• Journal of Korean Society of Forest Science
• /
• v.79 no.1
• /
• pp.16-20
• /
• 1990

Canonical correlation analysis was used to relate 17 soil-site variables to bamboo diameter, height, and internodal characteristics. The first canonical correlation was highly significant, explained much of the variance in both sets of variables, and the canonical variates made sense biologically. Surface soil depth, total nitrogen and percent organic matter had high positive correlations with the first soil-site canonical variate. Clay content (%) and cation exchange capacity were negatively correlated with the first soil-site canonical variate. Only 8 of predictor variables were considered relevant for predicting bamboo growth.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.23 no.1
• /
• pp.55-70
• /
• 2019

The phase properties of ground acceleration records from Mw 5.5~6.5 earthquakes are analyzed. The interrelationships between phase properties and significant durations, as well as PGA, are clarified through both of theoretical and empirical approaches. The probabilistic characteristics of phase information is also discussed based on previous studies and it is shown that circular normal distribution is the most appropriate probability distribution for the phase angle and phase difference. Whereas those variates can be modeled by Gaussian random variables. From the survey results on the frequency dependency of the phase statistics, a simple model is introduced, which is possible to express the frequency dependency of phase information. It is also shown that the significant duration can be controlled by appropriately chosen standard deviation of phase difference for 4~8Hz frequency band and additional consideration of phase scattering in higher frequency band through a series of Monte Carlo simulations. The source of phase scattering effect is also pointed out and discussed.

• Journal of Korean Society of Forest Science
• /
• v.77 no.4
• /
• pp.429-444
• /
• 1988

Considering the recent trent toward the development of multiple-use of forest trees, investigations for comprehensive information on these young stands of Hinoki cypress are necessary for rational forest management. From this point of view, 83 sample trees were selected and cut down from 23-ear old stands of Hinoki cypress at Changsung-gun, Chonnam-do. Various stem growth factors of felled trees were measured and canonical correlaton analysis, principal component analysis and factor analysis were applied to investigate the stem growth characteristics, relationships among stem growth factors, and to get potential information and comprehensive information. The results are as follows ; Canonical correlation coefficient between stem volume and quality growth factor was 0.9877. Coefficient of canonical variates showed that DBH among diameter growth factors and height among height growth factors had important effects on stem volume. From the analysis of relationship between stem-volume and canonical variates, which were linearly combined DBH with height as one set, DBH had greater influence on volume growth than height. The 1st-2nd principal components here adopted to fit the effective value of 85% from the pincipal component analysis for 12 stem growth factors. The result showed that the 1st-2nd principal component had cumulative contribution rate of 88.10%. The 1st and the 2nd principal components were interpreted as "size factor" and "shape factor", respectively. From summed proportion of the efficient principal component fur each variate, information of variates except crown diameter, clear length and form height explained more than 87%. Two common factors were set by the eigen value obtained from SMC (squared multiple correlation) of diagonal elements of canonical matrix. There were 2 latent factors, $f_1$ and $f_2$. The former way interpreted as nature of diameter growth system. In inherent phenomenon of 12 growth factor, communalities except clear length and crown diameter had great explanatory poorer of 78.62-98.30%. Eighty three sample trees could he classified into 5 stem types as follows ; medium type within a radius of ${\pm}1$ standard deviation of factor scores, uniformity type in diameter and height growth in the 1st quadrant, slim type in the 2nd quadrant, dwarfish type in the 3rd quadrant, and fall-holed type in the 4 th quadrant.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.1
• /
• pp.201-208
• /
• 2021

This study aims to provide the relevant basis upon which prediction of dyslipidemia should be made based on body information. Using the National Health Insurance big data (3,312,971 people) canonical correlation analysis was performed between body information and lipid-profile. Body information included age, height, weight and waist circumference, while the lipid-profile included total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol. As a result, when the waist circumference and the weight are large, triglycerides increase and HDL cholesterol level decreases. In terms of age, weight, waist circumference, and HDL cholesterol, the canonical variates (the degree of influence) were significantly different according to sex. In particular, the canonical variate was dramatically changed around the forties and fifties in women in terms of weight, waist circumference, and HDL cholesterol. The canonical correlation results of the health care big data presented in this study will help construct a predictive model that can evaluate an individual's health status based on body information that can be easily measured in a non-invasive manner.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1244-1244
• /
• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1042-1042
• /
• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.

• Journal of Korea Water Resources Association
• /
• v.42 no.5
• /
• pp.365-374
• /
• 2009

Probability plotting positions are used for the graphical display of annual maximum rainfall or flood series and the estimation of exceedance probability of those values. In addition, plotting positions allow a visual examination of the fitness of probability distribution provided by frequency analysis for a given data. Therefore, the graphical approach using plotting position has been applied to many fields of hydrology and water resources planning. In this study, the plotting position formula for the Gumbel distribution is derived by using the order statistics and the probability weight moment of the Gumbel distribution for various sample sizes. And then, the parameters of plotting position formula for the Gumbel distribution are estimated by using genetic algorithm. The appropriate plotting position formulas for the Gumbel distribution are examined by the comparison of root mean square errors and biases between theoretical reduced Gumbel variates and those calculated from derived and existing plotting position formulas. As the results, Gringorten's plotting position formula has the smaller root mean square errors and biases than any other formulas.

• Journal of Korea Technology Innovation Society
• /
• v.20 no.1
• /
• pp.127-152
• /
• 2017

China's utility model as a supplement to the invention patent, has short application duration, fast authorized speed, and has the same exclusive rights with patents, so companies can quickly dominate the market. But the utility model does not need to carry out substantive examination, so has lower stability, high frequency of invalid to accepted, so compare with the invention patent, difficult to be protected. In order to actively encourage the small and medium-sized enterprises to promote their inventions, and protect domestic patents, China established a protection policy of patent evaluation report for the utility model rights, especially the patent evaluation report can be used as evidence in a patent infringement trial, to provide judicial remedies for utility model patentee and the party of patent disputes. Many experts believe that the establishment of patent evaluation report system can improve the stability of the utility model patent right, and when the defendant request for invalidation of the patent right in the defense period, if there is no novelty, creativity lost or no other reason has not led to the stability of patent right given in a patent evaluation report of the utility model patents, the court may not suspend the trial, without having to wait for the Patent Reexamination Board makes the patent invalid declaration decisions, can improve the efficiency of the judicial process, accelerate the patentee's time. However, in practical patent infringement, the patent evaluation report system and invalidation system are in conflict. In this paper, through the analysis of the current China utility model system and compared with the South Korean utility model system, review the role and character of the patent evaluation report system, and through the actual cases of the utility model patent infringement litigation, analysis possible variates from the decision of patent evaluation report, to find out the reason of the patent evaluation report system being in conflict with the invalidation system, and research on the effectiveness for protecting Utility Model with China's Patent Evaluation Report.

• Korean Journal of Soil Science and Fertilizer
• /
• v.6 no.1
• /
• pp.17-26
• /
• 1973

A survey on nutrient recovery by rice plant was carried out countrywide in 1967 and 1968. The relationships between percent recovery of fertilizer nutrient and climatic zone or deposition mode, drainage grade, and texture of paddy soil profile, or chemical characteristics of surface soil were as follows. 1. The percent recovery of fertilizer nitrogen was highest in south and least in north, and that of potassium was highest in south and least in middle climatic zone. 2. Since the percent recovery of Phosphorus variates yearly with climatic zone, mode of deposition drainage grade or soil texture, it seemed to depend greatly on soil-weather interaction. 3. Nitrogen recovery was highest in alluvial colluvial (AC) and it was followed by alluvial (A), fluvomarine (FM) and old alluvial in decreasing order while potassium recovery was OA>AC>A>FM. 4. The greater the drainage was, the higher the nitrogen recovery. The recovery of potassium and phosphorus tended to show high in moderately well drain, and low in poorly and well drain. 5. Nitrogen recovery was highest in fine silty and gradually decreased with coarseness. That of potassium or phosphorus was greater in those below fine loamy than in those above coarse silty. 6. Nitrogen recovery was high in Jisan, Geugrag, and Sindab series, and low in Hwadong, Gyuam, Yongji and Hwabong series. 7. Nitrogen recovery showed significant positive correlation with the content of organic matter (OM), Ca, CEC of surface soil and only in the year of high phosphorus recovery it had significant negative correlation with soil phosphorus. Phosphorus recovery had significant posititive correlation with CEC, Mg or Ca. 8. Potassium recovery showed negative correlation with K/(Ca+Mg), P, OM or K while positive correlation with Ca, Mg, CEC but significant only with K/(Ca+Mg) in the year of low potassium recovery. In the year of high K recovery it showed positive correlation with P, OM, K/(Ca+Mg) or K while negative with CEC, Mg or Ca but significant only with P, OM or CEC. Soil potassium has significant positive correlation with soil OM and P only in the year of low potassium recovery. 9. The percent recovery of N, P or K showed negative correlation coefficient with pH without significant. 10. There was significant positive correlation between OM and P, K or K/(Ca+Mg), P and K or K/(Ca+Mg), K and K/(Ca+Mg), Mg or CEC, Ca and K/(Ca+Mg), Mg, CEC or pH, Mg and CEC while significant negative correlation between Mg and OM, P or K/(Ca+Mg), P and CEC, Ca and K/(Ca+Mg). 11. From the percent rcovery of fertilizer and soil chemical characteristics it was known that soil organic matter increase nitrogen uptake, that K uptake has closer relation to K/(Ca+Mg) than K, that Mg affects P ugtake, and that the annual difference of P and K recovery was partly explainable.