• Korean Journal of Applied Biomechanics
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• v.25 no.2
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• pp.157-165
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• 2015

Object : The goals of this research were to make Performance Enhanced Model(PE) taken the largest performance index (PI) through artificial variation of principle components calculated by principle component analysis for trial data, and to verify the effect through comparing kinematic factors between trial data (Raw) and PE. Method : Ten subjects (5 men, 5 women) were recruited and 80% of their maximal record was considered. The PI is a regression equation. In order to develop PE, we extracted Principle components from trial position data (by Principle Components Analysis (PCA)). Before PCA, we made 17 position data to 3 row matrix according to components. We calculated 3 eigen value (principle components) through PCA. And except Y (medial-lateral direction) component (because motion of Y component is small), principle components of X (anterior-posterior direction) and Z (vertical direction) components were changed as following. Changed principle components = principle components + principle components ${\times}$ k. After changing the each principle component, we reconstructed position data using the changed principle components and calculated performance index (PI). A Paired t-test was used to compare Raw data and Performance Enhanced Model data. The level of statistical significance was set at $p{\leq}0.05$. Result : The PI was significantly increased about 12.9kg at PE ($101.92{\pm}6.25$) when compared to the Raw data ($91.29{\pm}7.10$). It means that performance can be increased by optimizing 3D positions. The difference of kinematic factors as follows : the movement distance of the bar from start to lock out was significantly larger (about 1cm) for PE, the width of anterior-posterior bar position in full phase was significantly wider (about 1.3cm) for PE and the horizontal displacement toward the weightlifter after beginning of descent from maximal height was significantly greater (about 0.4cm) for PE. Additionally, the minimum knee angle in the 2-pull phase was significantly smaller (approximately 2.7cm) for the PE compared to that of the Raw. PE was decided at proximal position from the Raw (origin point (0,0)) of PC variation). Conclusion : PI was decided at proximal position from the Raw (origin point (0,0)) of PC variation). This means that Performance Enhanced Model was decided by similar motion to the Raw without a great change. Therefore, weightlifters could be accept Performance Enhanced Model easily, comfortably and without large stress. The Performance Enhance Model can provide training direction for athletes to improve their weightlifting records.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.3
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• pp.251-255
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• 2004

The epidemics of rice blast which occurred in south parts of Korea during the period from 1999 to 2001 and damaged several high quality rice cultivars developed using "Milyang 95" and/or "Milyang 96" as a parent. Genetic diversity of 23 rice cultivars including "Milyang 95" and it's relatives was assessed using 54 simple sequence repeats (SSR) markers reported to be linked to major blast resistance genes. Fifty-four SSR markers representing fifty-seven loci in the rice genome detected polymorphism among the 23 cultivars and revealed a total of 170 alleles with an average of 3.0 alleles per primer, The number of amplified bands ranged from 1 to 7. Several SSR markers including RM249, RM206 and OSR20 were informative for assessing the genetic diversity of relatively closed japonica rice cultivars. The 23 cultivars were classified into four groups by cluster analysis based on Nei's genetic distances, and the cultivars developed from same parents showed a tendency to cluster together that is consistant with genealogical information. High quality rice cultivars, Daesanbyeo, Donganbyeo, and Milyang 95 belonged to the same cluster, At the loci, RM254 and OSR32, all of the cultivars derived from the crosses using "Milyang 95" shared same alleles, suggesting that these japonica cultivars might carry alleles that are identical by descent. Evaluation of 23 rice cultivars against blast needs to be confirmed regarding the relationship between genotype and blast resistance.p between genotype and blast resistance.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.19-33
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• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.

• Journal of Korean Neurosurgical Society
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• v.29 no.2
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• pp.240-248
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• 2000

Objective : Spontaneous intracranial hypotension is a rarely reported syndrome of spontaneous postural headache associated with low CSF pressure and has rarely been demonstrated radiographically or surgically. But recently, it is being recognized with increasing frequency. The purpose of this study was to characterize clinical and imaging features, etiologic factors, and outcome in the spontaneous intracranial hypotension. Patients and Methods : We reviewed our experience with documented cases of spontaneous intracranial hypotension in 5 consecutive patients with orthostatic headaches from April 1998 to April 1999. Results : The mean age was 41 years(from 35 to 49 years). All patients had postural headaches, which were completely alleviated by recumbency position. Nausea, neck pain, horizontal diplopia, photophobia, and blurred vision were noted in some of the patients. Brain MRI showed diffuse pachymeningeal gadolinium enhancement, subdural collections of fluid, and descent of the brain. The opening pressure from lumbar puncture was $4cmH_2O$ or less in three of five patients whereas the opening pressure was within normal range in two patients. All patients underwent radioisotope cisternography and computerized tomographic myelography. On radioisotope cisternography, CSF leakage was suspected at the level of cervical area(1 patient), upper thoracic area(2 patients), mid-thoracic area(1 patient). Computed tomography myelography revealed extraarachnoid accumulation of contrast media(compatible finding with CSF leakage) at the level of cervical or thoracic area. In all patients, the symptoms resolved in response to supportive measures or epidural blood patch(1 patient). Conclusion : Spontaneous spinal CSF leakage is increasingly recognized as a cause of spinal postural headache. Most CSF leaks are located at the cervicothoracic junction or in the thoracic spine and can be demonstrated by variable diagnostic method. The condition is usually self-limiting and its prognosis is typically good.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.3
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• pp.205-211
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• 2005

This study was carried out to construct cold-tolerance characteristics. The RILs were developed from progenies of a cross between cold-susceptible Tongil-type rice variety, Milyang 23 and cold-tolerant Japonica rice variety, Stejaree 45 by single seed descent methods. The 175 RILs $(F_8)$ were evaluated for cold tolerance traits by field screening under cold-water irrigation. Frequency distribution of RILs in leaf discoloration, heading delay, culm length reduction and number of spikelets reduction displayed nearly normal distributions with transgressive segregations to either side of parents, while the spikelet fertility reduction and panicle exsertion at low-temperature showed the more or less skewed continuous distribution toward the susceptible parent. Higher heritabilities over $60\%$ were observed in leaf discoloration, spikelet fertility reduction, panicle exsertion, while relatively lower heritabilities less than $40\%$ were observed in culm length reduction, number of spikelets reduction and grain yield reduction. Some cold-tolerance RILs were selected effectively by cold water irrigation, which are expected to be good materials in breeding program for cold tolerance.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.3
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• pp.289-300
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• 2013

This study numerically considered the characteristic of smoke movement and the effect of hot smoke gas on tunnel wall surface temperature during road tunnel fire under boundary condition of fire growth curve that is applied to fire analysis in road tunnels. The maximum heat release rate were 20 MW and 100 MW and tunnel air velocities were 2.5 m/s and velocity induced by thermal buoyancy respectively, also the cooling effect of tunnel wall was considered. As results, when tunnel air velocity was constant at 2.5 m/s during tunnel fire, due to the cooling effect of tunnel wall, the smoke layer was rapidly descent after some distance and it flowed the same patterns at the downstream. When heat release rate was 100 MW (and jet fan was not installed), the maximum temperature of tunnel wall surface has risen up to $615^{\circ}C$. The heat transfer coefficient of tunnel wall surface was varied from 13 to $23W/m^2^{\circ}C$ approximately.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.97-107
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• 2017

This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model (adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load (SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.

• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.109-123
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• 2004

Corporate financial distress and bankruptcy prediction is one of the major application areas of artificial neural networks (ANNs) in finance and management. ANNs have showed high prediction performance in this area, but sometimes are confronted with inconsistent and unpredictable performance for noisy data. In addition, it may not be possible to train ANN or the training task cannot be effectively carried out without data reduction when the amount of data is so large because training the large data set needs much processing time and additional costs of collecting data. Instance selection is one of popular methods for dimensionality reduction and is directly related to data reduction. Although some researchers have addressed the need for instance selection in instance-based learning algorithms, there is little research on instance selection for ANN. This study proposes a genetic algorithm (GA) approach to instance selection in ANN for bankruptcy prediction. In this study, we use ANN supported by the GA to optimize the connection weights between layers and select relevant instances. It is expected that the globally evolved weights mitigate the well-known limitations of gradient descent algorithm of backpropagation algorithm. In addition, genetically selected instances will shorten the learning time and enhance prediction performance. This study will compare the proposed model with other major data mining techniques. Experimental results show that the GA approach is a promising method for instance selection in ANN.

• Korean Journal of Applied Biomechanics
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• v.18 no.4
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• pp.67-76
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• 2008

The purpose of this study was to analyze differences in kinematic variables for mogul short turn motion between superior and inferior group, so that it can explore more effective mogul short turn motions. To meet the goals, this study selected total 10 ski players who would participate in mogul short turn event of the National Technical Ski Championship 2007, so that it could analyze kinematic variables by way of 3D motion analysis using DLT method. As a result, this study came to the following conclusions; For total and phase-specific duration, it was found that superior group took shorter time than inferior group. Superior group's Center of Mass was stands for more high value in up-down movement skill than inferior group. However right-left movement scale was less than them. In this reason, superior group was made a straight descent at the same time made a fast front-rear velocity. In the part of up-down movement velocity show that move slowly in the drop-in phase while increased in the bump-up phase. It is show that superior group was less tinny than inferior group include joint angle and knee joint angle. However leaning angle of trunk and the body inclination angle were more high figured than inferior group. Leaning angle of lower limbs also showed high figure at the center mogul. Lastly, In the part of body torsion angle show that superior group was high figure direction of right turn in the drop-in phase while in bump-up phase, made a high figure direction of left turn.