• Journal of Gifted/Talented Education
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• v.26 no.2
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• pp.319-346
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• 2016

In this study, by examining the characteristics of boys and girls which would appear in the performance process of Goldberg machine activities, it would be attempted to provide the implications for the development and teaching methods of gifted and talented programs. The object of study was organized into separate 2 groups of boys and girls by each, composed of a total of 16 people among 5th graders of the gifted class in elementary school, located in Gyeonggi province. The final assignment was to make the Goldberg machine in order to have the beads get to the target spot latest, in which the analysis was implemented qualitatively by participating in and observing the performance process of students. After dividing the Goldberg machine activities into the steps of planning, production, outcome, assessment and reflection, their analysis results are as follows: First, in the planning stage, the girls explained minutely the process of Goldberg machine in writing, whereas the boys represented it visually simply. Second, in the production stage, the boys showed the task commitment by trying to realize the machine as designed initially, but the girls showed their appearance to represent it simply and easily. Third, in the sophistication and efficiency of the machine production, the boys were superior to the girls, and in the creativity and diversity of the use of materials, the girls were more excellent. Fourth, in the assessment and reflection, the boys evaluated it individually, and the girls showed their appearance to evaluate it by reflecting others'thinking. Hence, when developing and teaching the gifted and talented programs, it would be required that the teaching and learning contents be recomposed by considering the gender, or that the various class strategies be sought. Further, the broader and more systematic studies, on the performance process of gifted students based on the gender, should be carried out.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• Journal of the Korean Society for information Management
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• v.37 no.1
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• pp.51-78
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• 2020

Recently, the emphasis on self-directed learning and lifelong education is increasing the importance of school libraries in the curriculum. Accordingly, various studies have been conducted mainly from a structural, institutional and operational point of view. However, more research is necessary on the micro topics such as school library users' autonomous intrinsic motivations in the sense that school libraries play key roles in autonomy-based self-directed education. This study aims at finding out what types of school library use motivations are more important and the degree to which the use motivations affect the school library service quality based on the self-determination theory. In addition, this study examines how the use motivations and the perceived service quality vary depending on the school grade of the library users. Based on a focus-group-interview pilot study, a questionnaire survey was administered on the effects of school library motivations on perceived library service quality to 588 students from 5 high schools and 2 middle schools in Seoul. When the service quality and its components, service affect, information control, and library as place were set as dependent variables, in all these four cases, intrinsic motivations were more significant than extrinsic motivations. In addition, when middle school students and high school students were selected as separate analysis target groups, the results of both analyses show that the higher the intrinsic motivations were, the higher the perceived service quality was. The contribution of this study is that it applies the self-determination theory to school library service, measures the influence of motivation type based on the theoretical basis, and focuses on micro aspects to improve school library services.

• Journal of the Korean School Mathematics Society
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• v.18 no.2
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• pp.187-201
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• 2015

The purpose of this study was to examine the effects of using process-based writing poems in the elementary mathematics classrooms. For this study, we chose 128 elementary school students to examine their mathematical achievements and attitude towards mathematics when using process-centered writing poems in the elementary mathematics classrooms. Process-based mathematics and writing programs developed mainly on the geometry units were composed of four levels, idea generation, idea selection, use and idea organization grouped into similar sections in order to separate into two sections. The results of the practice of this study's problem can be summarized as follows. First, the process-based mathematics and writing activity of geometry had a positive impact on academic achievement in mathematics. Although there was not a significant difference in the fourth and fifth grades, significant differences in the fifth and sixth grade were found. Second, in regards to attitudes in mathematics, process-based mathematics and writing activities had a positive impact. In particular, the improvement of mathematical attitudes was evident in all grades. It confirmed the effective facilitation of interest and enjoyment towards learning mathematics by 4th, 5th and 6th graders who had undertaken these mathematics classes.

• Journal of Hospice and Palliative Care
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• v.22 no.4
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• pp.207-218
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• 2019

Purpose: Physician competency in end-of-life (EOL) care is becoming increasingly important. This study investigated the EOL care curricula in Korean medical schools. Methods: Questionnaires were issued to the faculty members responsible for the EOL care curricula at each of the medical schools. These included questions on the structure and content of the curricula, teaching methods, and faculty members' attitudes to the curricula. Results: Characteristics of the EOL care curricula were compiled from 27 (66%) of the 41 medical schools. All of the medical schools taught essential aspects of the EOL care curriculum either as a separate course or embedded within other medical education courses. The mean time spent on EOL care teaching was 10 hrs (range, 2~32 hrs). The most frequently taught topics were delivering bad news (100%) and symptom management (74%). When the palliative care education assessment tool (PEAT) was used to evaluate the curricula, a median of 11 PEAT objectives was met (range, 2~26; maximum, 83). More than two teaching methods were used in most of the curricula. However, lectures were the only teaching method used by three medical schools. 78% of faculty members who were responsible for curriculum reported dissatisfaction with it, whereas 18% believed that the time allotted to it was adequate. Only 7% of these faculty members believed that their students were adequately prepared to practice EOL care. Conclusion: There is a need to improve EOL care education in basic medical curricula and to take a more systematic approach to achieving learning outcomes.

• Korean Journal of Occupational Health Nursing
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• v.17 no.1
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• pp.23-33
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• 2008

Purpose: The purpose of the study was to identify attitudes of middle managers toward employees with psychological problems and to determine factors affecting their attitudes. Methods: A questionnaire with Community Attitudes Toward Mentally Ill (CAMI) scales was administered to 161 middle managers working in small and medium-sized enterprises based in Seoul and Gyeonggi Province. Results: There are four separate subscales on the CAMI. Mean score for authoritarianism was $35.0{\pm}4.4$, benevolence $23.0{\pm}4.8$, social restrictiveness $32.3{\pm}4.9$ and community mental health ideology $27.2{\pm}5.1$ According to multiple regression analysis, middle managers with no experience of learning mental illness through mass media or higher levels of depression symptom were more authoritative and less benevolent towards employees with psychological problems. The experience of meeting a patient with mental problem contribute to positive attitudes toward people with mental illnesses in social restrictiveness subscale and community mental health ideology subscale among CAMI. Conclusion: This study suggests that experience of having patients with mental problems and information on psychological problems will have great influence on attitudes of middle managers toward employees with psychological problems. It might be important to help middle manager manage their depression because their depression also affects their attitudes toward employees with psychological problems.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.246-252
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• 2018

Hand gestures are attracting attention as a NUI (Natural User Interface) of wearable devices such as smart glasses. Recently, to support efficient media consumption in IoT (Internet of Things) and wearable environments, the standardization of IoMT (Internet of Media Things) is in the progress in MPEG. In IoMT, it is assumed that hand gesture detection and recognition are performed on a separate device, and thus provides an interoperable interface between these modules. Meanwhile, deep learning based hand gesture recognition techniques have been recently actively studied to improve the recognition performance. In this paper, we propose a method of hand gesture recognition based on CNN (Convolutional Neural Network) for various applications such as media consumption in wearable devices which is one of the use cases of IoMT. The proposed method detects hand contour from stereo images acquisitioned by smart glasses using depth information and color information, constructs data sets to learn CNN, and then recognizes gestures from input hand contour images. Experimental results show that the proposed method achieves the average 95% hand gesture recognition rate.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.

• Journal of Dental Anesthesia and Pain Medicine
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• v.23 no.2
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• pp.101-110
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• 2023

Background: Dentists make various efforts to reduce patients' anxiety and fear associated with dental treatment. Dental sedation is an advanced method that dentists can perform to reduce patients' anxiety and fear and provide effective dental treatment. However, dental sedation is different from general dental treatment and requires separate learning, and if done incorrectly, can lead to serious complications. Therefore, sedation is performed by a limited number of dentists who have received specific training. This study aimed to investigate the proportion of dentists who practice sedation and the main sedatives they use in the context of the Republic of Korea. Methods: We used the customized health information data provided by the Korean National Health Insurance. We investigated the number of dental hospitals or clinics that claimed insurance for eight main sedatives commonly used in dental sedation from January, 2007 to September, 2019 at the Health Insurance Review and Assessment Service. We also identified the changes in the number of dental medical institutions by region and year and analyzed the number and proportion of dental medical institutions prescribing each sedative. Results: In 2007, 302 dental hospitals prescribed sedatives, and the number increased to 613 in 2019. In 2007, approximately 2.18% of the total 13,796 dental institutions prescribed sedatives, increasing to 3.31% in 2019. In 2007, 168 institutions (55.6%) prescribed N₂O alone, and in 2019, 510 institutions (83.1%) made claims for it. In 2007, 76 (25.1%) hospitals made claims for chloral hydrate, but the number gradually decreased, with only 29 hospitals (4.7%) prescribing it in 2019. Hospitals that prescribed a combination of N₂O, chloral hydrate, and hydroxyzine increased from 27 (8.9%) in 2007 to 51 (9%) in 2017 but decreased to 38 (6.1%) in 2019. The use of a combination of N₂O and midazolam increased from 20 hospitals (6.6%) in 2007 to 51 hospitals (8.3%) in 2019. Conclusion: While there is a critical limitation to the investigation of dental hospitals performing sedation using insurance claims data, namely exclusion of dental clinics providing non-insured treatments, we found that in 2019, approximately 3.31% of the dental clinics were practicing sedation and that N₂O was the most commonly prescribed sedative.

• 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.