• Journal of Life Science
• /
• v.23 no.11
• /
• pp.1325-1335
• /
• 2013

Working dogs, such as rescue dogs, military watch dogs, guide dogs, and search dogs, are selected by in-training examination of desired traits, including concentration, possessiveness, and boldness. In recent years, genetic information has been considered to be an important factor for the outstanding abilities of working dogs. To characterize the molecular features of the canine genes related to phenotypes for working dogs, we investigated the 24 previously reported genes (AR, BDNF, DAT, DBH, DGCR2, DRD4, MAOA, MAOB, SLC6A4, TH, TPH2, IFT88, KCNA3, TBR2, TRKB, ACE, GNB1, MSTN, PLCL1, SLC25A22, WFIKKN2, APOE, GRIN2B, and PIK3CG) that were categorized to personality, olfactory sense, and athletic/learning ability. We analyzed the chromosomal location, gene-gene interactions, Gene Ontology, and expression patterns of these genes using bioinformatic tools. In addition, variable numbers of tandem repeat (VNTR) or microsatellite (MS) polymorphism in the AR, MAOA, MAOB, TH, DAT, DBH, and DRD4 genes were reviewed. Taken together, we suggest that the genetic background of the canine genes associated with various working dog behaviors and skill performance attributes could be used for proper selection of superior working dogs.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.18 no.4
• /
• pp.234-265
• /
• 2013

Oceanic current maps in the secondary school science and earth science textbooks have played an important role in piquing students's inquisitiveness and interests in the ocean. Such maps can provide students with important opportunities to learn about oceanic currents relevant to abrupt climate change and global energy balance issues. Nevertheless, serious and diverse errors in these secondary school oceanic current maps have been discovered upon comparison with up-to-date scientific knowledge concerning oceanic currents. This study presents the fundamental methods and strategies for constructing such maps error-free, through the unification of the diverse current maps currently in the textbooks. In order to do so, we analyzed the maps found in 27 different textbooks and compared them with other up-to-date maps found in scientific journals, and developed a mapping technique for extracting digitalized quantitative information on warm and cold currents in the East Sea. We devised analysis items for the current visualization in relation to the branching features of the Tsushima Warm Current (TWC) in the Korea Strait. These analysis items include: its nearshore and offshore branches, the northern limit and distance from the coast of the East Korea Warm Current, outflow features of the TWC near the Tsugaru and Soya Straits and their returning currents, and flow patterns of the Liman Cold Current and the North Korea Cold Current. The first draft of the current map was constructed based upon the scientific knowledge and input of oceanographers based on oceanic in-situ measurements, and was corrected with the help of a questionnaire survey to the members of an oceanographic society. In addition, diverse comments have been collected from a special session of the 2013 spring meeting of the Korean Oceanographic Society to assist in the construction of an accurate current map of the East Sea which has been corrected repeatedly through in-depth discussions with oceanographers. Finally, we have obtained constructive comments and evaluations of the interim version of the current map from several well-known ocean current experts and incorporated their input to complete the map's final version. To avoid errors in the production of oceanic current maps in future textbooks, we provide the geolocation information (latitude and longitude) of the currents by digitalizing the map. This study is expected to be the first step towards the completion of an oceanographic current map suitable for secondary school textbooks, and to encourage oceanographers to take more interest in oceanic education.

• Journal of vocational education research
• /
• v.35 no.4
• /
• pp.131-153
• /
• 2016

The purpose of this study was to derive evaluator's role and capability for institution accreditation evaluation of NCS-based vocational competency development training. This study attempted to explore in various ways evaluator's minute roles using Delphi method, and to derive knowledge, skill, attitude and integrity needed to verify the validity. To the end, this study conducted the Delphi research for over three rounds by selecting education training professionals and review evaluation professions as professional panels. From the results, roles of evaluators were defined as the total eight items including operator, moderator-mediator, cooperator, analyzer, verifier, institution evaluator, institution consultant, and learner, and the derived capabilities with respect to each role were 25 items in total. The area of knowledge included four items of capabilities such as HRD knowledge, NCS knowledge, knowledge of vocational competency development training, and knowledge of training institution accreditation evaluation, and the area of skill comprised fourteen items of capabilities such as conflict management ability, interpersonal relation ability, word processing ability, problem-solving ability, analysis ability, pre-preparation ability, time management ability, decision making ability, information comprehension and utilization ability, comprehensive thinking ability, understanding ability of vocational competency development training institutions, communication ability, feedback ability, and core understanding ability. The area of attitude was summarized with the seven items in total including subjectivity and fairness, service mind, sense of calling, ethics, self-development, responsibility, and teamwork. The knowledge, skill and attitude derived from the results of this study may be utilized to design and provide education programs conducive to qualitative and systematic accreditation and assessment to evaluators equipped with essential prerequisites. It is finally expected that this study will be helpful for designing module education programs by ability and for managing evaluator's quality in order to perform pre-service education and in-service education according to evaluator's experience and role.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.1-17
• /
• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.139-161
• /
• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Korean Home Economics Education Association
• /
• v.33 no.3
• /
• pp.85-105
• /
• 2021

The purpose of this study is to develop and implement cooperative consumption education programs using design thinking in middle school home economics education classes to understand the impact on students' cooperative problem solving competency. Accordingly, a cooperative consumption education program based on design thinking was developed according to the ADDIE model, and the evaluation was conducted on a total of 25 students. The results of the study were as follows. First, based on prior research, we developed a consumption education program based on D. school's design thinking process under the theme of 'Creating a Shared School' for the practice of cooperative consumption. As a result of expert validity verification of the teaching/learning course plan and workbook for the eight sessions, the average question was 4.72 (out of 5 points) and the average CVI was 0.93, indicating that the content validity and field suitability were excellent. Second, to summarize the results achieved from the implementation of the cooperative consumption education program, the pre-/post-test using the revised and supplemented cooperative problem-solving competency tool, and the open-ended survey, It was confirmed that the developed program had a significant effect on improving not only the students' knowledge and perceived necessity for cooperative consumption along with the awareness of practice, but also the cooperative problem-solving competency. As a follow-up study, we propose to expand the research to a wider audience, and to further conduct research and develop programs applied with design thinking in home economics curriculum and in consumer competency development. This study confirmed that cooperative consumption education programs using design thinking are effective in improving youth's cooperative problem-solving competency and is meaningful in that they developed consumption education programs under the theme of 'cooperative consumption' in response to changing consumer education needs.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1095-1105
• /
• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Korean Association of Arts Management
• /
• no.53
• /
• pp.127-151
• /
• 2020

In this study, the announced in 2010 was used to identify the flow of social value expansion on the culture and art education and to provide the future direction. In the context of culture and art education, cultural diversity is an important agenda, and thus the case of cultural diversity program by the National Museum of Contemporary Art was analyzed to discuss the social role of culture and art education. To summarize the findings, it is a program of promoting understanding on the diversity of art in the same period and various identifies of the members of our society through the exhibition of «Artist of the Year», and it is a program on cultural diversity of understanding the diversity and identity of various members in our society through the contemporary artwork. Through theme-based approach, education on cultural diversity and cooperative learning were applied to develop the sense of community. Based on the core values of education on cultural diversity, the contents of the education program on performed total of 80 times on 14 programs in 2017-2019 were analyzed. As a result, the analysis showed 9 core values, and the core values of education on cultural diversity derived through the content analysis of the education program on were ① Engagement, ② Conversation, ③ Citizenship, ④ Coexistence, ⑤ Human Rights, ⑥ Difference, ⑦ Equality, ⑧ Cooperation, ⑨ Solidarity. As the space enhancing the cultural engagement, the art museum is a place for realizing the value on cultural diversity, so it has significance as the space that embraces both cultural diversity and culture and art education. In this sense, it was identified that the discussion on the cultural diversity and culture and art education was being expanded into the perspective of various groups and communities in our society through the core values of engagement, conversation, citizenship, coexistence, human rights, difference, equality, cooperation, solidarity and direction, which were provided by the analysis of the contents on the art museum education program on cultural diversity in the National Museum of Contemporary Art.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.57-78
• /
• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.

• Journal of Science Education
• /
• v.47 no.2
• /
• pp.192-210
• /
• 2023

This study was intended to develop the plant molecular biology experimental program using Rapid-cycling Brassica rapa (RcBr) based on the teaching steps and teaching methods of the cognitive apprenticeship model and to determine its application effects. In order to improve a subject's cognitive function and expertise on molecular biology experiments, two themes composed of a total 8 class sessions were selected: 'Identification of DFR gene in purple RcBr and non-purple RcBr' and 'Identification of RcBr's genetic polymorphism site using the DNA profiling method'. Research subjects were 18 pre-service teaching majors in biology education of H University in Chungbuk, Korea. The effectiveness of the developed program was verified by analyzing the enhancement of 'cognitive function' related to the use of molecular biology knowledge and technology, and the enhancement of 'domain-general metacognitive abilities.' The effect of the developed program was also determined by analyzing the task flow diagram provided. The developed program was effective in improving the cognitive functions of the pre-service teachers on the use of knowledge and technology of molecular biology experiments. It was especially effective to improve the higher cognitive function of pre-service teachers who did not have the previous experience. The developed program also showed a significant improvement in the task of metacognitive knowledge and in the planning, checking, and evaluation of metacognitive regulation, which are sub-elements of domain-general metacognitive abilities. It was found that the developed program's self-test activity could help the pre-service teachers to improve their metacognitive regulation. Therefore, this developed program turned out to be helpful for pre-service teachers to develop core competencies needed for molecular biology experimental classes. If the teaching and learning materials of the developed program could be reconstructed and applied to in-service teachers or high school students, it would be expected to improve their metacognitive abilities.