• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.991-1001
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• 2020

Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.93-106
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• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.

• Therapeutic Science for Rehabilitation
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• v.10 no.1
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• pp.105-114
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• 2021

Objective : The purpose of this study was to identify and compare the sex differences in episodic memory and spatial cognition in healthy young adults. Methods : Forty-eight undergraduates (male=24, female=24) were assessed for sex differences using the visual stimuli episodic memory task and the virtual reality-based spatial cognition task. The accuracy rates (%) for the What, Where, and When conditions of the episodic memory task and the average distance error (cm) for 10 trials of the spatial cognition task were analyzed. Results : There were no significant sex differences between the three conditions. The male participants showed a significantly higher performance on the spatial cognition task than the female participants Conclusion : The results of this study indicated that the sex differences in episodic memory could be altered by the test methods. Although episodic memory and spatial cognition mainly depend on the hippocampus, the sex-related differences between the two functions were inconsistent, suggesting that these two functions are independent.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.98-106
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• 2021

The paper deals with comparative study on sensitivity analysis using various methods regarding to design of experiments for structure design of an active type DSF (Deck support frame) that was developed for float-over installation of offshore plant. The thickness sizing variables of structure member of the active type DSF were considered the design factors. The output responses were defined from the weight and the strength performances. Various methods such as orthogonal array design, Box-Behnken design, and Latin hypercube design were applied to the comparative study. In order to evaluate the approximation performance of the design space exploration according to the design of experiments, response surface method was generated for each design of experiment, and the accuracy characteristics of the approximation were reviewed. The design enhancement results such as numerical costs, weight minimization, etc. via the design of experiment methods were compared to the results of the best design. The orthogonal array design method represented the most improved results for the structure design of the active type DSF.

• Korean Journal of Food Science and Technology
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• v.52 no.6
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• pp.580-586
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• 2020

Fishery resources have been widely consumed as protein- and vitamin-rich food sources in the Korean diet. However, information regarding their vitamin levels is extremely limited. In this study, trienzyme-Lactobacillus casei method was validated and used to determine the folate contents in fishery foods. The trienzyme-L. casei method for folate analysis showed excellent accuracy (85.2 to 95.3% recovery) and precision (repeatability 1.4% RSD and reproducibility 2.4% RSD). Folate contents of 20 fish foods (4 fish, 3 crustaceans, 3 sea algae, 3 cephalopods, 4 shellfish, and 3 others) ranged from 1.75 to 97.98 ㎍/100 g. Furthermore, we found that the folate content in seaweed fusiforme was the highest, followed by gulfweed (69.73 ㎍/100 g). Folate analysis using the trienzyme-L. casei method was determined excellent based on the z-score of -0.3 in the Food Analysis Performance Assessment Scheme test. Analytical and method validation data generated in this study could be used to update the national food composition table on vitamin B9 in Korean fishery resources.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.503-514
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• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• Journal of the Korean Society of Radiology
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• v.15 no.3
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• pp.307-312
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• 2021

In nuclear medicine, radioactive isotope tracers are administered to the human body to obtain and evaluate disease morphological information and biological function information. Dose calibrator is a device used to measure the radioactivity of a single nuclide in medical institutions. Administration of the correct dose to the human body acts as an important factor in diagnosis and treatment, and measurement through a dose calibrator before administration is the most important factor. Dose calibrator performs daily quality control after installation in each medical institution. Quality control is a means of guaranteeing quality control after installation, and is essential for improving the quality of treatment and promoting patient safety. Therefore, accurate and standardized performance evaluation methods should be established. In this study, 3D printing was used for quantitative evaluation of quality control by increasing the accuracy and standardization of quality control. When the 3D printer was installed and reproducibility was tested, the error range of the expected value and reading value decreased by 0.302% in the F-18 nuclide and 0.09% in the ^99mTc-pertechnate nuclide than when the 3D printer was installed. The error rate for other nuclides was also found to have a low error rate for reproducibility tests when 3D printing was installed.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.35-43
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• 2021

Citrus quality is generally determined by its sugar content and acidity. In particular, sugar content is a very important factor because it determines the taste of citrus. Currently, the most commonly used method of measuring citrus sugar content in farms is a portable juiced sugar meter and a non-destructive sugar meter. This method can be easily measured by individuals, but the accuracy of the sugar content is inferior to that of the citrus NongHyup official machine. In particular, there is an error difference of 0.5 Brix or more, which is still insufficient for use in the field. Therefore, in this paper, we propose an AI model that predicts the citrus sugar content of unmeasured days within the error range of 0.5 Brix or less based on the previously collected citrus sugar content and meteorological data (average temperature, humidity, rainfall, solar radiation, and average wind speed). In addition, it was confirmed that the prediction model proposed through performance evaluation had an mean absolute error of 0.1154 for Seongsan area and 0.1983 for the Hawon area in Jeju Island. Lastly, the proposed model supports an error difference of less than 0.5 Brix and is a technology that supports predictive measurement, so it is expected that its usability will be highly progressive.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.161-171
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• 2021

This paper presents the findings of a comparative study on minimum weight design and sensitivity evaluation using different experimental design methods for the structural design of an active-type deck support frame (DSF) developed for the float-over installation of an of shore plant topside. The thickness sizing variables of the structural members of a passive-type DSF were considered the design factors, and the output responses were defined using the weight and strength performances. The design of the experimental methods applied in the comparative study of the minimum weight design and the sensitivity evaluation were the orthogonal array design, Box- Behnken design, and Latin hypercube design. A response surface method was generated for each design of the experiment to evaluate the approximation performance of the design space exploration according to the experimental design, and the accuracy characteristics of the approximation were reviewed. Regarding the minimum weight design, the design results, such as numerical costs and weight minimization, of the experimental design for the best design case, were evaluated. The Box- Behnken design method showed the optimum design results for the structural design of the passive-type DSF.