• Journal of Distribution Science
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• v.10 no.12
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• pp.35-43
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• 2012

Purpose - This study analyzes the effects of types of leadership on the employees of Italian restaurants, its efficacy, and organizational citizenship behavior, utilizing a causal assessment model. In this study, independent variables such as the type of leadership perceived in the manager or chef by an Italian restaurant's employees, and its efficacy were parameters, and the organizational citizenship behavior and organizational effectiveness were the variables representing the results in the hypothesis. The study aimed to draw implications by verifying the leadership via efficacy and the impact on organizational citizenship behavior of Italian restaurants. Research design, data, methodology - For the purpose of this analysis, specific questionnaire items were configured according to the theory and efficacy of the study. From a questionnaire used in organizational citizenship behavior comprising 22 questions, six were modified to suit the research purpose of this study. The configured questionnaire comprised 5 parts and 40 items. A Likert (Likert) 5-point scale was utilized to measure responses to the questionnaire items from the employees of an Italian restaurant in Seoul who participated in the survey. For data collection, 400 questionnaires were distributed, and 344 collected. Factor analysis and reliability verification were conducted using SPSS18.0 and AMOS18.0. A covariance structure analysis was conducted to test the research hypotheses. Results - Based on the results of the analyses, the summary and suggested implications of the research are as follows: The covariance structure analysis used to analyze the kind of effect transformational and transactional leadership styles in Italian restaurant employees had on self-efficacy, group-efficacy, and organizational citizenship behavior, indicated that among the characteristics of transformational leadership (such as, idealized influence, inspirational motivation, individual consideration, and intellectual stimulation), idealized influence and individual consideration had a positive influence on self-efficacy. Idealized influence, individual consideration, conditional reward, and management by exception also positively influenced self-efficacy and altruistic and conscientious behavior (organizational citizenship behavior). Conclusions - Results suggest that with regard to self-efficacy and group efficacy, managers in different departments and chefs should provide team members with a vision for the future, increase their confidence in their abilities, and build their trust in the organization. By evaluating employee performance and experiences, management can demonstrate leadership and encourage organizational citizenship behavior through enjoyable, voluntary participation. Transformational and transactional leadership is effective in group processes that include social-exchange relationships, self-efficacy and group efficacy, and organizational citizenship behavior. However, as this research study utilizes only self-reported data, it has several limitations, such as a vulnerability of errors caused by the various experiment types. A significant limitation of this study is the lack of potential for the duplication of results. The covariance structure analysis, however, provides complementation to limit the impact of errors from self-reporting studies. A future study can extend this research by utilizing different data collection methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.242-252
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• 2016

This study seeks to verify the effect size of the variables relating to job satisfaction through a meta-analysis focusing on previous papers over the last 20 years that have examined the job satisfaction of kindergarten teachers and verified the trend by publication year through a meta-regression analysis. The study results are as follows. First, the results demonstrated that the total effect size of the variables relating to kindergarten teachers was large. Second, the organization's internal variable and organization's external variable groups, except for the individual characteristics variable group, showed a large effect size at similar levels. Third, the status of teachers in the individual characteristics variable group showed a meaningfully large effect size and the interpersonal relationship variable in organization's internal variable group showed a relatively larger effect size than the organizational climate variable did. Finally, the results of the verification of the trend by publication year showed that job satisfaction increased for all variables, except for the individual characteristics variable in more recent publications. This study highlights the imperative of making every effort to improve the organization's internal and external environment. In particular, measures to reduce job stress and work load are required. Furthermore, the atmosphere that is devoted to the teaching profession and teacher efficacy is important.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.461-470
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• 2020

This study analyzed the determinants of land compensation costs using the CALS (Continuous Acquisition & Life-Cycle Support) system to generate data for the construction (planning, design, building, management) process. For analysis, variables used in the related research on land costs were used, which included eight variables (Land Area, Individual Public Land Price, Appraisal & Assessment, Land Category, Use District 1, Terrain Elevation, Terrain Shape, and Road). Also, the variables were analyzed using the machine learning-based Xgboost algorithm. Individual Public Land Price was identified as the most important variable in determining land cost. We used a linear multiple regression analysis to verify the determinants of land compensation. For this verification, the dependent variable included was the Individual Public Land Price, and the independent variables were the numeric variable (Land Area) and factor variables (Land Category, Use District 1, Terrain Elevation, Terrain Shape, Road). This study found that the significant variables were Land Category, Use District 1, and Road.

• Korean Journal of Cognitive Science
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• v.17 no.2
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• pp.75-102
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• 2006

Three experiments were conducted using a verification task to examine good and poor readers' generation of causal inferences(with because sentences) and contrastive inferences(with although sentences). The unfamiliar, critical verification statement was either explicitly mentioned or was implied. In Experiment 1, both good and poor readers responded accurately to the critical statement, suggesting that both groups had the linguistic knowledge necessary to the required inferences. Differences were found, however, in the groups' verification latencies. Poor, but not good, readers responded faster to explicit than to implicit verification statements for both because and although sentences. In Experiment 2, poor readers were induced to generate causal inferences for the because experimental sentences by including fillers that were apparently counterfactual unless a causal inference was made. In Experiment 3, poor readers were induced to generate contrastive inferences for the although sentences by including fillers that could only be resolved by making a contrastive inference. Verification latencies for the critical statements showed that poor readers made causal inferences in Experiment 2 and contrastive inferences in Experiment 3 doting comprehension. These results were discussed in terms of context effect: Specific encoding operations performed on anomaly backgrounded in another passage would form part of the context that guides the ongoing activity in processing potentially relevant subsequent text.

• Journal of Animal Science and Technology
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• v.45 no.2
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• pp.191-198
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• 2003

This study was carried out to investigate a usefulness of the microsatellite DNA markers for individual identification and parentage verification in three dog breeds. A total of 59 random dog (31 Chiwawa, 20 Poongsan, 8 Labrador Retriever) samples were genotyped by using 14 markers (Chiwawa dog), 16 markers (Poongsan dog), and 12 markers (Labrador Retriever dog) among the 17 international standard markers (PEZ1, 3, 5, 6, 8, 10, 11, 12, 13, 15, 16, 17, 20, 21, FHC2010, FHC2054 and FHC2079), respectively. The number of alleles per locus varied from 4 to 14 with a mean value of 6.07 in Chiwawa dog, 2 to 9 with a mean of 4.75 in Poongsan dog, and 3 to 5 with a mean of 4.00 in Labrador Retriever dog. Observed heterozygosity was ranged 0.419${\sim}$0.968 (mean 0.755), 0.300${\sim}$0.950 (mean 0.597) and 0.125${\sim}$0.750 (mean 0.604), and expected heterozygosity was ranged 0.432${\sim}$0.883 (mean 0.711), 0.262${\sim}$0.817 (mean 0.559) and 0.425${\sim}$0.808 (mean 0.660) in these three dog breeds. PIC value was ranged 0.397${\sim}$0.856 (mean 0.659), 0.222${\sim}$0.772 (mean 0.503) and 0.354${\sim}$0.717 (mean 0.563) in these three dog breeds. Of the 17 markers, PEZ1, PEZ3, PEZ6, PEZ10, PEZ12 loci, PEZ1, PEZ6, PEZ13 loci, and PEZ8, PEZ12 loci have relatively high PIC value (＞0.7) in Chiwawa dog, Poongsan dog and Labrador Retriever dog, respectively. The exclusion probability was ranged 0.240${\sim}$0.741, 0.111${\sim}$0.616, and 0.198${\sim}$0.529, and the combination of microsatellite loci was 0.9999, 0.9991, and 0.9968 in Chiwawa dog, Poongsan dog and Labrador Retriever dog, respectively. These results can give basic information for developing parentage verification and individual identification system in these three dog breeds.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.1-15
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• 2006

In Korea, a resident registration card has various personal information such as a present address, a resident registration number, a face picture and a fingerprint. A plastic-type resident card currently used is easy to forge or alter and tricks of forgery grow to be high-degree as time goes on. So, whether a resident card is forged or not is difficult to judge by only an examination with the naked eye. This paper proposed an automatic recognition method of a resident card which recognizes a resident registration number by using a refined ART2-based RBF network newly proposed and authenticates a face picture by a template image matching method. The proposed method, first, extracts areas including a resident registration number and the date of issue from a resident card image by applying Sobel masking, median filtering and horizontal smearing operations to the image in turn. To improve the extraction of individual codes from extracted areas, the original image is binarized by using a high-frequency passing filter and CDM masking is applied to the binaried image fur making image information of individual codes better. Lastly, individual codes, which are targets of recognition, are extracted by applying 4-directional contour tracking algorithm to extracted areas in the binarized image. And this paper proposed a refined ART2-based RBF network to recognize individual codes, which applies ART2 as the loaming structure of the middle layer and dynamicaly adjusts a teaming rate in the teaming of the middle and the output layers by using a fuzzy control method to improve the performance of teaming. Also, for the precise judgement of forgey of a resident card, the proposed method supports a face authentication by using a face template database and a template image matching method. For performance evaluation of the proposed method, this paper maked metamorphoses of an original image of resident card such as a forgey of face picture, an addition of noise, variations of contrast variations of intensity and image blurring, and applied these images with original images to experiments. The results of experiment showed that the proposed method is excellent in the recognition of individual codes and the face authentication fur the automatic recognition of a resident card.

• Journal of Animal Science and Technology
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• v.53 no.1
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• pp.29-34
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• 2011

Using the materials collected from nine farms in a three-way cross system to produce commercial pigs produced from F1 sows (Landrace $\times$ Large White) $\times$ Duroc, the power of individual discrimination and parentage of the 13 microsatellite (MS) marker set that has been suggested for individual/brand identification (traceability) was empirically tested. Initially, genotypes of the parental population ($F_1$ sows and Duroc), and commercial pigs were determined and the genotype frequency and polymorphic index were estimated using the Cervus 2.0 program. The probability of identity among genotypes of random individuals, that random half sibs and that of full sib individuals, based on the genotypes from 91 $F_1$ sows and Duroc were expected to be $4.94{\times}10^{-34}$, $8.16{\times}10^{-23}$ and $2.01{\times}10^{-08}$, respectively, using the API-CALC version 1.0 program. When commercial pigs were included, the estimates increased to $3.74{\times}10^{-35}$, $5.48{\times}10^{-25}$ and $2.96{\times}10^{-11}$, respectively. For the empirical verification of the estimated powers of individual discrimination and parentage, the parentage test was performed for 452 commercial pigs using PAPA version 2.0, and individuals with the same genotype were investigated using the Cervus version 2.0 program. Parents for all commercial pigs were successfully estimated and no identical individual was identified in the pedigree. Although the individual discriminating power was not fully verified because of the lack of individuals corresponding with the theoretical power, the 100% efficiency of parentage test was clearly confirmed. Therefore, we believe that the 13 MS marker set in conjunction with management record/information for the pig production kept in a farm/brand should be useful in the pork traceability in a brand unit.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.397-406
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• 2019

The purpose of this study was to develop the evaluation scale according to the major selection attribute of culinary major students. For the scale development, 69 items were extracted through theoretical review. After that, a survey was conducted on 73 students who are currently studying culinary majors, and two culinary majors and three culinary major professors conducted in-depth analysis and refining. The questionnaire was conducted from March 18 to March 27, 2019, and the second expert group survey was conducted from August 19 to August 31, 2019 based on Kendall's W-validated mean and standard deviation. The results of this study are as follows. First, 46 properties were derived from the first expert group survey. Second, six factors and 32 attributes were derived through consultation with the second and third expert groups. Thirdly, through the verification of Kendall's W attributes of 32 six factors, verification of consensus on the importance of experts was made, and the final four factors (individual factors, occupational factors, major characteristics factors, and university-related factors) were derived. It was. The results of this study suggest that the final composition of the culinary major selection attribute is expected to contribute enough to increase student satisfaction, school loyalty, and enrollment rate of students through the measurement scale to grasp the competitiveness of the culinary major.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.794-807
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• 2022

Although registration of dogs is mandatory according to the revision of the Animal Protection Act, the registration rate is low due to the inconvenience of the current registration method. In this paper, a performance improvement study was conducted on the dog face recognition technology, which is being reviewed as a new registration method. Through deep learning learning, an embedding vector for facial recognition of a dog was created and a method for identifying each dog individual was experimented. We built a dog image dataset for deep learning learning and experimented with InceptionNet and ResNet-50 as backbone networks. It was learned by the triplet loss method, and the experiments were divided into face verification and face recognition. In the ResNet-50-based model, it was possible to obtain the best facial verification performance of 93.46%, and in the face recognition test, the highest performance of 91.44% was obtained in rank-5, respectively. The experimental methods and results presented in this paper can be used in various fields, such as checking whether a dog is registered or not, and checking an object at a dog access facility.