• The Journal of Engineering Geology
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• v.31 no.3
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• pp.381-393
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• 2021

Physical, mechanical, hydraulic, and geophysical tests were applied to validate methods of inspecting the effectiveness of grouting on an agricultural reservoir dam. Data obtained from series of in situ and laboratory tests considered four stages: before grouting; during grouting; immediately after grouting; and after aging the grouting for 28 days. The results of SPT and triaxial tests, including the unit weight, compressive strength, friction angle, cohesion, and N-value, indicated the extent of ground improvement with respect to grout injection. However, they sometimes contained errors caused by ground heterogeneity. Hydraulic conductivity obtained from in situ variable head permeability testing is most suitable for identifying the effectiveness of grouting because the impermeability of the ground increased immediately after grouting. Electric resistivity surveying is useful for finding a saturated zone and a seepage pathway, and multichannel analysis of surface waves (MASW) is suitable for analyzing the effectiveness of grouting, as elastic velocity increases distinctly after grouting injection. MASW also allows calculation from the P- and S- wave velocities of dynamic properties (e.g., dynamic elastic modulus and dynamic Poisson's ratio), which can be used in the seismic design of dam structures.

• Journal of Life Science
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• v.30 no.12
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• pp.1101-1108
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• 2020

Tartary buckwheat is a grain with many flavonoids, such as rutin, quercetin, kaempferol, and myricetin. This study aimed to optimize extraction conditions to maximize the rutin, quercetin, and myricetin contents of tartary buckwheat sprout extracts using response surface methodology. A BoxBehnken design containing 15 experiments was employed to evaluate the effects of extraction conditions, such as temperature (X1, 50~70℃), extraction time (X2, 5~9 hr), and ethanol concentration (X3, 60~90%). The coefficients of determination (R2) for all the dependent variables (extraction temperature, extraction time, and extraction ethanol concentration) were determined to be over 0.95, indicating significance. The p-value of the model in lack of fit was over 0.1 than means, indicating that the model was well predicted. The optimal extraction conditions for rutin, quercetin, and myricetin contents were obtained at X1 = 51.03, X2 = 6.62, and X3 = 69.16, respectively. Under these optimal conditions, the predicted rutin, quercetin, and myricetin contents were 808.467 ㎍/ml, 193.296 ㎍/ml, and 37.360 ㎍/ml, respectively. For the validation of the model, ten experiments were performed and the experimental rutin and quercetin contents were measured at 802.84±8.49 ㎍/ml, 193.76±2.80 ㎍/ml, and 34.84±0.43 ㎍/ml, respectively. The experimental rutin and quercetin contents were similar to the predicted contents, but the experimental myricetin content was lower than predicted.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1323-1337
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• 2020

GEO-KOMPSAT-2A AMI (Advanced Meteorological Imager) radiometric calibration evaluation is an essential element not only for functional and performance verification of the payload but for the quality of the sensor data. AMI instrument consists of six reflective channels and ten thermal infrared ones. One of the key parameters representing radiometric properties of the sensor is a SNR (Signal-to-Noise Ratio) for the reflective channels and a NEdT (Noise Equivalent delta Temperature) for the IR ones respectively. Other important radiometric calibration parameters are a dynamic range and a gain value related with the responsivity of detectors. To verify major radiometric calibration performance of AMI, an offline radiometric evaluation tool was developed separately with a real-time AMI data processing system. Using the evaluation tool, validation activities were carried out during the GEO-KOMPSAT-2A In-Orbit Test period. The results from the evaluation tool were cross checked with those of the HARRIS, which is the AMI payload vendor. AMI radiometric evaluation activities were conducted through three phases for both sides (Side 1 and Side 2) of AMI payload. Results showed that performances of the key radiometric properties were outstanding with respect to the radiometric requirements of the payload. The effectiveness of the evaluation tool was verified as well.

• Korean Journal of Culture and Social Issue
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• v.26 no.3
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• pp.195-220
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• 2020

The purpose of this study was to validate the Korean version of the post-traumatic growth inventory-expanded(K-PTGI-X), which has been widely used to assess posttraumatic growth. The PTGI-X is a measure of the addition of the items to measure the existential growth as the need for modification to the factors of the 'increase of spiritual interest' in the existing PTGI is suggested. We examined the factor structure, reliability, and validity of a Korean version of the PTGI-X among 625 Korean adults who have experienced trauma events. First, EFA confirmed the appropriate PTGI-X factor structure and found that the 4-factor structure was the most appropriate. Next, as a result of CFA, it was found that the model to which correlation between items was added to the 4-factor model was good. Next, testing internal consistency, CR, and AVE of the K-PTGI-X showed that PTGI-X's items are reliable. Also, we tested the concurrent validity and discriminative validity. All of the K-PTGI-X scales significantly correlated with measures of deliberate rumination and core-belief except for the intrusive rumination. Finally, to add an understanding of K-PTGI-X, t-test according to demographic variables was conducted. Recommendations for future research and implications were discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.341-352
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• 2022

Medical image segmentation is the most important task in radiation therapy. Especially, when segmenting medical images, the liver is one of the most difficult organs to segment because it has various shapes and is close to other organs. Therefore, automatic segmentation of the liver in computed tomography (CT) images is a difficult task. Since tumors also have low contrast in surrounding tissues, and the shape, location, size, and number of tumors vary from patient to patient, accurate tumor segmentation takes a long time. In this study, we propose a method algorithm for automatically segmenting the liver and tumor for this purpose. As an advantage of setting the boundaries of the tumor, the liver and tumor were automatically segmented from the CT image using the 2D CoordConv DeepLab V3+ model using the CoordConv layer. For tumors, only cropped liver images were used to improve accuracy. Additionally, to increase the segmentation accuracy, augmentation, preprocess, loss function, and hyperparameter were used to find optimal values. We compared the CoordConv DeepLab v3+ model using the CoordConv layer and the DeepLab V3+ model without the CoordConv layer to determine whether they affected the segmentation accuracy. The data sets used included 131 hepatic tumor segmentation (LiTS) challenge data sets (100 train sets, 16 validation sets, and 15 test sets). Additional learned data were tested using 15 clinical data from Seoul St. Mary's Hospital. The evaluation was compared with the study results learned with a two-dimensional deep learning-based model. Dice values without the CoordConv layer achieved 0.965 ± 0.01 for liver segmentation and 0.925 ± 0.04 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.927 ± 0.02 for liver division and 0.903 ± 0.05 for tumor division. The dice values using the CoordConv layer achieved 0.989 ± 0.02 for liver segmentation and 0.937 ± 0.07 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.944 ± 0.02 for liver division and 0.916 ± 0.18 for tumor division. The use of CoordConv layers improves the segmentation accuracy. The highest of the most recently published values were 0.960 and 0.749 for liver and tumor division, respectively. However, better performance was achieved with 0.989 and 0.937 results for liver and tumor, which would have been used with the algorithm proposed in this study. The algorithm proposed in this study can play a useful role in treatment planning by improving contouring accuracy and reducing time when segmentation evaluation of liver and tumor is performed. And accurate identification of liver anatomy in medical imaging applications, such as surgical planning, as well as radiotherapy, which can leverage the findings of this study, can help clinical evaluation of the risks and benefits of liver intervention.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.485-496
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• 2022

This study is for the software implementation to generate atmospheric and surface reflectance products from RapidEye satellite imagery. The software is an extension based on Orfeo Toolbox (OTB) and an open-source remote sensing software including calibration modules which use an absolute atmospheric correction algorithm. In order to verify the performance of the program, the accuracy of the product was validated by a test image on the Radiometric Calibration Network (RadCalNet) site. In addition, the accuracy of the surface reflectance product generated from the KOMPSAT-3A image, the surface reflectance of Landsat Analysis Ready Data (ARD) of the same site, and near acquisition date were compared with RapidEye-based one. At the same time, a comparative study was carried out with the processing results using QUick Atmospheric Correction (QUAC) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) tool supported by a commercial tool for the same image. Similar to the KOMPSAT-3A-based surface reflectance product, the results obtained from RapidEye Extension showed accuracy of agreement level within 5%, compared with RadCalNet data. They also showed better accuracy in all band images than the results using QUAC or FLAASH tool. As the importance of the Red-Edge band in agriculture, forests, and the environment applications is being emphasized, it is expected that the utilization of the surface reflectance products of RapidEye images produced using this program will also increase.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.261-269
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• 2021

Without any validation of the incompressible assumption, most of previous studies on cavitation flow and its noise have utilized numerical methods based on the incompressible Reynolds Average Navier-Stokes (RANS) equations because of advantage of its efficiency. In this study, to investigate the effects of the flow compressibility on the Tip Vortex Cavitation (TVC) flow and noise, both the incompressible and compressible simulations are performed to simulate the TVC flow, and the Ffowcs Williams and Hawkings (FW-H) integral equation is utilized to predict the TVC noise. The DARPA Suboff submarine body with an underwater propeller of a skew angle of 17 degree is targeted to account for the effects of upstream disturbance. The computation domain is set to be same as the test-section of the large cavitation tunnel in Korea Research Institute of Ships and Ocean Engineering to compare the prediction results with the measured ones. To predict the TVC accurately, the Delayed Detached Eddy Simulation (DDES) technique is used in combination with the adaptive grid techniques. The acoustic spectrum obtained using the compressible flow solver shows closer agreement with the measured one.