Bohlin Rheometer (Lund, Sweden). The ${\beta}-glucan$ concentration of 1.0 g/dl and 2.0 g/dl behaved as Newtonian fluid. However, above the concentration of 3.0 g/dl ${\beta}-glucan$ solution, it showed thixotropic and psedoplastic characteristics. Barley ${\beta}-glucan$ appears a damping at 0.5 frequency for the 4.0 g/dl solution. Below 0.5 frequency, it appears a viscous behavior property and above 0.5 frequency, it appears a elastic behavior property.

• Journal of Environmental Impact Assessment
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• v.26 no.6
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• pp.553-562
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• 2017

In these days, agricultural reservoirs are considered as a useful resource for recreational purposes, tour and cultural amenity for vicinity communities as well as irrigation water supply. However, many of the agricultural reservoirs are showing a eutrophic or hyper-eutrophic state and high level of organic contamination. In particular, about 44.7% of the aged agricultural reservoirs that constructed before 1945 exceed the water quality criteria for irrigational water use. In addition to external loading, internal nutrient loading from bottom sediment may play an important role in the nutrient budget of the aged reservoirs. The objectives of this study were to characterize variations of thermal structure of a shallow M reservoir (mean depth 1.7 m) and examine the potential of internal nutrient loading by continuous monitoring of vertical water temperature and dissolved oxygen (DO) concentration profiles in 2015 and 2016. The effect of internal loading on the total loading of the reservoir was evaluated by mass balance analysis. Results showed that a weak thermal stratification and a strong DO stratification were developed in the shallow M Reservoir. And, dynamic temporal variation in DO was observed at the bottom of the reservoir. Persistent hypoxic conditions (DO concentrations less than 2 mg/L) were established for 87 days and 98 days in 2015 and 2016, respectively, during the no-rainy summer periods. The DO concentrations intermittently increased during several events of atmospheric temperature drop and rainfall. According to the mass balance analysis, the amount of internal $PO_4-P$ loading from sediment to the overlying water were 37.9% and 39.7% of total loading during no-rainy season in 2015 and 2016, respectively on August when algae growth is enhanced with increasing water temperature. Consequently, supply of DO to the lower layer of the reservoir could be effective countermeasure to reduce nutrient release under the condition of persistent DO depletion in the bottom of the reservoir.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.52-61
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• 2011

Whereas recent researches have elucidated the positive ecological roles of large wood (LW) in fishbearing channels, LW is also recognized as a negative factor of log-laden debris flows and floods in densely populated areas. However in Republic of Korea, no study has investigated longitudinal variations of LW distribution and dynamic along the stream corridor. Hence to elucidate 1) physical factors controlling longitudinal distribution of LW and 2) their effect on variation in LW load amount, we surveyed the amount of LW with respect to channel morphology in a mountain stream, originated from Mt. Ki-ryong in Inje, Gangwondo. Model selection in the Generalized Linear Model procedure revealed that number of boulder (greater than or equal to 1.0 m in diameter), bankfull channel width and their interaction were the best predictors explaining LW load volume per unit channel segment area (unit LW load). In general, boulders scattered within small mountain streams influence LW retention as flow obstructions. However, in this study, we found that the effect of the boulders vary with the channel width; that is, whereas the unit LW load in the segment with narrow channel width increased continuously with increasing boulder number, it in the segment with wide channel width did not depend on the boulder number. This should be because that, in two channels having different widths, the rates of channel widths reduced by boulders are different although boulder numbers are same. Our findings on LW load varying with physical factors (i.e., interaction of boulder number and channel width) along the stream corridor suggest understanding for longitudinal continuum of hydrogeomorphic and ecologic characteristics in stream environments, and these should be carefully applied into the erosion control works for systematic watershed management and subsequent disaster prevention.

• Korean Chemical Engineering Research
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• v.50 no.6
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• pp.980-987
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• 2012

Nonpoint source pollution causes leaks and overtopping, depending on the state of the sewer network as well as aggravates the pollution load of the aqueous water system as it is introduced into the sewer by wash-off. According, the need for efficient sewer monitoring system which can manage the sewage flowrate, water quality, inflow/infiltration and overflow has increased for sewer maintenance and the prevention of environmental pollution. However, the sewer monitoring is not easy since the sewer network is built in underground with the complex nature of its structure and connections. Sewer decontamination mechanism as well as pipe network monitoring and fault diagnosis of water network system on system analysis proposed in this study. First, the pollution removal pattern and behavior of contaminants in the sewer pipe network is analyzed by using sewer process simulation program, stormwater & wastewater management model for expert (XP-SWMM). Second, the sewer network fault diagnosis was performed using the multivariate statistical monitoring to monitor water quality in the sewer and detect the sewer leakage and burst. Sewer decontamination mechanism analysis with static and dynamic state system results showed that loads of total nitrogen (TN) and total phosphorous (TP) during rainfall are greatly increased than non-rainfall, which will aggravate the pollution load of the water system. Accordingly, the sewer outflow in pipe network is analyzed due to the increased flow and inflow of pollutant concentration caused by rainfall. The proposed sewer network monitoring and fault diagnosis technique can be used effectively for the nonpoint source pollution management of the urban watershed as well as continuous monitoring system.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.33 no.4
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• pp.275-284
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• 1997

This experiment was carried out to measure the sinking depth of each buoy, the change in the net shape of the net, and the tension of sand bag line according to the R (from bag net to the fish court) and L (from fish court to the bag net) current directions and their velocity by the model experiment. The model net was one-fiftieth of the real net, and its size was determined after considering the Tauti’s Similarity Law and the dimension of the experimental tank. 1. The changes of the net shape were as follows : In the current R, the end net of fish court moved 20mm down the lowerward tide and 10mm upper part. So the whole model net moved up at 0.2m/sec. The shape of the net showed an almost linear state from bag net to the fish court at 0.6m/sec. In the current L, the door net moved 242mm down the lowerward tide and 18mm upper part. So the whole model net moved up at 0.2m/sec. The net shape showed an almost linear state from the fish court to the bag net at 0.5m/sec. 2. The sinking depths of each buoy were as follows: In the current R, the head buoy started sinking at 0.2m/sec and sank 20mm, 99mm at 0.3m/sec and 0.6m/sec, respectively. The end buoy didn't sink from 0m/sec to 0.6m/sec but showed a slight quake. In the current L, the end buoy started sinking at 0.1m/sec, and sank 5mm and 108mm at 0.2m/sec and 0.6m/sec, respectively. The whole model net sank at 0.5m/sec except the head buoy. 3. The changes of the sand bag line tension were as follows: In the current R, the tension affected by the sand bag line of the head buoy showed 273.51g at 0.1m/sec increased to 1298.40g at 0.6m/sec. In the current L, the tension affected by the sand bag line of the end buoy on one side showed 137.08g at 0.1m/sec increased to 646.00g at 0.6m/sec. The changes in the sand bag line tension were concentrated on the sand bag line of the upperward tide with increasing velocity at the R and L current directions. However, no significant increase in tension was observed in the other sand bag lines.

• Korean Journal of Agricultural Science
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• v.22 no.1
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• pp.1-10
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• 1995

All agricultural crops and products should be cultured, harvested, handled and processed by the proper mechanical methods in the mechanized farming systems. Agricultural crops might be injured or deformed through various working stages due to static or dynamic forces of machines. Mechanical forces had to be applied with proper degrees to the agricultural crops in incoincidence with properties of crops without any damage of crops so as to increase the work efficiency qualitatively. Knowledges of mechanical properties of agricultural materials are essential to prevent of agricultural crops in relation with mechanical farming system. This study was carried out to examine and analyze the creep behavior of the rice stalk on growing and harvesting periods by mechanical model with computer measurement system in radial directional compressive force and bending force. The creep behavior of the rice stalk could be predicted precisely and its results approached closely to the measured values. The creep behaviors were increased greatly with increase of compressive force, namely, the steady state creep behavior occurred at the force less then 25N and the logarithmic creep behavior at the force bigger than 30N. The instantaneous elastic modulus $E_o$ and the retardation time ${\tau}_K$ were increased together with increase of applied forces, meanwhile the retarded elastic modulus $E_r$ and viscosity ${\eta}_v$ were decreased with increase of applied forces in mechanical model being expected the creep behavior in relation with the level of applied forces, which was well explained that the rice stalk might be visvo-elastic material. In the creep test along the stalk portion with compressive force and bending force, the intermediate portion showed greatest values and also the lower portion showed the least values, which implied that the intermediate portions of rice stalk were very weak. The steady state creep behavior occured at the intermediate portion and the upper portion in the rice stalk at the compressive force larger than 25.0N, which showed the possibility of injury due to external forces.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• The Korean Journal of Nuclear Medicine
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• v.39 no.6
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• pp.413-420
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• 2005

Purpose: Neuroreceptor PET studies require 60-120 minutes to complete and head motion of the subject during the PET scan increases the uncertainty in measured activity. In this study, we investigated the effects of the data-driven head mutton correction on the evaluation of endogenous dopamine release (DAR) in the striatum during the motor task which might have caused significant head motion artifact. Materials and Methods: $[^{11}C]raclopride$ PET scans on 4 normal volunteers acquired with bolus plus constant infusion protocol were retrospectively analyzed. Following the 50 min resting period, the participants played a video game with a monetary reward for 40 min. Dynamic frames acquired during the equilibrium condition (pre-task: 30-50 min, task: 70-90 min, post-task: 110-120 min) were realigned to the first frame in pre-task condition. Intra-condition registrations between the frames were performed, and average image for each condition was created and registered to the pre-task image (inter-condition registration). Pre-task PET image was then co-registered to own MRI of each participant and transformation parameters were reapplied to the others. Volumes of interest (VOI) for dorsal putamen (PU) and caudate (CA), ventral striatum (VS), and cerebellum were defined on the MRI. Binding potential (BP) was measured and DAR was calculated as the percent change of BP during and after the task. SPM analyses on the BP parametric images were also performed to explore the regional difference in the effects of head motion on BP and DAR estimation. Results: Changes in position and orientation of the striatum during the PET scans were observed before the head motion correction. BP values at pre-task condition were not changed significantly after the intra-condition registration. However, the BP values during and after the task and DAR were significantly changed after the correction. SPM analysis also showed that the extent and significance of the BP differences were significantly changed by the head motion correction and such changes were prominent in periphery of the striatum. Conclusion: The results suggest that misalignment of MRI-based VOI and the striatum in PET images and incorrect DAR estimation due to the head motion during the PET activation study were significant, but could be remedied by the data-driven head motion correction.

• The Korean Journal of Nuclear Medicine
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• v.39 no.6
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• pp.421-429
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• 2005

Purpose: It has been postulated that dopamine release in the striatum underlies the reinforcing properties of nicotine. Substantial evidence in the animal studies demonstrates that nicotine interacts with dopaminergic neuron and regulates the activation of the dopaminergic system. The aim of this study was to visualize the dopamine release by smoking in human brain using PET scan with $[^{11}C]raclopride$. Materials and Methods: Five male non-smokers or ex-smokers with an abstinence period longer than 1 year (mean age of $24.4{\pm}1.7$ years) were enrolled in this study $[^{11}C]raclopride$, a dopamine D2 receptor radioligand, was administrated with bolus-plus-constant infusion. Dynamic PET was performed during 120 minutes ($3{\times}20s,\;2{\times}60s,\;2{\times}120s,\;1{\times}180s\;and\;22{\times}300s$). following the 50 minute-scanning, subjects smoked a cigarette containing 1 mg of nicotine while in the scanner. Blood samples for the measurement of plasma nicotine level were collected at 0, 5, 10, 15, 20, 25, 30, 45, 60, and 90 minute after smoking. Regions for striatal structures were drawn on the coronal summed PET images guided with co-registered MRI. Binding potential, calculated as (striatal-cerebellar)/cerebellar activity, was measured under equilibrium condition at baseline and smoking session. Results: The mean decrease in binding potential of $[^{11}C]raclopride$ between the baseline and smoking in caudate head, anterior putamen and ventral striatum was 4.7%, 4.0% and 7.8%, respectively. This indicated the striatal dopamine release by smoking. Of these, the reduction in binding potential in the ventral striatum was significantly correlated with the cumulated plasma level of the nicotine (Spearman's rho=0.9, p=0.04). Conclusion: These data demonstrate that in vivo imaging with $[^{11}C]raclopride$ PET could measure nicotine-induced dopamine release in the human brain, which has a significant positive correlation with the amount or nicotine administered bt smoking.

• Journal of Technology Innovation
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• v.14 no.1
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• pp.31-66
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• 2006

The country with a relative abundance of human capital conducts relatively more R&D in the steady state than its partner. This country acquires the know-how to produce a relatively wider range of innovative goods. High technology comprises a large share of the national economy in the human-capital rich country and real output growth is faster. This prediction would seem to accord weakly with empirical observation of Korean economy.