• Journal of Information Processing Systems
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• v.10 no.2
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• pp.314-333
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• 2014

When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two-class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction-oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.6
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• pp.675-687
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• 2009

In general, the forming process of the curved hull plates consists of sub tasks, such as roll bending, line heating, and triangle heating. In order to complement the automated curved hull forming system, it is necessary to develop an algorithm to classify the curved hull plates of a ship into standard shapes with respect to the techniques of forming task, such as the roll bending, the line heating, and the triangle heating. In this paper, the curved hull plates are classified by four standard shapes and the combination of them, or saddle, convex, flat, cylindrical shape, and the combination of them, that are related to the forming tasks necessary to form the shapes. In preprocessing, the Gaussian curvature and the mean curvature at the mid-point of a mesh of modeling surface by Coon's patch are calculated. Then the nearest neighbor method to classify the input plate type is applied. Tests to verify the developed algorithm with sample plates of a real ship data have been performed.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.3
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• pp.281-292
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• 2021

With the development of deep learning technology, researchers and technicians keep attempting to apply deep learning in various industrial and academic fields, including the defense. Most of these attempts assume that the data are balanced. In reality, since lots of the data are imbalanced, the classifier is not properly built and the model's performance can be low. Therefore, this study proposes cost-sensitive learning as a solution to the imbalance data problem of image classification in the defense field. In the proposed model, cost-sensitive learning is a method of giving a high weight on the cost function of a minority class. The results of cost-sensitive based model shows the test F1-score is higher when cost-sensitive learning is applied than general learning's through 160 experiments using submarine/non-submarine dataset and warship/non-warship dataset. Furthermore, statistical tests are conducted and the results are shown significantly.

• Journal of radiological science and technology
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• v.45 no.4
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• pp.341-346
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• 2022

The color overlay pattern of thyroid shear wave elastography applied in this study distinguishes benign and malignant nodules based on the optimal cut-off value of 74.2 kPa. From august 2021 to september 2021, thyroid ultrasound and elastography were performed on 57 patients with thyroid lesions using an ultrasound device RS85 prestige (Samsung Medison, Korea) and a 2-14 MHz linear transducer. In addition, the results of classification by K-TIRADS for each thyroid nodule and the results of classification by color overlay pattern according to the kPa value of acoustic ultrasound were compared and analyzed. In the color overlay pattern, the results classified as 40 people from dark blue to light blue and 17 people from green to red were similar to the K-TIRADS category results, which were classified as 42 benign and 15 malignant. Between blue and light blue, benign, and between green and red, malignant. If the shear wave elastography method is applied before the fine-needle aspiration cytology of the thyroid nodule is performed, the differential diagnosis of thyroid tissue from benign and malignant can be predicted in advance, and it will help to reduce unnecessary invasive tests.

• Smart Structures and Systems
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• v.29 no.4
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• pp.589-598
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• 2022

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1372-1384
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• 2024

The discrimination of the source for xenon gases' release can provide an important clue for detecting the nuclear activities in the neighboring countries. In this paper, three machine learning techniques, which are logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN), were applied to develop the predictive models for discriminating the source for xenon gases' release based on the xenon isotopic activity ratio data which were generated using the depletion codes, i.e., ORIGEN in SCALE 6.2 and Serpent, for the probable sources. The considered sources for the neighboring countries of South Korea include PWRs, CANDUs, IRT-2000, Yongbyun 5 MWe reactor, and nuclear tests with plutonium and uranium. The results of the analysis showed that the overall prediction accuracies of models with SVM and KNN using six inputs, all exceeded 90%. Particularly, the models based on SVM and KNN that used six or three xenon isotope activity ratios with three classification categories, namely reactor, plutonium bomb, and uranium bomb, had accuracy levels greater than 88%. The prediction performances demonstrate the applicability of machine learning algorithms to predict nuclear threat using ratios of xenon isotopic activity.

• Journal of Korean Tunnelling and Underground Space Association
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• v.8 no.2
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• pp.183-196
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• 2006

Rock joint roughness obtained from the camera-type 3D scanner was classified into waviness and unevenness. The classification criteria were established in the previous study; digital filtering was used to distinguish one from another. The classified and original profiles were used to produce metal moulds. For accurate machining of the moulds, the WEDM(Wire-cut Electric Discharge Machining) was adopted. Specimens were cast using high strength gypsum, and joint shear tests were performed by varying normal stress from low value to high one. Roughness mobilization characteristics depending on the asperity scale and the applied normal stress were investigated. A new equation was proposed to predict shear strength of rock joint, which can consider the characteristics of roughness mobilization and roughness parameters. The roughness quantification composed of waviness and unevenness was found to be a useful method to predict the joint shear strength.

• Journal of Sasang Constitutional Medicine
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• v.21 no.1
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• pp.79-84
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• 2009

1. Objectives This study was proposed to use the test-retest method to verify the independence and agreement between the questions (Short Form Sasang Classification Questionnaire for American ; SF_SSCQ-A). 2. Methods The questionnaire was administered to 50 American and they were asked to complete the questionnaire again two weeks after the first administration. The final subjects are 42 except of 5 dropouts and 3 insufficient respondents. A reliability analysis for each individual question was conducted using the test-retest method to verify the independence and agreement between the questions. To verify independence, a Chi-square test with crosstabs analysis and p-values was used. The agreement verification was conducted by using the Cohen kappa coefficient. In addition, the constitutional variable was defined as the constitutional attribute of each question, and Pearson's correlation coefficient was confirmed after testing the scale. 3. Results and Conclusions The test-retest method showed that Pearson's correlation coefficients for the results of the two tests ranged from 0.66 to 0.87. The Chi-square test results indicated that there are 7 independent item. Among the other 114 questions, the agreement test revealed that 88 questions (70.5%) showed above common agreement.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.1
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• pp.76-83
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• 2011

FCAW(Flux Cored Arc Welding) is a widely used welding method in shipbuilding. It also conducts WPS(Welding Procedure Specification) requested by the classification variations of the factors which affect the quality on the welded area such as thickness of base metal, type of welding wire and shielding gas etc. which has to be satisfied. CO2 is commonly used as a shielding gas for FCAW due to the economic point of view. The amount of shielding gas is stated when classification certify WPS. However, the shielding gas is unnecessarily used at the shipyard leaning only on the welder's experience as there are classification standards for using the shielding gas. It causes production cost to rise. Also recently, CO2 is a main contributor for global warming, and large amounts of CO2 are discharged into the atmosphere during shipbuilding processes without any filtration. Therefore it was confirmed by the security of the welded area as a result of conducting the destructive and non-destructive tests with setting up the factors and the standards by using the Taguchi method. Then the FCAW shielding gas's amounts were calculated precisely when assembling a ship. It will be applied to cost reduction and prevention of environmental pollution at the shipyard.

• Resources Recycling
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• v.31 no.6
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• pp.66-72
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• 2022

The circularity and particle size distribution of products obtained from dry classification after circularity tests using a high-intensity mixer were investigated to evaluate the use of domestic graphite concentrate as a lithium-ion battery material. At a rotor speed of 3,000 rpm, the particle size and circularity of the concentrated sample and product were unchanged. The circularity increased and particle size decreased when the rotor speeds were increased to 6,000 rpm, 10,000 rpm, and 12,000 rpm and the operating time was increased. For instance, the circularity increased from 0.870 to 0.936 when the rotor speed was increased from 3,000 rpm to 12,000 rpm for an operating time of 10 min. After the circularity test, dry classification was performed, wherein the circularity of the coarse particles was found to have increased to 0.947. Round particles were observed in the SEM images, indicating that high circularity was successfully achieved.