• Journal of Animal Science and Technology
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• v.54 no.2
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• pp.83-87
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• 2012

The purpose of this study was to estimate genetic parameters for productive traits in swine. Productive traits were considered on average daily gain (ADG), body height (BH) and body length (BL). Genetic analysis was consisted of 18,668 heads for productive traits which were based on on-farm performance tested from May, 2007 to Apr, 2011. For estimating genetic parameters on productive traits, single best model was fitted after finding source of variance on fixed and random effects and estimated with a multiple trait animal model by using DF-REML (Derivative-Free Restricted Maximum Likelihood). The estimated heritabilities of Duroc, Berkshire, Landrace and Yorkshire 0.22-0.58 for the average daily gain, 0.34-0.41 for the body height and 0.4-0.52 for the body length, respectively. Phenotypic correlations of average daily gain with body height and body length for the four breeds were 0.42-0.48, 0.53-0.58, 0.34-0.46 and 0.47-0.56, respectively. Phenotypic correlations of body height with body length were 0.41, 0.57, 0.52, 0.59, respectively. The estimated genetic correlation coefficients of average daily gain with body height and body length estimated for the four breeds were 0.34-0.47, 0.70-0.75, 0.17-0.38 and 0.50-0.53, respectively. The estimated genetic correlation coefficients of body height with body length were 0.57, 0.69, 0.61 and 0.71, respectively.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.27-57
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• 2008

Many consumers enjoy the challenge of purchasing a brand that matches well with their own values and personalities (for example, Ko et al., 2008; Ko et al., 2006). Therefore, the personalities of consumers can impact on the final selection of a brand and its brand personality in two ways: first, the consumers may incline to purchase a brand or a product that reflects their own personalities; second, consumers tend to choose a company that has similar brand personalities to those brands that are being promoted. Therefore, the objectives of this study are following: 1. Is there any empirical relationship between a consumer's personality and the personality of a brand that he or she chooses? 2. Can a corporate brand be differentiated by the brand personality? In short, consumers are more likely to hold favorable attitudes towards those brands that match their own personality and will most probably purchase those brands matching well with their personality. For example, Matzler et al. (2006) found that extraversion and openness were positively related to hedonic product value; and that the personality traits directly (openness) and indirectly (extraversion, via hedonic value) influenced brand effects, which in turn droved attitudinal and purchase loyalty. Based on the above discussion, the following hypotheses are proposed: Hypothesis 1: the personality of a consumer is related to the brand personality of a product/corporate that he/she purchases. Kuksov (2007) and Wernerfelt (1990) argued that brands as a symbolic language allowed consumers to communicate their types to each other and postulated that consumers had a certain value of communicating their types to each other. Therefore, how brand meanings are established, and how a firm communicate with consumers about the meanings of the brand are interesting topics for research (for example, Escalas and Bettman, 2005; McCracken, 1989; Moon, 2007). Hence, the following hypothesis is proposed: Hypothesis 2: A corporate brand identity is differentiated by the brand personality. And there are significant differences among companies. A questionnaire was developed for collecting empirical measures of the Big-Five personality traits and brand personality variables. A survey was conducted to the online access panel members through the Internet during December 2007 in Korea. In total, 500 respondents completed the questionnaire, and considered as useable. Personality constructs were measured using the Five-factor Inventory (NEO-FFI) scale and a total of 30 items were actually utilized. Brand personality was measured using the five-dimension scale developed by Aaker (1997). A total of 17 items were actually utilized. The seven-point Likert-type scale was the format of responses, for example, from 1 indicating strongly disagreed to 7 for strongly agreed. The Analysis of Moment Structures (AMOS) was used for an empirical testing of the model, and the Maximum Likelihood Estimation (MLE) was applied to estimate numerical values for the components in the model. To diagnose the presence of distribution problems in the data and to gauge their effects on the parameter estimates, bootstapping method was used. The results of the hypothesis-1 test empirically show that there exit certain causality relationship between a consumer's personality and the brand personality of the consumer's choice. Thus, the consumer's personality has an impact on consumer's final selection of a brand that has a brand personality matches well with their own personalities. In other words, the consumers are inclined to purchase a brand that reflects their own personalities and tend to choose a company that has similar brand personalities to those of the brand being promoted. The results of this study further suggest that certain dimensions of the brand personality cause consumers to have preference to certain (corporate) brands. For example, the conscientiousness, neuroticism, and extraversion of the consumer personality have positively related to a selection of "ruggedness" characteristics of the brand personality. Consumers who possess that personality dimension seek for matching with certain brand personality dimensions. Results of the hypothesis-2 test show that the average "ruggedness" attributes of the brand personality differ significantly among Korean automobile manufacturers. However, the result of ANOVA also indicates that there are no significant differences in the mean values among manufacturers for the "sophistication," "excitement," "competence" and "sincerity" attributes of the corporate brand personality. The tight link between what a firm is and its corporate brand means that there is far less room for marketing communications than there is with products and brands. Consequently, successful corporate brand strategies must position the organization within the boundaries of what is acceptable, while at the same time differentiating the organization from its competitors.

• Journal of Animal Science and Technology
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• v.44 no.5
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• pp.499-506
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• 2002

This study was conducted to estimate the heritabilities and genetic and phenotypic correlations among average daily gain, age at 90kg and backfat thickness in Yorkshire pigs. The data were obtained from 2,111 heads of Yorkshire tested at National Livestock Research Institute from May, 1994 to April, 2000. Genetic parameters were estimated with a multiple trait animal model by using DF-REML (Deri- vative-Free REstricted Maximum Likelihood). The results obtained are summarized as follows ; The means of traits studied were 0.871${\pm}$0.124 kg for average daily gain, 145.397${\pm}$11.718 days for age at 90kg and 1.476${\pm}$0.241 cm for backfat thickness. The estimated heritabilities were 0.55 for average daily gain, 0.56 for age at 90kg and 0.55 for backfat thickness. The genetic correlation of average daily gain with age at 90kg and backfat thickness were －0.82, 0.10, respectively. The genetic correlation of age at 90kg with backfat thickness was －0.25. The phenotypic correlations of average daily gain(ADG) with age at 90kg and backfat thickness and age at 90kg with backfat thickness were －0.77, 0.02 and －0.05 respectively. Though phenotypic correlation of ADG and age at 90kg was low, breeding project should be carefully considered by high genetic correlation. High heritabilities on all economic traits were obtained. Therefore, it is considered that suitable selection and management is needed successful improvement.

• Journal of Animal Science and Technology
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• v.54 no.5
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• pp.331-336
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• 2012

This study was conducted to estimate genetic parameters for ultrasound and carcass traits in Hanwoo. Heritabilities and genetic and phenotypic correlations were estimated for carcass and ultrasound measurements collected from Hanwoo cows (n=312) born at Hanwoo experiment station. Traits evaluated were eye muscle area (EMA), backfat thickness (BF), marbling score (MS) from carcass, and ultrasound eye muscle area (UEMA), ultrasound backfat (UBF), and ultrasound marbling score (UMS). Parameters were estimated using multi-trait animal models byderivative-free restricted maximum likelihood procedures. Estimated heritabilities for UBF, UEMA and UMS were 0.43, 0.23 and 0.32, while heritabilities for BF, EMA and MS were 0.33, 0.13 and 0.33 in fattened cows, respectively. Genetic correlations between ultrasound and carcass measurements were estimated to -0.19, -0.61, and -0.36 for UBF: UEMA, UBF: UMS, and UEMA: UMS in fattened cows, respectively. Phenotypic correlations between ultrasound and carcass measurements were 0.03, 0.13 and 0.26 for UBF: UEMA, UBF: UMS, and UEMA: UMS in fattened cows, respectively. As for the steer, genetic correlations between ultrasound and carcass measurements were 0.36, -0.80 and 0.27 for UBF: UEMA, UBF: UMS, and UEMA: UMS in steers, respectively. Phenotypic correlations between ultrasound and carcass measurements were 0.13, 0.07 and 0.41 for UBF: UEMA, UBF: UMS, and UEMA: UMS in steers, respectively. In conclusion, this finding would be very useful to implement into Hanwoo breeding program.

• Journal of Animal Science and Technology
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• v.49 no.6
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• pp.689-698
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• 2007

This study aims to estimate the genetic parameters for carcass traits on Hanwoo of breeding farmhouses using Animal Products Grading Service’s data of 428,812 cattle from 101 slaughterhouses nationwide from 2000 to 2005. Using carcass traits of carcass weight, eye muscle area, backfat thickness, marbling score, meat color and fat color that greatly influence Hanwoo's grade, the effects of carcass year, carcass season, sex and carcass region were estimated. Based upon carcass traits of carcass weight, eye muscle area, backfat thickness, marbling score and meat color that greatly influence Hanwoo’s grade, the heritabilities and genetic parameters were estimated of 17,578 Hanwoo slaughtered in 2005 with existing herdbook, where EM-REML algorithm was used in estimating genetic parameters. The mean and standard deviation of each carcass trait are 321.42±53.62kg, 76.25±10.43cm2, 9.96± 4.14mm, 3.75±2.00, 4.83±0.48 and 2.99±0.40, for carcass weight, eye muscle area, backfat thickness, marbling score, meat color and fat color, respectively. As a result of analysis on the effects of carcass year, the carcass weight, backfat thickness and meat color came out highest as 359.40±0.181, 9.82±0.017 and 4.90±0.002, respectively in 2004. As a result of analysis on the effects of carcass season, the carcass weight and eye muscle area came out highest as 345.88±0.144 and 79.57±0.033 respectively in spring, and the backfat thickness was highest as 8.78±0.013 in winter, and the meat color and fat color slightly came out higher as 4.88±0.002 and 2.96±0.001 in fall, while the marbling score was highest as 3.29±0.006 in summer. The results of the analysis on the effects of sex indicated that the backfat thickness and fat color were highest as 10.53±0.010 and 3.07±0.001 in cow, the carcass weight came out highest in Hanwoo steer as 368.03±0.068kg, the eye muscle area were highest as 82.96±0.042 in bull, and the marbling score was highest as 4.19±0.007 in steer, and the meat color was highest as 4.89±0.001 in cow. Regarding the results of analysis on the effects of carcass region, the carcass weight, eye muscle area,

• Journal of Embryo Transfer
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• v.20 no.3
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• pp.255-269
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• 2005

This study was aimed to investigate genetic relationships, variables, and correlations between economic traits and metabolic materials in serum components according to bleeding periods and breeding locations for the castrated and not castrated Hanwoo cattle at National Livestock Research Institute. Analysis of variance for serum hormones and metabolic materials showed significant differences by breeding locations except for testosterone and globulin. Statistical differences for serum components were detected by birth year except for cortisol, total protein, globulin and creatinine, and by castration except for total protein and BUN. All the serum components were tended to have sire effects except for testosterone resulting in some degree of additive gene actions. Breeding locations showed statistical significances for carcass weight and back fat thickness, but not in carcass rate, KPH, live weight and transportation weight loss. Effects of breeding locations and castration were significant for all weight measurement periods except for 9 month and 6 month, respectively. A significant sire effect was observed in all weight measurements. Least squared means for concentration of serum components by breeding year, season and castration were not significant. High concentration of cortisol, creatinine and triglyceride and low concentration of IGF-1 and glucose were detected in castrated cattle. Concentration of testosterone with castrated cattle was $5.2\%$ corresponding to non castrated cattle. Estimation of heritabilities of serum components using a sire model with restricted maximum likelihood were ranged 0.07 to 0.58. High heritabilities were estimated for total protein, albumin, globulin, cortisol, creatinine and BUN were 0.53, 0.54, 0.42, 0.45, 0.58 and 0.54, respectively. Low heritabilities were estimated fur calcium, testosterone and IGF-1 for 0.07, 0.15 and 0.12, respectively. Heritabilities for carcass weight, back fat thickness, meat yield index, KPH, and IMF were estimated as 0.39, 0.45, 0.30 0.13, and 0.93. Heritabilities of weights on 18, 12, 9, 6, and 24 month were estimated as 0.78, 0.76, 0.62, 0.58 and 0.58. Estimated heritabilities for average daily gain on 6${\~}$2, 12${\~}$18, and 18${\~}$24 month were 0.80, 0.75 and 0.19, respectively.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.509-516
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• 2004

This study is to investigate the amount of biased estimates for heritability and genetic correlation according to data structure on marbling scores in Korean cattle. Breeding population with 5 generations were simulated by way of selection for carcass weight, Longissimus muscle area and latent values of marbling scores and random mating. Latent variables of marbling scores were categorized into five by the thresholds of 0, I, 2, and 3 SD(DSI) or seven by the thresholds of -2, -1, 0,1I, 2, and 3 SD(DS2). Variance components and genetic pararneters(Heritabilities and Genetic correlations) were estimated by restricted maximum likelihood on multivariate linear mixed animal models and by Gibbs sampling algorithms on multivariate threshold mixed animal models in DS1 and DS2. Simulation was performed for 10 replicates and averages and empirical standard deviation were calculated. Using REML, heritabilitis of marbling score were under-estimated as 0.315 and 0.462 on DS1 and DS2, respectively, with comparison of the pararneter(0.500). Otherwise, using Gibbs sampling in the multivariate threshold animal models, these estimates did not significantly differ to the parameter. Residual correlations of marbling score to other traits were reduced with comparing the parameters when using REML algorithm with assuming linear and normal distribution. This would be due to loss of information and therefore, reduced variation on marbling score. As concluding, genetic variation of marbling would be well defined if liability concepts were adopted on marbling score and implemented threshold mixed model on genetic parameter estimation in Korean cattle.

• Journal of Animal Science and Technology
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• v.45 no.5
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• pp.667-678
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• 2003

The objectives of this study were to investigate the genetic characteristics of body weight by growth periods for Hanwoo. A total of 1,736 records were used for body weight. The data for body weights were collected from 1990 to 2000 in Daekwanryong branch, National Livestock Research Institute(NLRI). Estimates of (co)variance components were obtained by derivative-free Restricted Maximum Likelihood (DF-REML). The results are summarized as follows; The means for the weights were 25.60, 79.31, 98.91, 145.40, 283.26, 392.32, 545.65kg at birth, 3, 4, 6, 12, 18, 24month postpartum, respectively. The effects of calving year-season were significant for the milk yield of cow. Heritability estimates of direct genetic effects for birth weight were 0.54(all), 0.52(female), 0.36(male) in modelⅠ, 0.45(all), 0.41(female), 0.24(male) in modelⅡ, and heritabilities estimates of direct genetic effects for 4 month(weaning) weight were 0.47(all), 0.33(female), 0.28(male) in modelⅠ, 0.38(all), 0.21(female), 0.21(male) in modelⅡ. Heritability estimates for male and female data differed from those for combined data. The estimates became smaller for the body weights at 12 month or later(0.13～0.05). The heritabilities of average daily gain were smaller than those for body weights, but showed that the similar pattern to body weights.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.