• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.2
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• pp.79-83
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• 2018

Purpose $^{18}F-FDG$, which is commonly used in PET-CT examinations, is low in capacity and it is difficult to keep the amount of radioactivity busy when the specific activity is high, increasing the amount of space dose and radioactive contamination in the distribution room. Therefore, while evaluating the actual dose administered to patients during the manual dispense process, the medical institution intends to assess the usefulness of the auto dispenser by comparing the differences from the actual dose administered to the patient using the new automatic dispense. Materials and Methods From July 2016 to December 2016, 846 patients were manually administered by workers using $^{18}F-FDG$ and $^{18}F-FDG$ 906 patients were using auto dispenser from July 2017 to December 2017. Results Capacity administered to patients during the manual dispense averaged $35.41{\pm}27.79%$ compared to the recommended dose, and the auto dispenser process showed a small difference of $-2.15{\pm}3.99%$ compared to the recommended dose(p<0.05). Conclusion Working people did not have to touch radioactive medicines directly while they were busy in the auto dispenser, and because of the availability of other tasks far away, the time and distance to receive the exposure were also advantageous. It is believed that future use by many medical institutions will not only reduce the dose to patients but also help reduce the exposure dose to workers.

• Journal of Wetlands Research
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• v.21 no.2
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• pp.140-146
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• 2019

In order to manage non-point sources, the paradigm of the system should be changed so that the management of non-point sources will be systematized from the beginning of the use and development of the land. It is necessary to change the method of national subsidy support and poeration plan for the non-point source management area. In order to increase the effectiveness of the non-point source reduction project, it is necessary to provide a minimum support ratio and to provide additional support according to the performance of the local government. A new system should be established to evaluate the performance of non-point source reduction projects and to monitor the operational effectiveness. It is necessary to establish the related rules that can lead the local government to take responsible administration so that the local governments faithfully carry out the non-point source reduction project and achieve the planned achievement and become the sustainable maintenance. Alternative solutions are needed, such as problems with the use of $100{\mu}m$ filter in automatic sampling and analysis, timely acquisition of water sampling and analysis during rainfall, and effective management of non-point sources network operation management. As an alternative, it is necessary to consider improving the performance of sampling and analysis equipment, and operate the base station. In addition, countermeasures are needed if the amount of pollutant reduction according to the non-point source reduction facility promoted by the national subsidy is required to be used as the development load of the TMDLs. As an alternative, it is possible to consider supporting incentive type of part of the maintenance cost of the non-point source reduction facility depending on the amount of pollutants reduction.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1615-1633
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• 2023

Sargassum horneri is one of the floating algae in the sea, which breeds in large quantities in the Yellow Sea and East China Sea and then flows into the coast of Republic of Korea, causing various problems such as destroying the environment and damaging fish farms. In order to effectively prevent damage and preserve the coastal environment, the development of Sargassum horneri detection algorithms using satellite-based remote sensing technology has been actively developed. However, incorrect detection information causes an increase in the moving distance of ships collecting Sargassum horneri and confusion in the response of related local governments or institutions,so it is very important to minimize false detections when producing Sargassum horneri spatial information. This study applied technology to automatically remove false detection results using the GOCI-II-based Sargassum horneri detection algorithm of the National Ocean Satellite Center (NOSC) of the Korea Hydrographic and Oceanography Agency (KHOA). Based on the results of analyzing the causes of major false detection results, it includes a process of removing linear and sporadic false detections and green algae that occurs in large quantities along the coast of China in spring and summer by considering them as false detections. The technology to automatically remove false detection was applied to the dates when Sargassum horneri occurred from February 24 to June 25, 2022. Visual assessment results were generated using mid-resolution satellite images, qualitative and quantitative evaluations were performed. Linear false detection results were completely removed, and most of the sporadic and green algae false detection results that affected the distribution were removed. Even after the automatic false detection removal process, it was possible to confirm the distribution area of Sargassum horneri compared to the visual assessment results, and the accuracy and precision calculated using the binary classification model averaged 97.73% and 95.4%, respectively. Recall value was very low at 29.03%, which is presumed to be due to the effect of Sargassum horneri movement due to the observation time discrepancy between GOCI-II and mid-resolution satellite images, differences in spatial resolution, location deviation by orthocorrection, and cloud masking. The results of this study's removal of false detections of Sargassum horneri can determine the spatial distribution status in near real-time, but there are limitations in accurately estimating biomass. Therefore, continuous research on upgrading the Sargassum horneri monitoring system must be conducted to use it as data for establishing future Sargassum horneri response plans.

• Journal of Sericultural and Entomological Science
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• v.16 no.2
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• pp.77-98
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• 1974

This study was carried out ill an attempt to investigate the properties and activities of sericin obtained from silk cocoon shells in silk reeling water through various instrumental analyses. In addition, the effects of the characteristics in sericin solubility on the reeling process and silk qualities were also studied on the basis of tile above conditions. The results obtained are as follows： I. The sericin properties and activities through various analytical instruments. 1. The water solubility of each amino acid such as serine, glycine. glutamic and aspartic acids against the pH modified water by using automatic amino acid analyzer, showed the lowest solubility at PH 5, but it increased according to drifting toward the alkalinity. 2. When the obtained sericin particles by water pH variation were observed with the electronic microscope, it was found for the sericin Particles to expand in the alkaline regions. 3. The IR spectrum results showed the differences among the pH modified sericin solutions at the range of 2,100cm-1 and 1.890cm-1 of wave number. 4. The existence of sericin with in silk fabrics made differences in the X-ray interference intencity, that is, the non-degummed fabrics had the interference peak at 2$\theta$=14$^{\circ}$, 17$^{\circ}$, 24$^{\circ}$, and the degummed ones had it at 2$\theta$=17$^{\circ}$, 18$^{\circ}$, 20$^{\circ}$, 23$^{\circ}$, 26$^{\circ}$. II. The results of sericin solubility for silk reeling process. 1. The sericin solubility and swelling had a tendency to increase up with high M-alkalinity and pH value of water. But in case of acidity. water hardness and concentration of ethylene glycol were high, the sericin solubility and swelling were decreased. 2. With the filature experiments, the best conditions of filature orator are summarized as fellows ; a. pH; 6.9${\pm}$0.2 d. acidity; below10ppm b. total hardness; 55：5ppm e. Fe ion; none c. M-alkalinity; 40${\pm}$10ppm

• Journal of Gastric Cancer
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• v.5 no.4 s.20
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• pp.228-237
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• 2005

Purpose: We investigated the impacts of the methylation states of the P16 and the hMLH1 genes on pathogenesis and genetic expression of stomach cancer and their relationships with Helicobater pylori infection, and with other clinico-pathologic factors. Material and Methods: In our study, to detect protein expression and methylation status of the P16 and the hMLH1 genes in 100 advanced gastric adenocarcinomas, used immunohistochemical staining and methylation-specific PCR (MSP) and direct automatic genetic sequencing analysis. Results: Methylation of the P16 gene was observed in 19 out of 100 cases (19%) and in the 18 of those cases (94.7%) loss of protein expression was seen. We were sble to show that loss of P16 gene expression was related to methylation of the P16 gene (kappa coefficient=0.317, p=0.0011). Methylation of the hMLH1 gene was observed in 27 cases (27%), and in 24 cases of those 27 cases (88.8%), loss of protein expression was seen, which suggested that loss of protein expression in the hMLH1 gene is related to methylation of hMLH1 gene (kappa coefficient=0.675, P<0.0001). Also methylation of the hMLH1 gene was related to age, size of the mass, and lauren's classification. Conclusion: We found that methylation of DNA plays an important role in inactivation of the P16 and the hMLH1 genes. The methylation of the hMLH1 genes is significantly related to age, size of the mass, and lauren's classification.

• Progress in Medical Physics
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• v.24 no.2
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• pp.85-91
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• 2013

At present, megavoltage computed tomography (MVCT) is the only method used to correct the position of tomotherapy patients. MVCT produces extra radiation, in addition to the radiation used for treatment, and repositioning also takes up much of the total treatment time. To address these issues, we suggest the use of a video image-guided setup (VIGS) system for correcting the position of tomotherapy patients. We developed an in-house program to correct the exact position of patients using two orthogonal images obtained from two video cameras installed at $90^{\circ}$ and fastened inside the tomotherapy gantry. The system is programmed to make automatic registration possible with the use of edge detection of the user-defined region of interest (ROI). A head-and-neck patient is then simulated using a humanoid phantom. After taking the computed tomography (CT) image, tomotherapy planning is performed. To mimic a clinical treatment course, we used an immobilization device to position the phantom on the tomotherapy couch and, using MVCT, corrected its position to match the one captured when the treatment was planned. Video images of the corrected position were used as reference images for the VIGS system. First, the position was repeatedly corrected 10 times using MVCT, and based on the saved reference video image, the patient position was then corrected 10 times using the VIGS method. Thereafter, the results of the two correction methods were compared. The results demonstrated that patient positioning using a video-imaging method ($41.7{\pm}11.2$ seconds) significantly reduces the overall time of the MVCT method ($420{\pm}6$ seconds) (p<0.05). However, there was no meaningful difference in accuracy between the two methods (x=0.11 mm, y=0.27 mm, z=0.58 mm, p>0.05). Because VIGS provides a more accurate result and reduces the required time, compared with the MVCT method, it is expected to manage the overall tomotherapy treatment process more efficiently.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.306-315
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• 2021

This study analyzed the effects of light-shielding curtains and halogens on spectrum when acquiring hyperspectral images in a greenhouse. The image data of tarp (1.4*1.4 m, 12%) with 30 degrees of angles was achieved three times with four conditions depending on 14 heights using the automatic image acquisition system installed in the greenhouse at the department of Southern Area of National Institute of Crop Science. When the image was acquired without both a light-shielding curtain and halogen lamp, there was a difference in spectral tendencies between direct light and shadow parts on the base of 550 nm. The average coefficient of variation (CV) for direct light and shadow parts was 1.8% and 4.2%, respective. The average CV value was increased to 12.5% regardless of shadows. When the image was acquired only used a halogen lamp, the average CV of the direct light and shadow parts were 2 .6% and 10.6%, and the width of change on the spectrum was increased because the amount of halogen light was changed depending on the height. In the case of shading curtains only used, the average CV was 1.6%, and the distinction between direct light and shadows disappeared. When the image was acquired using a shading curtain and halogen lamp, the average CV was increased to 10.2% because the amount of halogen light differed depending on the height. When the average CV depending on the height was calculated using halogen and light-shielding curtains, it was 1.4% at 0.1m and 1.9% at 0.2 m, 2 .6% at 0.3m, and 3.3% at 0.4m of height, respectively. When hyperspectral imagery is acquired, it is necessary to use a shading curtain to minimize the effect of shadows. Moreover, in case of supplementary lighting by using a halogen lamp, it is judged to be effective when the size of the object is less than 0.2 m and the distance between the object and the housing is kept constant.

• The Korean Journal of Elementary Counseling
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• v.7 no.2
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• pp.101-115
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• 2008

The purposes of this study were to consist an anger control program in order to help children confirm and modify their cognitive errors in peer anger-provoking situations(Lee Mi-gyeong, 2006), that and to examine the effects of this program on anger-provoking experience, anger controllability and peer relationship. The cognitive-behavioral anger control program was consisted of 16 sessions. The focus of the program were placed on perceiving angry feelings, confirming automatic thinking and cognitive errors and acquiring how to correct the cognitive errors(1st-10th sessions), and checking cognitive errors in 13 anger-provoking situations and practicing way to correct the errors(11th-15th sessions). To examine the effects of the program, 10 children who had a lot of anger-provoking experiences, and were poor at anger control and faced difficulties with peer relationship were selected. The cognitive-behavioral anger control program was implemented for eight weeks, twice a week, 40 minutes each. The collected data were analysed by the ANOVA method using the SPSS and Kwakstat(Kwak Ho-wan, 1993). What cognitive errors children made and how they modified the errors during the program were checked. The findings of the study were as follows: The cognitive-behavioral anger control program served to cut down on the anger-provoking experiences, to improve their anger controllability, to boost their peer relationship, and that effect lasted till six weeks later. And the cognitive errors they made during the program were in the order as follows: stating the oughtness of their behavior, followed by naming, seeing everything in black and white, emotional judgment, mind reading, linking the situation to themselves, overgeneralizing, and hasty conclusion. The ways to correct the cognitive errors were in the order as follows: putting oneself in another's place, explaining in a different manner, looking for proof, thinking of it is so difficult to indure, thinging of there is moral to it, and thinking of how angry after passing time.

• 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.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.2
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• pp.127-136
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• 2022

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMY_normal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 m/s). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).