• Title/Summary/Keyword: 다항함수(多項函數)

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Dependence of 0.01M CaCl2 Soluble Phosphorus on Extractable P and P Sorptivity in Upland Soil (밭토양(土壤)에서 유효린산함량(有效燐酸含量)과 인산흡수능(燐酸吸收能)에 따른 0.01M CaCl2 가용(可溶) 인산농도(燐酸濃度) 변화(變化))

  • Yoon, Jung-Hui;Jung, Beung-Gan;Kim, Yoo-Hak
    • Korean Journal of Soil Science and Fertilizer
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    • v.31 no.3
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    • pp.266-270
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    • 1998
  • The identification of soil P level that exceed crop requirement is a prerequisite in implementing sustainable management of fertilizer and manure P to prevent soil and freshwater from contamination. To investigate the relationship between 0.01M $CaCl_2$ soluble P, and available P and P sorption capacity of 40 soils, P content and P sorptivity were analyzed. Single linear relationship revealed the dependence of 0.01M $CaCl_2-P$ on available P($r^2=0.479$), bioavailable P($r^2=0.281$), P sorption($r^2=-0.465$) and P absorption coefficient($r^2=-0.056^{NS}$). Thus available P as $P_2O_5$(AVP) and P sorption (PS) were most important factors in determining the concentration of 0.01M $CaC1_2-P$($CaC1_2-P$). In multinomial equation related $CaC1_2-P$ with AVP and PS, the determination coefficient was improved to 0.745. The logarithm of $CaC1_2-P$ was linearly related to AVP/PS. Consequently, the equation, $0.01M\;CaCl_2-P=0.1284e^{0.3288AVP/PS}$ could be suggested to estimate the concentration of P in 20mL of 0.01M $CaCl_2$ solution containing 2g of soil shaken for 17 hours.

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VOT Derivation for Different Trip Purposes, Travel Modes and Testing of Their Significance (통행목적별 수단별 통행시간가치도출 및 유의성 검정)

  • Kim, Hyeon;Oh, Se-Chang;Choi, Gi-Ju
    • Journal of Korean Society of Transportation
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    • v.17 no.1
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    • pp.113-129
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    • 1999
  • It is widely recognized that the value of travel time (VOT) plays an important role both in choosing the transportation alternatives on an individual level, and in analyzing and evaluating transportation plans and other public policy makings on a collective level. There is, however, a great deal of difficulties to correctly estimate the VOT. In addition, although there are lots of methods to estimate the VOT so for, not many recommendations have been presented to reflect the localities associated with the VOT derivation in Korea. This study aims at deriving the VOT for different trip purposes and travel modes with their significances tested. To accomplish this purposes, a logit-based travel mode choice model based on revealed preference (RP) data has been formulated, calibrated using the discrete choice model of LIMDEP package for various trip purpose models. For each trip purpose and travel mode, the VOT has been calculated along with the significance testing of the derived VOTs. From the results given in this research, the VOTs for different purposes and modes are identified different, and they are statistically significant. The updated results here in this paper may be a yardstick in evaluating the transportation plans and policies by providing more detailed VOT information for different categories, especially in urban context.

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Genetic Parameters for Milk Production and Somatic Cell Score of First Lactation in Holstein Cattle with Random Regression Test-Day Models (임의회귀 검정일 모형을 이용한 홀스타인 젖소의 1산차 산유형질 및 체세포지수에 대한 유전모수)

  • Lee, D.H.;Jo, J.H.;Han, K.G.
    • Journal of Animal Science and Technology
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    • v.45 no.5
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    • pp.739-748
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    • 2003
  • The objective of this study was to estimate genetic parameters for test-day milk production and somatic cell score using field data collected by dairy herd improvement program in Korea. Random regression animal models were applied to estimate genetic variances for milk production and somatic cell score. Heritabilities for milk yields, fat percentage, protein percentage, solid-not-fat percentage, and somatic cell score from test day records of 5,796 first lactation Holstein cows were estimated by REML algorithm in single trait random regression test-day animal models. For these analyses, Legendre polynomial covariate function was applied to model the fixed effect of age-season, the additive genetic effect and the permanent environment effect as random. Homogeneous residual variance was assumed to be equal throughout lactation. Heritabilities as a function of time were calculated from the estimated curve parameters from univariate analyses. Heritability estimates for milk yields were in range of 0.13 to 0.29 throughout first lactation. Heritability estimates for fat percentage, protein percentage and solid-not-fat percentage were within 0.09 to 0.11, 0.12 to 0.19 and 0.17 to 0.23, respectively. For somatic cell score, heritabilities were within 0.02 to 0.04. Heritabilities for milk productions and somatic cell score were fluctuated by days in milk with comparing 305d milk production.

Assessment of the Potential Consumers' Preference for the V2G System (V2G 시스템에 대한 잠재적 소비자의 선호 평가)

  • Lim, Seul-Ye;Kim, Hee-Hoon;Yoo, Seung-Hoon
    • Journal of Energy Engineering
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    • v.25 no.4
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    • pp.93-102
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    • 2016
  • Vehicle-to-Grid (V2G) system, bi-direction power trading technology, enables drivers possessing electric vehicle to sell the spare electricity charged in the vehicle to power distribution company. The drivers gain profit by charging electricity in the day time of high electricity rate. In this regard, the government is preparing the policies of building and supporting V2G infrastructure and demanding the potential consumers' preference for the V2G system. This paper attempts to analyze the consumers' preference using the data from obtained a survey of randomly selected 1,000 individuals. To this end, choice experiment, an economic technique, is employed here. The attributes considered in the study are residual amount of electricity, electricity trading hours, required plug-in time, and price measured as an amount additional to current gasoline vehicle price. The multinomial logit model, which requires the assumption of 'independence of irrelevant alternatives', is applied but the assumption could not be satisfied in our data. Thus, we finally utilized nested logit model which does not require the assumption. All the parameter estimates in the utility function are statistically significant at the 10% level. The estimation results show that the marginal willingness to pay (MWTP) for one hour increase in electricity trading hours is estimated to be KRW 1,601,057. On the other hand, a one percent reduction in residual amount of electricity and one hour reduction in required plug-in time in V2G system are computed to be KRW -91,911 and -470,619, respectively. The findings can provide policy makers with useful information for decision-making about introducing and managing V2G system.

A Reflectance Normalization Via BRDF Model for the Korean Vegetation using MODIS 250m Data (한반도 식생에 대한 MODIS 250m 자료의 BRDF 효과에 대한 반사도 정규화)

  • Yeom, Jong-Min;Han, Kyung-Soo;Kim, Young-Seup
    • Korean Journal of Remote Sensing
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    • v.21 no.6
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    • pp.445-456
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    • 2005
  • The land surface parameters should be determined with sufficient accuracy, because these play an important role in climate change near the ground. As the surface reflectance presents strong anisotropy, off-nadir viewing results a strong dependency of observations on the Sun - target - sensor geometry. They contribute to the random noise which is produced by surface angular effects. The principal objective of the study is to provide a database of accurate surface reflectance eliminated the angular effects from MODIS 250m reflective channel data over Korea. The MODIS (Moderate Resolution Imaging Spectroradiometer) sensor has provided visible and near infrared channel reflectance at 250m resolution on a daily basis. The successive analytic processing steps were firstly performed on a per-pixel basis to remove cloudy pixels. And for the geometric distortion, the correction process were performed by the nearest neighbor resampling using 2nd-order polynomial obtained from the geolocation information of MODIS Data set. In order to correct the surface anisotropy effects, this paper attempted the semiempirical kernel-driven Bi- directional Reflectance Distribution Function(BRDF) model. The algorithm yields an inversion of the kernel-driven model to the angular components, such as viewing zenith angle, solar zenith angle, viewing azimuth angle, solar azimuth angle from reflectance observed by satellite. First we consider sets of the model observations comprised with a 31-day period to perform the BRDF model. In the next step, Nadir view reflectance normalization is carried out through the modification of the angular components, separated by BRDF model for each spectral band and each pixel. Modeled reflectance values show a good agreement with measured reflectance values and their RMSE(Root Mean Square Error) was totally about 0.01(maximum=0.03). Finally, we provide a normalized surface reflectance database consisted of 36 images for 2001 over Korea.

Optimization of Multiclass Support Vector Machine using Genetic Algorithm: Application to the Prediction of Corporate Credit Rating (유전자 알고리즘을 이용한 다분류 SVM의 최적화: 기업신용등급 예측에의 응용)

  • Ahn, Hyunchul
    • Information Systems Review
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    • v.16 no.3
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    • pp.161-177
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    • 2014
  • Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

A Study on the Prediction Model of Stock Price Index Trend based on GA-MSVM that Simultaneously Optimizes Feature and Instance Selection (입력변수 및 학습사례 선정을 동시에 최적화하는 GA-MSVM 기반 주가지수 추세 예측 모형에 관한 연구)

  • Lee, Jong-sik;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.23 no.4
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    • pp.147-168
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    • 2017
  • There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.