• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Gastric Cancer
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• v.8 no.1
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• pp.27-34
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• 2008

Purpose: Many recent studies have reported on the feasibility and usefulness of laparoscopy assisted distal gastrectomy (LADG) for treating early gastric cancer. On the other hand, there has been few reports about laparoscopy assisted total gastrectomy (LATG) because upper located gastric cancer is relatively rare and the surgical technique is more difficult than that for LADG, We now present our procedure and results of performing LATG for the gastric cancer located in the upper or middle portion of the stomach. Materials and Methods: From Jan 2005 to Sep 2007, 96 patients underwent LATG by four surgeons at the Asan Medical Center, Seoul, Korea. Among them, 48 consecutive patients who were operated on by asingle surgeon were analyzed with respect to the clinicopathological features, the surgical results and the postoperative courses with using the prospectively collected laparoscopy surgery data. Results: There was no conversion to open surgery during LATG. For all the reconstructions, Roux-en Y esophago-jejunostomy and D1+beta lymphadenectomy were the standard procedures. The mean operation time was $212{\pm}67$ minutes. The mean total number of retrieved lymph nodes was $28.9{\pm}10.54$ (range: $12{\sim}64$) and all the patients had a clear proximal resection margin in their final pathologic reports. The mean time to passing gas, first oral feeding and discharge from the hospital was 2.98, 3.67 and 7.08 days, respectively. There were 5 surgical complications and 2 non-surgical complications for 5 (10.4%) patients, and there was no mortality. None of the patients needed operation because of complications and they recovered with conservative treatments. The mean operation time remained constant after 20 cases and so a learning curve was present. The morbidity rate was not different between the two periods, but the postoperative course was significantly better after the learning curve. Analysis of the factors contributing to the postoperative morbidity, with using logistic regression analysis, showed that the 8MI is the only contributing factor forpostoperative complications (P=0.029, HR=2.513, 95% CI=1.097-5.755). Conclusions: LATG with regional lymph node dissection for upper and middle early gastric cancer is considered to be a safe, feasible method that showed an excellent postoperative course and acceptable morbidity. BMI should be considered in the patient selection at the beginning period because of the impact of the BMI on the postoperative morbidity.

• School Mathematics
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• v.19 no.3
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• pp.571-591
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• 2017

The purpose of this study is to understand the characteristics of mathematical discourse about the length in the class that learns the length of the curve defined by definite integral. For this purpose, this study examined the discourse about length by paying attention to the usage of the word 'length' in the class participants based on the communicative approach. As a result of the research, it was confirmed that the word 'length' is used in three usages - colloquial, operational, and structural usage - in the process of communicating with the discourse participants. Particularly, each participant did not recognize the difference even though they used different usage words, and this resulted in ineffective communication. This study emphasizes the fact that the difference in usage of words used by participants reduces the effectiveness of communication. However, if discourse participants pay attention to the differences of these usages and recognize that there are different discourses, this study suggests that meta - level learning can be possible by overcoming communication discontinuities and resolving conflicts.

• Communications of Mathematical Education
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• v.23 no.3
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• pp.625-642
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• 2009

In the school mathematics the concept of tangent is introduced in several steps in suitable contexts. Students are required to reflect and revise their concepts of tangent in order to apply the improved concept to wider range of contexts. In this paper we consider the tangent as the optimal linear approximation to a curve at a given point and make three discussions on pedagogical aspects of it. First, it provides a method of finding roots of real numbers which can be used as an application of tangent. This may help students improve their affective variables such as interest, attitude, motivation about the learning of tangent. Second, this concept reflects the modern point of view of tangent, the linear approximation of nonlinear problems. Third, it gives precise meaning of two tangent lines appearing two sides of a cusp point of a curve.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.21-28
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• 2019

Side effects of drugs mean harmful and unintended effects resulting from drugs used to prevent, diagnose, or treat diseases. These side effects can lead to patients' death and are the main causes of drug developmental failures. Thus, various methods have been tried to identify side effects. These can be divided into biological and systems biology approaches. In this study, we use systems biology approach and focus on using various phenotypic information in addition to the chemical structure and target proteins. First, we collect datasets that are used in this study, and calculate similarities individually. Second, we generate a set of features using the similarities for each drug-side effect pair. Finally, we confirm the results by AUC(Area Under the ROC Curve), and showed the significance of this study through a comparison experiment.

• Steel and Composite Structures
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• v.52 no.1
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• pp.31-43
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• 2024

A very widely used analytical method (mathematical model), mentioned in Eurocode 3, to examine the connections' bending behavior is the component-based method that has certain weak points shown in the plastic behavior part of the moment-rotation curves. In the component method available in Eurocode 3, for simplicity, the effect of strain hardening is omitted, and the bending behavior of the connection is modeled with the help of a two-line diagram. To make the component method more efficient and reliable, this research proposed its advanced version, wherein the plastic part of the diagram was developed beyond the guidelines of the mentioned Regulation, implemented to connect the end plate, and verified with the moment-rotation curves found from the laboratory model and the finite element method in ABAQUS. The findings indicated that the advanced component method (the method developed in this research) could predict the plastic part of the moment-rotation curve as well as the conventional component-based method in Eurocode 3. The comparison between the laboratory model and the outputs of the conventional and advanced component methods, as well as the outputs of the finite elements approach using ABAQUS, revealed a different percentage in the ultimate moment for bolt-extended end-plate connections. Specifically, the difference percentages were -31.56%, 2.46%, and 9.84%, respectively. Another aim of this research was to determine the optimal dimensions of the end plate joint to reduce costs without letting the mechanical constraints related to the bending moment and the resulting initial stiffness, are not compromised as well as the safety and integrity of the connection. In this research, the thickness and dimensions of the end plate and the location and diameter of the bolts were the design variables, which were optimized using Particle Swarm Optimization (PSO), Snake Optimization (SO), and Teaching Learning-Based Optimization (TLBO) to minimization the connection cost of the end plate connection. According to the results, the TLBO method yielded better solutions than others, reducing the connection costs from 43.97 to 17.45€ (60.3%), which shows the method's proper efficiency.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.145-156
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• 2023

In this study, Fourier-transform near-infrared (FT-NIR) spectra of Korean red pine seeds stored at -18℃ and 4℃ for 18 years were analyzed. To develop seed-germination prediction models, the performance of seven machine learning methods, namely XGBoost, Boosted Tree, Bootstrap Forest, Neural Networks, Decision Tree, Support Vector Machine, PLS-DA, were compared. The predictive performance, assessed by accuracy, misclassification, and area under the curve (0.9722, 0.0278, and 0.9735 for XGBoost, and 0.9653, 0.0347, and 0.9647 for Boosted Tree), was better for the XGBoost and decision tree models when compared with other models. The 54 wave-number variables of the two models were of high relative importance in seed-germination prediction and were grouped into six spectral ranges (811~1,088 nm, 1,137~1,273 nm, 1,336~1,453 nm, 1,666~1,671 nm, 1,879~2,045 nm, and 2,058~2,409 nm) for aromatic amino acids, cellulose, lignin, starch, fatty acids, and moisture, respectively. Use of the NIR spectral data and two machine learning models developed in this study gave >96% accuracy for the prediction of pine-seed germination after long-term storage, indicating this approach could be useful for non-destructive viability testing of stored seed genetic resources.

• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.125-137
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• 2023

This study analyzed the types of knowledge building discourse and knowledge building processes in small group learning using augmented reality. Eight 8th grade students took classes using augmented reality in solubility, boiling and melting points. These classes were carried out twice and all the classes were videotaped and recorded. Every student participated in a semi-structured interview. In the types of knowledge building discourse, the proportion of knowledge sharing and knowledge construction was similar. Beneath the knowledge sharing, the proportion of introductory level discussion was higher than identifying key elements of augmented reality. Recalling existing knowledge rarely appeared. Under the knowledge construction, the proportion of advanced level discussion was the highest and the proportion of sharing and critiquing ideas at a different level and efforts to rise above current levels of explanation was similar. The introductory level discussion and identifying key elements of augmented reality were developed into efforts to rise above current levels of explanation and sharing and critiquing ideas at a different level. Visualized results of knowledge building processes showed all the students' graph drew an upward curve, though cumulative number of impact value was different by each student. As a result of the study, effective ways of improving small group learning using augmented reality are discussed.