• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.84-89
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• 2014

Not only the emission regulation of on-road vehicle engine, but also emission regulation of off-road engine have been reinforced. It is the reason of wide application of emission reduction technology for off-road engines. In this study, optimization of engine parameters (Injector hole number, Injection timing and EGR rate) for reduction of NOx and smoke emissions were conducted by using the analysis of sensitivity and S/N ratio of Taguchi method(DOE). As results, this paper shows optimum value of the parameters for NOx and smoke emission reduction. From the result of reproducibility verification, it is final that the prediction value of NOx and smoke has the error of below 10%. Consequently, the method and results of this study will be used for quantitative reference to EGR control mapping in next study.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.145-152
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• 2000

X-ray diffraction method detects change of crystal lattice distance under material surface using diffraction angle 2$\theta$. This technique can be applied to the behavior on slip band and micro crack due to material degradation. The relation between half-value breadth and number of cycle has three stages which constitute rapid decrease in initial number of cycles, slight decrease in middle number of cycles and rapid decrease in final number of cycles. The ratio of half-value breadth takes a constant value on B/B$_{0}$-N diagram with loading condition except early part of fatigue life. The ratio of half-value breadth B/B$_{0}$ with respect to number of cycle to failure N$_{f}$ has linear behavior on B/B$_{0}$-log N$_{f}$ diagram. Therefore, in this paper the estimation of fatigue life by average gradient method has much less estimated mean error than the estimation of fatigue life by log B/B$_{0}$-log N/N$_{f}$ relation.elation.ation.

• Journal of Preventive Medicine and Public Health
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• v.45 no.4
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• pp.259-266
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• 2012

Objectives: This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model. Methods: In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve. Results: The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater. Conclusions: When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

• Journal of Korea Game Society
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• v.22 no.1
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• pp.33-42
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• 2022

In online games, interactions with other players may threaten player satisfaction. Therefore, matching players of similar skill levels is important for players' experience. However, with the current evaluation method which is only based on the final result of the game, newbies and returning players are difficult to be matched properly. In this study, we propose a method to improve matchmaking quality. We build machine learning models to predict the MMR of players and derive the basis of the prediction. The error of the best model was 40.4% of the average MMR range, confirming that the proposed method can immediately place players in a league close to their current skill level. In addition, the basis of predictions may help players to accept the result.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.5
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• pp.1172-1182
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• 1993

In orthogonal machining a quantitative model for burr formation process and fracture when tool exits workpiece is proposed. When no fracture during burr formation burr formation process is divided by three parts; Initiation, Development and Final burr formation. According to the properties of workpiece fracture will happen or not after initiation of burr formation. Considering the fact that fracture depends on the ductility of workpiece, the fracture strain obtained from ductile fracture criterion is used for prediction. It is verified that the fracture strain from tension test can be used as fracture criterion in burr formation without large error. For detailed observation of burr formation an experimental stage for micro orthogonal cutting inside SEM (Scanning Electron Microscope) is built. Through the comparison between model prediction and experimental result from orthogonal machining in milling machine the model is verified.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.33-41
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• 2022

In this article, we proposed to predict natural gas (NG) leakage levels through feature selection based on a factor analysis (FA) of the integrating the Korean Meteorological Agency data and natural gas leakage data for considering complex factors. The paper has been divided into three modules. First, we filled missing data based on the linear interpolation method on the integrated data set, and selected essential features using FA with OrdinalEncoder (OE)-based normalization. The dataset is labeled by K-means clustering. The final module uses four algorithms, K-nearest neighbors (KNN), decision tree (DT), random forest (RF), Naive Bayes (NB), to predict gas leakage levels. The proposed method is evaluated by the accuracy, area under the ROC curve (AUC), and mean standard error (MSE). The test results indicate that the OrdinalEncoder-Factor analysis (OE-F)-based classification method has improved successfully. Moreover, OE-F-based KNN (OE-F-KNN) showed the best performance by giving 95.20% accuracy, an AUC of 96.13%, and an MSE of 0.031.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.349-358
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• 2008

In performing the reliability analysis for predicting the settlement with time of alluvial clay layer at Kobe airport, the uncertainties of geotechnical properties were examined based on the stochastic and probabilistic theory. By using Terzaghi's consolidation theory as the objective function, the failure probability was normalized based on AFOSM method. As the result of reliability analysis, the occurrence probabilities for the cases of the target settlement of ${\pm}10%,\;{\pm}25%$ of the total settlement from the deterministic analysis were 30~50%, 60%~90%, respectively. Considering that the variation coefficients of input variable are almost similar as those of past researches, the acceptable error range of the total settlement would be expected in the range of 10% of the predicted total settlement. As the result of sensitivity analysis, the factors which affect significantly on the settlement analysis were the uncertainties of the compression coefficient Cc, the pre-consolidation stress Pc, and the prediction model employed. Accordingly, it is very important for the reliable prediction with high reliability to obtain reliable soil properties such as Cc and Pc by performing laboratory tests in which the in-situ stress and strain conditions are properly simulated.

• Advances in biomechanics and applications
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• v.1 no.3
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• pp.169-185
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• 2014

Computational multibody models of the elbow can provide a versatile tool to study joint mechanics, cartilage loading, ligament function and the effects of joint trauma and orthopaedic repair. An efficiently developed computational model can assist surgeons and other investigators in the design and evaluation of treatments for elbow injuries, and contribute to improvements in patient care. The purpose of this study was to develop an anatomically correct elbow joint model and validate the model against experimental data. The elbow model was constrained by multiple bundles of non-linear ligaments, three-dimensional deformable contacts between articulating geometries, and applied external loads. The developed anatomical computational models of the joint can then be incorporated into neuro-musculoskeletal models within a multibody framework. In the approach presented here, volume images of two cadaver elbows were generated by computed tomography (CT) and one elbow by magnetic resonance imaging (MRI) to construct the three-dimensional bone geometries for the model. The ligaments and triceps tendon were represented with non-linear spring-damper elements as a function of stiffness, ligament length and ligament zero-load length. Articular cartilage was represented as uniform thickness solids that allowed prediction of compliant contact forces. As a final step, the subject specific model was validated by comparing predicted kinematics and triceps tendon forces to experimentally obtained data of the identically loaded cadaver elbow. The maximum root mean square (RMS) error between the predicted and measured kinematics during the complete testing cycle was 4.9 mm medial-lateral translational of the radius relative to the humerus (for Specimen 2 in this study) and 5.30 internal-external rotation of the radius relative to the humerus (for Specimen 3 in this study). The maximum RMS error for triceps tendon force was 7.6 N (for Specimen 3).

• Smart Media Journal
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• v.9 no.3
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• pp.122-129
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• 2020

The possibility that an asymptotic coronavirus-19 infected person around the world is not aware of his infection and can spread it to people around him is still a very important issue in that the public is not free from anxiety and fear over the spread of the epidemic. In this paper, the K-means clustering algorithm and deep learning-based crowd aggregation were proposed to determine the possibility of contact with confirmed cases of infectious diseases. As a result of 300 iterations of all input learning images, the PSNR value was 21.51, and the final MAE value for the entire data set was 67.984. This means the average absolute error between observations and the average absolute error of fewer than 4,000 people in each CCTV scene, including the calculation of the distance and infection rate from the confirmed patient and the surrounding persons, the net group of potential patient movements, and the prediction of the infection rate.

• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.376-383
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• 2012

A number of scientific researches are currently being conducted on the potential health hazards of power frequency electric and magnetic field (EMF). There exists a non-objective and psychological belief that they are harmful, although no scientific and objective proof of such exists. This possible health risk from ELF magnetic field (MF) exposure, especially for children under 17 years of age, is currently one of Korea's most highly contested social issues. Therefore, to assess the magnetic field exposure levels of those children in their general living environments, the personal MF exposure levels of 436 subjects were measured for about 6 years using government funding. Using the measured database, estimation formulas were developed to predict personal MF exposure levels. These formulas can serve as valuable tools in estimating 24-hour personal MF exposure levels without directly measuring the exposure. Three types of estimation formulas were developed by applying evolutionary computation methods such as genetic algorithm (GA) and genetic programming (GP). After tuning the database, the final three formulas with the smallest estimation error were selected, where the target estimation error was approximately 0.03 ${\mu}T$. The seven parameters of each of these three formulas are gender (G), age (A), house type (H), house size (HS), distance between the subject's residence and a power line (RD), power line voltage class (KV), and the usage conditions of electric appliances (RULE).