• Journal of Veterinary Science
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• v.25 no.2
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• pp.29.1-29.11
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• 2024

Background: Preservation of biological tissues has been used since ancient times. Regardless of the method employed, tissue preservation is thought to be a vital step in veterinary surgery teaching and learning. Objectives: This study was designed to determine the usability of chemically preserved cadaveric equine heads for surgical teaching in veterinary medicine. Methods: Six cadaveric equine heads were collected immediately after death or euthanasia and frozen until fixation. Fixation was achieved by using a hypertonic solution consisting of sodium chloride, sodium nitrite and sodium nitrate, and an alcoholic solution containing ethanol and glycerin. Chemically preserved specimens were stored at low temperatures (2℃ to 6℃) in a conventional refrigerator. The specimens were submitted to gross and organoleptic assessment right after fixative solution injection (D0) and within 10, 20, and 30 days of fixation (D10, D20, and D30, respectively). Samples of tissue from skin, tongue, oral vestibule, and masseter muscle were collected for histological evaluation at the same time points. Results: Physical and organoleptic assessments revealed excellent specimen quality (mean scores higher than 4 on a 5-point scale) in most cases. In some specimens, lower scores (3) were assigned to the range of mouth opening, particularly on D0 and D10. A reduced the range of mouth opening may be a limiting factor in teaching activities involving structures located in the oral cavity. Conclusions: The excellent physical, histologic, and organoleptic characteristics of the specimens in this sample support their usability in teaching within the time frame considered. Appropriate physical and organoleptic characteristics (color, texture, odor, and flexibility) of the specimens in this study support the use of the method described for preparation of reusable anatomical specimens.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.285-294
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• 2024

This study aims to enhance learners' creative and integrative thinking through the use of a practical music education program, facilitating high-quality artistic activities and the integration of various disciplines. To achieve this, a practical music education program incorporating the PDIE model was designed, and the content validity of the developed program was verified. Through this process, We have researched and described methodologies for multidisciplinary research that can be applied in practical music education. This paper focuses on the fourth session of the study, which deals with the creative and integrative education of practical music and mathematics. The mathematical theory of interest in this research is the Fibonacci sequence, fundamental to the golden ratio in art. The goal is to enable balanced and high-quality creative activities through learning and applying the Fibonacci sequence. Additionally, to verify the validity and effectiveness of the instructional plan, including the one used in the 15-week course, we have detailed the participants involved in the content validation, the procedures of the research, the research tools used, and the methods for collecting and analyzing various data. Through this, We have confirmed the potential of creative and integrative education in higher practical music education and sought to develop educational methodologies for cultivating various creative talents in subsequent research.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.99-107
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• 2024

While the demand for extrapolating video content horizontally or vertically is increasing, even the most advanced techniques cannot successfully extrapolate all videos. Therefore, it is important to determine if a given video can be well extrapolated before attempting the actual extrapolation. This can help avoid wasting computing resources. This paper proposes a video classifier that can identify if a video is suitable for horizontal extrapolation. The classifier utilizes optical flow and an adaptive Gaussian blur network, which can be applied to flow-based video extrapolation methods. The labeling for training was rigorously conducted through user tests and quantitative evaluations. As a result of learning from this labeled dataset, a network was developed to determine the extrapolation capability of a given video. The proposed classifier achieved much more accurate classification performance than methods that simply use the original video or fixed blur alone by effectively capturing the characteristics of the video through optical flow and adaptive Gaussian blur network. This classifier can be utilized in various fields in conjunction with automatic video extrapolation techniques for immersive viewing experiences.

• Journal of Korean Elementary Science Education
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• v.43 no.4
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• pp.461-475
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• 2024

In this study, we analyzed the self-assessments presented in the matter units of third grade science textbooks, according to the 2015 Revised National Curriculum. The analytical framework, comprising the 'position,' 'subject and content,' 'response form,' 'scoring criteria,' 'reference,' 'creation of assessment criteria,' and 'feedback,' was developed based on previous studies. We analyzed the science textbooks from seven publishers. The results revealed that self-assessments were primarily conducted in the 'closing' section of textbooks, showing a similar pattern to traditional result-oriented assessments. However, some textbooks presented the self-assessments in the 'body' and 'introduction' sections. The subjects of the self-assessments were mostly students, with a relatively balanced assessment of students' 'knowledge,' 'competency,' and 'attitude.' The most common 'response form' was 'rating,' although there was a tendency for the textbooks to standardize the response forms to either 'rating' or 'checklist.' Furthermore, none of the textbooks provided 'scoring criteria' or 'references' for the self-assessments. Only some textbooks used 'feedback' in a limited manner. Additionally, the 'creation of assessment criteria,' which was emphasized in the curriculum, was not presented at all. Based on the results of this study, we discussed strategies to improve the self-assessments presented in the science textbooks and implications for the effective use of self-assessments.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.169-178
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• 2020

The reductions of troops/human resources, and improvement in combat power have made Korean Department of Defense actively adapt 4th Industrial Revolution technology (Artificial Intelligence, Big Data). The defense information system has been developed in various ways according to the task and the uniqueness of each military. In order to take full advantage of the 4th Industrial Revolution technology, it is necessary to improve the closed defense datamanagement system.However, the establishment and usage of data standards in all information systems for the utilization of defense big data and artificial intelligence has limitations due to security issues, business characteristics of each military, anddifficulty in standardizing large-scale systems. Based on the interworking requirements of each system, data sharing is limited through direct linkage through interoperability agreement between systems. In order to implement smart defense using the 4th Industrial Revolution technology, it is urgent to prepare a system that can share defense data and make good use of it. To technically support the defense, it is critical to develop Multi Repository Meta-Data Management (MRMM) that supports systematic standard management of defense data that manages enterprise standard and standard mapping for each system and promotes data interoperability through linkage between standards which obeys the Defense Interoperability Management Development Guidelines. We introduced MRMM, and implemented by using vocabulary similarity using machine learning and statistical approach. Based on MRMM, We expect to simplify the standardization integration of all military databases using artificial intelligence and bigdata. This will lead to huge reduction of defense budget while increasing combat power for implementing smart defense.

• Journal of Korean Home Economics Education Association
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• v.18 no.3 s.41
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• pp.97-117
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• 2006

The purpose of this study was to investigate the teachers' recognition on the optimization of the educational contents of Clothing & Textiles in subjects of :he Practical Arts or the Technology & Home Economics in the course of elementary, middle and high schools. The statistical data for this research were collected from 203 questionnaires of teachers who work on elementary, middle and high schools. Mean. standard deviation, percentage were calculated using SPSS/WIN 12.0 program. Also. these materials were verified by t-test, One-way ANOVA and post verification Duncan. The results were as follows; First, The equipment ratio of practice laboratory were about 24% and very poor in elementary schools but those of middle and high school were 97% and 78% each and higher than elementary schools. Second, More than 50% of teachers recognized the amount of learning 'proper'. The elementary school teachers recognized the mount of learning in 'operating sewing machines' too heavy especially, the same as middle school teachers in 'making shorts': the same as high school teachers in 'making tablecloth and curtain' and 'making pillow cover or bag'. Third, All of the elementary, middle and high school teachers recognized the levels of total contents of clothing and textiles 'common'. The 80% of elementary school teachers recognized 'operating sewing machines' and 'making cushions' difficult especially. The same as middle school teachers in 'hand knitting handbag by crochet hoop needle', 'the various kinds of cloth' and 'making short pants'. The same as high school teachers in 'making tablecloth or curtain'. Fourth, Elementary school teachers recognized 'practicing basic hand needlework' and 'making pouch using hand needlework' important in the degree of educational contents importance. Middle school teachers recognized 'making short pants unimportant. High school teachers considered the contents focusing on practice such as 'making tablecloth and curtain' and 'making pillow cover or bags' unimportant. My suggestions were as follows; Both laboratories and facilities for practice should be established for making clothing and textiles lessons effective in Practical Arts in elementary schools. The 'operating sewing machines' which were considered difficult should be dealt in upper grade, re-conditioning to easier or omitted. The practical contents should be changed to student-activity-oriented and should be recomposed in order to familiar with students' living. It was needed to various and sufficient supports for increasing the teachers' practical abilities.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• Korean Journal of Culture and Social Issue
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• v.12 no.5_spc
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• pp.81-98
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• 2006

The objective of this research is to do a cultural comparison on the daily lives of the children of Korea, Japan and China. To achieve this objective, the questionnares were distributed to the 2940 mothers of children from the ages of 3 to 6 in the countries of Korea, Japan and China. The target audience consisted of 941 mothers living in Seoul and Kyunggi area for Korea, 1007 mothers living in Tokyo for Japan, and 992 mothers living in Beijing for China. As a result of the research, we found out that firstly, although children in general got up anytime between 7:00am to 9:00am and went to bed between 8:00pm and 11:00pm, 61.5% of the Korean children went to bed after 10pm and 16.8% after 11pm. Besides that, we found that compared to 3.51% of Korean children who got up before 6am, 13.41% of Japanese children and 17.24% of Chinese children got up before 6:00am. So we could see that the Korean children got up later and went to bed later than their Japanese and Chinese counterpart. This pattern could also be seen in the average rising time and bed time. Korean children went to bed at 10:00pm and woke up at 7:75am whereas the Japanese children went to bed at 9:28pm and woke up at 7:39am, and the Chinese children went to bed at 9:05pm and woke up at 7:05am. The average sleeping hours for Japanese children was 10.12 hours, 9.50 hours for the Chinese and 9.75 hours for the Korean. As a result, we could see that the Korean children went to bed later, got up later and slept fewer hours than their Japanese and Chinese counterparts. Also, since the rising time and bedtime of the Korean children was later than those of the Chinese and Japanese counterparts, the former s' breakfast and dinner time was also much later. Secondly, we looked at the time children went off to and came back from institutes such as kindergarten and child care centers. The Chinese were earliest at going with average attendance at 7:83am, the Japanese came next at 8:59am and the Korean children were last at 8:90am, whereas the Japanese came first in coming back home at 3:36pm, Korean next at 3:91pm and the Chinese last at 5:46pm. Next when we looked at the hours spent at the kindergartens and child care centers, Japan spent 6.76 hours, Korea 7.01 hours and China spent the longest hours with 9.63 hours. Excluding China where all preschool institutes are centralized into kindergartens, we nest looked at time children went to and came back from the institutes as well as the time spent there. In the case of kindergarten, there was not much difference but in the case of child care centers, the Japanese children went to the child care centers mach earlier and came home later than the Korean children. Also, the time spent at the child care center was much longer for the Japanese than the Korean children. This fact coincides with the Korean mothers' number one wish to the kindergartens and child care centers i.e. for the institutes to prolong their school hours. Thus, the time spent at child care centers for Korea was 7.75 hours, 9.39 hours for Japan and 9.63 hours for China. The time for Korea was comparatively much shorter than that of Japan and China but if we consider the fact that 50% of the target audience was working mothers, we could easily presume that the working parents who usually use the child care centers would want the child care centers to prolong the hours looked after their children. Besides this, the next most wanted wish mothers have towards the child care centers and kindergartens was for those institutes to "look after their children when sick". This item showed high marks in all three countries, and the marks in Korea was especially higher when compared to Japan and China. Thirdly, we looked at the private extracurricular activities of the children. We found that 72.6% of the Korean children, 61.7% of the Japanese children, and 64.6% of the Chinese children were doing private extracurricular activities after attending kindergarten or day care centers. Amongst the private extracurricular activities done by Korean children, the most popular one was worksheet with 51.9% of the children doing it. Drawing (15.20%) and English (11.6%) came next. Swimming (21.95%) was the most popular activity for Japan, with English (17.48%), music (15,79%) and sports (14.70%) coming next. For China, art (30.95%) was first with English (22.08%) and music (19.96%) following next. All three countries had English as the most popular activity related to art and physical activities after school hours, but the rate for worksheet studies was much higher for Korea compared to Japan China. The reason Koreans universally use worksheet in because the parents who buy the worksheet are mothers who have easy access to advertisement or salespeople selling those products. The price is also relatively cheap, the worksheet helps the children to grow the basic learning ability in preparation for elementary school, and it is thought to help the children to build the habit of studying everyday. Not only that but it is estimated that the worksheet education is being conducted because parents can share the responsibility of the children's learning with the worksheet-teacher who make home visits. Looking at the expenses spent on private extracurricular activities as compared to income, we found that China spent 5% of income for activities outside of regular education, Korea 3% and Japan 2%. Fourthly, we looked at the amount of time children spent on using multimedia. The majority of the children in Korea, Japan and China watch television almost every day. In terms of video games, the Japanese children played the games the most, with Korea and China following next. The Korean children used the computer the most, with Japan and China next. The Korean children used about 21.17% of their daily time on computers which is much more than the Japanese who used 20.62% of their time 3 or 4 times a week, or the Chinese. The Chinese children were found to use considerably less time on multimedia compared to the Korean of Japanese.