• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.2
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• pp.49-58
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• 2008

Generally, steel corrosion occurs in concrete structures due to carbonation in down-town area and underground site and it propagates to degradation of structural performance. In general diagnosis and inspection, only carbonation depth in sound concrete is evaluated but unsound concrete such as joint and cracked area may occur easily in a concrete member due to construction process. In this study, field survey of carbonation for RC columns in down-town area is performed and carbonation depth in joint and cracked concrete including sound area is measured. Probability of durable failure with time is calculated through probability variables such as concrete cover depth and carbonation depth which are obtained from field survey. In addition, service life of the structures is predicted based on the intended probability of durable failure in domestic concrete specification. It is evaluated that in a RC column, various service life is predicted due to local condition and it is rapidly decreased with insufficient cover depth and growth of crack width. It is also evaluated that obtaining cover depth and quality of concrete is very important because the probability of durable failure is closely related with C.O.V. of cover depth.

• Journal of radiological science and technology
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• v.38 no.4
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• pp.403-410
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• 2015

The purpose of this study was to investigate the problems of a signal to noise ratio measurement using a two region measurement method that is conventionally used when using a multi-channel coil and a parallel imaging technique. As a research method, after calculating the standard SNR using a single channel head coil of which coil satisfies three preconditions when using a two region measurement method, we made comparisons and evaluations after calculating an SNR by using a two region measurement method of which method is problematic because it is used without considering the methods recommended by reputable organizations and the preconditions at the time of using a multi-channel coil and a parallel imaging technique. We found that a two region measurement method using a multi-channel coil and a parallel imaging technique shows the highest relative standard deviation, and thus shows a low degree of precision. In addition, we found out that the difference of SNR according to ROI location was very high, and thus a spatial noise distribution was not uniform. Also, 95% confidence interval through Blend-Altman plot is the widest, and thus the conformity degree with a two region measurement method using the standard single channel head coil is low. By directly comparing an AAPM method, which serves as a standard of a performance evaluation test of a magnetic resonance imaging device under the same image acquisition conditions, an NEMA method which can accurately determine the noise level in a signal region and the methods recommended by manufacturers of a magnetic resonance imaging device, there is a significance in that we quantitatively verified the inaccurate problems of a signal to noise ratio using a two region measurement method when using a multi-channel coil and a parallel imaging technique of which method does not satisfy the preconditions that researchers could overlook.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.

• Journal of the Korean Wood Science and Technology
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• v.45 no.5
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• pp.580-587
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• 2017

Woodfiber insulation board can be considered as a one of the key material for low energy consumption, comfortable and safety construction of residential space because of its eco-friendly and high thermal insulation performance. This study was carried out to investigate the formaldehyde (HCHO) total volatile organic compounds (TVOC) emission properties and combustion shapes by flame test of low density fiberboards (LDFs) prepared with different adhesives. HCHO TVOC emission and combustion properties of LDFs prepared by melamine urea formaldehyde (MUF), phenol formaldehyde (PF), emulsified methylene diphenyl diisocyanate (eMDI) and latex resin adhesives were measured by desiccator method, 20 L chamber method, and flame test, respectively. As results, LDFs manufactured by MUF, eMDI and latex resin adhesives satisfied the Super $E_0$ grade of HCHO emission performance except PF resin. Furthermore, TVOC emission of all LDFs were satisfied the Korean indoor air quality standard (below $400{\mu}g/m^2{\cdot}h$). Especially, LDF with eMDI resin adhesive showed the lowest HCHO and TVOC emissivity, that $0.14mg/{\ell}$, $12{\mu}g/m^2{\cdot}h$, respectively. However, eMDI emitted the small amount ($3{\mu}g/m^2{\cdot}h$) of toluene in VOC components. In the flame test, LDF with MUF resin adhesives showed the most favorable shape after flame test compare to LDFs prepared other adhesives. Based on HCHO and TVOC emission, and combustion shapes, MUF resin adhesive may be recommended to prepare LDF for insulation purpose.

• 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 the Korean Society of Fisheries and Ocean Technology
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• v.32 no.4
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• pp.363-371
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• 1996

The Steering and Sailing Rules of International Regulations for Preventing Collisions at Sea now in use direct the best aid - action to avoid collision by the stand - on vessel. But these rules do not refer to the safety relative distance between two vessels when she should take such action. In this paper, the author analyzed the ship's collision avoiding actions from the viewpoint of ship motions and worked out mathematical formulas to calculate the relative distances necessary for taking action to avoid collision. Figuring out the values of maneuvering indices through experiments of 11 actual ships of small, medium, large and mammoth size, the author applied these values to the calculating formulas and calculated the minimum relative distances. The main results are as follows: 1. It was confIrmed that the stand - on vessel should keep the greatest relative distance for taking best aid - action to avoid collision when the cross angle of course was $90^{\circ}$ and near it(70-$90^{\circ}$ ). 2. When the cross angle of course was $90^{\circ}$ , the minimum relative distance of small vessel(GT: 160-650tons) was found to be more than about 6.8 times of her own length, and those of medium(GT : 2,300-3,500tons), large(GT : 22,OOO-62,OOOtons) and mammoth(GT : 91,000-139,000tons) vessels were found to be more than about 9.0 times, about 5.4 times and about 6.8 times of their own lengths. 3. It was confIrmed that collision danger was greater when crossing angle was obtuse than in an acute angle, therefore greater relative distance was to be kept by the stand - on vessel for taking best aid - action to avoid collision in the case of the obtuse angle. 4. In every vessels, in the case of $90^{\circ}$ cross angle of course the safety minimum relative distance was found to be more than about 9.0 times of their own lengths.

• International Journal of Highway Engineering
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• v.12 no.2
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• pp.107-113
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• 2010

In this study, the relative effects of low-chloride deicier(LCD) and two other deicing agents on the scaling of concrete were conducted in a series of tests at laboratory accordance with the ASTM C 672. The solutions concentration of deicers tested included 1, 4, 10%. Tap water was used as control. The amount of scaling was evaluated gravimetrically. As test result of deicer solution types, when applied to 4% solutions, surface scaling of concrete after 56 freeze-thaw cycles was produced significantly as about 9 times on LCD solution, as about 18 times on $CaCl_2$ solution, and as about 33 times on NaCl solution comparing with tap water. As test result of deicer solution concentrations, relatively low concentrations (of the 4% by weight) of deicer were produced more surface scaling than higher concentrations (of the 10% by weight) or lower concentrations (of the 1% by weight) of deicer. It show that the damaging concentration is of the order of 3~4% for previous research result. It appears that the mechanism of surface scaling is primarily physical rather than chemical. Also, the effect of chloride deicier types, freeze-thaw cycling, and air contents on the performance of concrete was experimentally investigated. The results show that the concrete specimens subjected to freeze-thaw cycling scaled more severely in exposure to deicing salt than those in non-exposure to deicing salt, weight losses of the specimens tested in exposure to deicing salt were twice as much as those tested in non-exposure to deicing salt. Relative dynamic modulus of elasticity of concrete specimens decreased more quickly in exposure to deicing salt than in non-exposure to deicing salt. Also, relative dynamic modulus of elasticity of concrete specimens in exposure to sodium chloride deicing salt was decreased more quickly comparing with exposure to LCD salt. It is also shown that the chloride contents according to concrete specimen depths was more largely in exposure to LCD salt. When concrete specimen is exposed to chloride deicing salts and freeze-thaw cycling, performance degradation in the entrained air concrete(AE concrete) retarded more considerably comparing with non-entrained air concrete(Non-AE concrete).

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.203-207
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• 2010

Purpose: Hepatitis B is infection caused by Hepatitis B virus (HBV). Currently, there are several methods, Kits and equipments for conducting Hepatitis B test. Due to ununiformed methods, it would cause some differences. To manage these differences, it needs process evaluating function of test system and reagent using particular standard substance. The aim of this study is to investigate tendency of RIA method's reagent used in Asan Medical Center through comparing several other test reagents using national standard substance. Materials and Methods: The standard substance in National Institute of Food and Drug Safety Evaluation's biology medicine consists of 5 things, 4 antigens and 1 antibody. We tested reagents using A, B company's Kits according to each test method. All tests are measured repeatedly to obtain accurate results. Results: Test result of "HBs Ag Mixed titer Performance panel" is obtained match rate compared S/CO unit standard with RIA method and EIA 3 reagents, CIA 2 reagents is that company A's reagent is 94.4% (17/18), 83.3% (15/18), B is 88.9% (16/18), 77.8% (14/18). Test result of "HBs Ag Low titer Performance panel" is obtain that EIA 2 reagents is shown 7 posive results, CIA 3 reagents is 11, and RIA method's company A's reagent is 3, B is 2 of 13 in low panel. "HBV surface antigen 86.76 IU/vial" tested dilution. A is obtain positive results to 600 times(0.14 IU/mL), B is 300 times (0.29 IU/mL). Case of "HBV human immunoglobulin 95.45 IU/vial", A is shown positive result to 10,000 times (9.5 mIU/mL) and B is 4,000 times (24 mIU/mL). Test result of "HBs Ag Working Standards 0.02~11.52 IU/mL" is shown that Company A's kit concentration level was 0.38IU/mL, company B was 2.23 IU/mL and higher level of concentration was positive results. Conclusion: When comparing various test reagents and RIA method according to National Standard substances for Hepatitis B test, we recognized that there were no significant trends between reagents. For hepatitis B virus antigen-antibody titers even in parts of the test up to 600 times the antigen, antibodies to 10,000 times the maximum positive results could be obtained. Therefore, we confirmed that results from Asan Medical Center are performed smoothly by reagents and system for hepatitis B virus test.