The availability for cardiorespiratory fitness measurement by 20 m shuttle run test in different sports type of elite athletes. Exercise Science (엘리트 선수들의 운동특성에 따른 20 m 셔틀런 검사의 유용성)
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- Exercise Science
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- v.21 no.2
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- pp.183-190
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- 2012
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This study is to evaluate the availability of cardiorespiratory fitness measurement by 20 m shuttle run test based upon energy contribution rates of elite athletes in different sports type. Sixty-seven elite athletes attending K national university participated in this study. They were divided by three groups based upon sports type, composed of Anaerobic Group (sprint, jumps, weightlifting, throw; n=35), Aerobic Group (medium-long distance; n=9), and Combat Sport Group (judo; n=23). 20 m shuttle run test was conducted by Leger et al.(1982) method and calculating acceleration using measured shuttle run repetitions was conducted by Brewer et al.(1988) method. To test the usefulness of VO2max, graded exercise treadmill test was conducted and standing long jump and 50 m run were measured as power fitness factors. Z-jump was used for measuring power, agility, and muscular endurance. Standing long jump and 50 m run of Anaerobic Group (AnG) was significantly higher than that of Aerobic Group (AeG) and Combat Sport Group (CG) (p<0.05). However, Z-jump of CG was significantly higher than that of AnG and AeG(p<.05). There was a higher correlation of 20 m shuttle run test and VO2max in AnG(r= 0.577, p<.0001) and CG(r= 0.760, p<.0001). Otherwise, there was a low correlation of 20 m shuttle run test and VO2max in AeG. There was no significant group difference to test the availability of 20 m shuttle run test and there was a reduced error when converting 20 m shuttle run results into VO2max. This study examined the usefulness of 20 m shuttle run test by converting 20 m shuttle run repetition results into VO2max calculation, which showed reduced error. Therefore, this study confirmed that it would be needed to convert 20 m shuttle run results into VO2max for universal and practical use in the field without dividing sports type.
To obtain basic information on the Korean local corn lines a total of 57 lines were selected from 1,000 Korean local collection at Chungnam National University, classified by principal component analysis, and genetic nature was investigated. The results are summarized as follows. 1. There were a great variation in mean values of plant characters of the lines. The mean values of plant characters except for density of kernels varied with types of crossing. All characters except. for tasselling dates were reduced in magnitude when selfed, while those characters were increased when topcrossed. 2. The correlation coefficients among characters studied ranged front 0.99 to -0.59. The correlation coefficients among characters were not greatly changed depending upon types of crosses. 3. In order to classify the lines more effectively, selected 12 plant characters were used to classify 57 local lines by principal component analysis. The first four component could explain 86.4%, 83.4% and 81.1% of the total variations in sibbed lines, selfed lines and topcrossed lines, respectively. 4. Contribution of characters to principal component was high at upper principal components and low at lower principal components. 5. Biological meaning of the principal component and plant types corresponding to the each principal component were explained clearly by the correlation coefficient between principal components and characters. The first principal component appeared to correspond to the size of plant and ear. The second principal component appeared to correspond to the degree of differentiation in organs and the duration of vegetative growing period. But biological meaning of the third and fourth principal components was not clear. 6. The lines were classified into 4 lineal groups by the taxonomic distance. Group I included 52 lines which was 91.2% of total lines, group II 3 lines, group III 1 lines and group IV I lines, respectively. Four groups could be characterized as follows : Group I : early maturity, short-culmed, medium height plant, small ears, medium kernels and medium yielding. Group II : late maturity, medium height plant, small ears, small kernels, prolific ears and higher yielding. Group III : medium maturity, tall-culmed, small ears, small kernels and low yielding. Group IV : medium maturity, tall-calmed, large ears, one ear plant and me yielding. 7. The inbreeding depression varied with plant characters and lines. The characters such as yield, kernel weight per ear, ear weight and plant height showed great degree of inbreeding depression. Group I showed high inbreeding depression in such characters as 100 kernel weight, leaf number, plant height and days to tasselling, while group II showed high inbreeding depression in other plant characters. 8. Heterosis of plant characters varied also with lines. The ear weight, kernel weight per ear, yield, 100 kernel weight, and plant height were some of the plant characters showing high heterosis. Group II showed high values of heterosis in such characters as ear length, ear diameter, ear weight, kernel weight per ear, 100 kernel weight, and leaf length, while group I was high in heterosis in other plant characters. 9. The degree of homozgosity was highest in ear weight (79.1%) and lowest in ear number per plant (-21%). Group II showed higher degree of homozygosity than group I. 10. Correlation coefficients between characters of ribbed and topcrossed lines were positive for all characters. Highly significant. correlation coefficients between ribbed and topcrossed lines were obtained especially for characters such as ear number per plant, plant height, leaf length and yield per plot.
Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.
This experiment was carried out to obtain genetic information for future corn breeding. The materials used for the study were obtained from the nationwide collection of Korean local corn lines. A total of 262 lines were used for the study of morphological characters and for the classification of lines. Results obtained are summarized as follows; 1. The days to flowering of lines ranged from 57 days to 87 days. Most lines had an average of 67 days of flowering days. 2. The number of tillers of lines showed a lot of variation among lines with 49.2% of coefficient of variation. 3. The coefficients of variation computed based on the phenotypic observation or measurement of each line were 36.1%, 27.2%, 20.0%, 16.4% and 16.3% for kernel weight per ear, 100 kernel weight, ear height, plant height and ear length, respectively. 4. Ear height, ear length, ear diameter, tiller number and days to flowering were highly and positively correlation with the plant height. Kernel size, ear size, and plant height were highly correlated with 100 kernel weight and kernel weight per ear. 5. The 262 corn lines were possibly classified into four major groups by the Euclidean distance. Group I comprised 110 lines, group II 74 lines, group III 66 lines and group IV 12 lines, respectively. Group I was characterized as having early maturity, medium plant height large kernel size and large ear size. Group II had medium maturity, short plant height, medium kernel size and small ear size. Group III had medium maturity, medium plant height, large kernel size and medium ear size. Group IV had late maturity, long plant height, small kernel size, small ear size and many tillering. 6. The plant height showed significant difference between group I and II, II and III, and II and IV group. No statistical differences were observed between group III and IV. The ear size of group I was significantly different from those of group II, III and IV. Also difference of ear size between group II and III was significant. The kernel size, 100 kernel weight and kernel weight per ear were all significantly different among all groups classified. The row number was different between group I and II. The row number of lines in group IV was significantly different with group I, II, III respectively. The number of tillers and flowering days of lines in group IV were greatly different from those of group I, II and III. 7. The corn lines collected from northwest plain regions and middle hilly regions in Korea had medium maturity, medium plant height, large ear and large kernels. The corn lines from middle eastern hilly regions had medium size of ear kernels. The corn lines from middle southern hilly regions had late maturity, small kernel size and many tillers. The corn lines from southwest plain areas had late maturity, long plant height and many tillers.
In order to compare the cardiac function of various groups of athletes, the resting electrocardiographic time intervals, amplitudes and vectors were analyzed in high school athletes of throwing(n=7), jumping(n=11), short track(n=8), long track(n=14), boxing(n=7), volleyball(n=8) and baseball(n=9), and nonathletic control students(n= 19). All athletic groups showed a significantly longer R-R interval(0.96-1.09 sec) than the controls (0.78 sec). Therefore, the heart rate was significantly slower in atheletes than in the control, but was not different among the different athletic groups. R-R interval is the sum of intervals of P-R, 0-T and T-P: P-R and Q-T intervals showed no difference among the control and athletic groups, but T-P interval in the jump, short track, long track and boxing groups was significantly higher than the control. R-B interval showed a significant correlation with T-P or Q-T intervals but no correlation with P-R or QRS complex. Comparing the amplitude of electrocardiographic waves, the athletic groups showed a lower trend in P wave than the controls. T wave in lead
Soil harness represents such physical properties as porosity, amount of water, bulk density and soil texture. It is very important to know the mechanical properties of soil as well as the chemical in order to research the fundamental phenomena in the growth and the distribution of tree roots. The writer intended to grip soil hardness by soil layer and also to grasp the root distribution and the correlation between soil hardness and the root distribution of Pinus riguda Mill. planted on the denuded hillside with sooding works by soil layer on soil profile. The site investigated is situated at Peongchang-ri 13, Kocksung county, Chon-nam Province. The area is consisted of 3.63 ha having on elevation of 167.5-207.5 m. Soil texture is sandy loam and parant rock in granite. Average slope of the area is
Introduction: Diffusion is process by which an innovation is communicated through certain channel overtime among the members of a social system(Rogers 1983). Bass(1969) suggested the Bass model describing diffusion process. The Bass model assumes potential adopters of innovation are influenced by mass-media and word-of-mouth from communication with previous adopters. Various expansions of the Bass model have been conducted. Some of them proposed a third factor affecting diffusion. Others proposed multinational diffusion model and it stressed interactive effect on diffusion among several countries. We add a spatial factor in the Bass model as a third communication factor. Because of situation where we can not control the interaction between markets, we need to consider that diffusion within certain market can be influenced by diffusion in contiguous market. The process that certain type of retail extends is a result that particular market can be described by the retail life cycle. Diffusion of retail has pattern following three phases of spatial diffusion: adoption of innovation happens in near the diffusion center first, spreads to the vicinity of the diffusing center and then adoption of innovation is completed in peripheral areas in saturation stage. So we expect spatial effect to be important to describe diffusion of domestic discount store. We define a spatial diffusion model using multinational diffusion model and apply it to the diffusion of discount store. Modeling: In this paper, we define a spatial diffusion model and apply it to the diffusion of discount store. To define a spatial diffusion model, we expand learning model(Kumar and Krishnan 2002) and separate diffusion process in diffusion center(market A) from diffusion process in the vicinity of the diffusing center(market B). The proposed spatial diffusion model is shown in equation (1a) and (1b). Equation (1a) is the diffusion process in diffusion center and equation (1b) is one in the vicinity of the diffusing center.