• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.365-373
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• 2013

Tile caching technology is a commonly used method that optimizes the delivery of map imagery across the internet in modern WebGIS systems. However the poor performance of the map tile cache update is one of the major causes that hamper the wider use of this technique for datasets with frequent updates. In this paper, we introduce a new algorithm, namely, Partial Area Cache Update (PACU) that significantly minimizes redundant update of map tiles where the update frequency of source map data is very large. The performance of our algorithm is verified with the cadastral map data of Pyeongtaek of Gyeonggi Province, where approximately 3,100 changes occur in a day among the 331,594 parcels. The experiment results show that the performance of the PACU algorithm is 6.6 times faster than the ESRI ArcGIS SERVER$^{(r)}$. This algorithm significantly contributes in solving the frequent update problem and enable Web Map Tile Services for data that requires frequent update.

• Journal of the Korean Geographical Society
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• v.45 no.1
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• pp.144-164
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• 2010

This study aims to reveal path selection strategies and individual differences in a navigation task. Two experiments were presented that studied human route planning performance as well as the cognitive strategies and processes involved. For the outdoor task, university students were asked to select a route based on the instruction, i.e. to find the best route from the campus main gate to the Education Building for conference visitors by locating eight signposts. Results indicate (1) that locations of signposts were selected preferably at decision points where the traveler needs to make a choice and starting/ending points of the navigation task and (2) a variety of route planning strategies considering efficiency goal (e.g., the shortest path), environmental characteristics (e.g., fewest turns), and aesthetic purpose (e.g., most scenic) were used. It is notable that some participants took into account more than one path by locating one or two signposts on an alternative route while others preferred a linear route connecting signposts between the start point and the destination. Prior to the main experiment, the same participants were asked to complete the same task inside the classroom to investigate changes in strategies between two tasks. Participants often tend to place signposts at more regular intervals for the indoor navigation task than the same task conducted outside.

• Journal of The Korean Association For Science Education
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• v.18 no.2
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• pp.245-256
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• 1998

In this study, we discussed about the implications of the idealization, which take an important role in physics, to the physics education. First, understanding of the idealization help the physics learning itself. This is because that various types of idealizations are included in the physics terms and concepts, derivation processes of physics laws and formulas, and explanation of natural phenomena and problem solving activities. Second, understanding of the idealization can help the application of the physics world to the real world. That is, by understanding the extent and the limit of idealization used in physics world, physics students can understand the discrepancies between the real world and the physics world. And also, by modifying or eliminating the idealization, students can extend the extent of understanding about how predictions based on the idealization used in the physics world will change. To do this, we suggested the application of computer simulation program in physics laboratories. Third, idealization take an important role in the inquiry learning for students' originality. The activities of identifying or controlling the variables, as one of the principal factors of scientific inquiry, need the appropriate establishment of the ideal conditions. And to analyze the limiting case or practice the thought experiments for understanding the impossible situation in the real world, ideal conditions also are needed. This study discussed above three aspects with various concrete examples and, with Park et al.'s study (Park et al., 1998), present the theoretical basis for the study of students' and teachers' understanding the idealization.

• Journal of KIISE
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• v.42 no.4
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• pp.451-458
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• 2015

Previous multimodal learning methods focus on problem-solving aspects, such as image and video search and tagging, rather than on knowledge acquisition via content modeling. In this paper, we propose the Multimodal Concept Hierarchy (MuCH), which is a content modeling method that uses a cartoon video dataset and a character-based subtitle generation method from the learned model. The MuCH model has a multimodal hypernetwork layer, in which the patterns of the words and image patches are represented, and a concept layer, in which each concept variable is represented by a probability distribution of the words and the image patches. The model can learn the characteristics of the characters as concepts from the video subtitles and scene images by using a Bayesian learning method and can also generate character-based subtitles from the learned model if text queries are provided. As an experiment, the MuCH model learned concepts from 'Pororo' cartoon videos with a total of 268 minutes in length and generated character-based subtitles. Finally, we compare the results with those of other multimodal learning models. The Experimental results indicate that given the same text query, our model generates more accurate and more character-specific subtitles than other models.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.245-252
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• 2013

The purpose of this study is to propose the implications after investigating how gifted education affected the school achievements and academic skills of students who have experience of gifted education. In this study, academic skills include academic knowledge, creative problem solving skills, logical thinking, persuasive skills, collaborative skills, self-directed learning skills, communication skills. The survey was conducted with 1,156 science high school and science academy students who have ongoing gifted education experience and depth interviews were also analyzed with some students to gather further in-depth information. As a result, students responded that gifted education affected very positively on knowledge, collaborative skills, communication skills and increased interest in related subjects. On the other hand, it showed lower positive responses on self-directed learning skills and persuasive skills. Also, students replied gifted education did not affect the school achievements but there was an opportunity to learn how to debate, research, and experiment and practice methods. The direction of gifted education to step forward was suggested based on these results. This study can be the basis for revising gifted education curriculum.

• Science of Emotion and Sensibility
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• v.15 no.3
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• pp.367-380
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• 2012

The purpose of this research was to implement and verify an information retrieval(IR) system based on users' relevance criteria for information search tasks. For this purpose, we implemented an IR system with a dynamic ranking model using users' relevance criteria varying with the types of information search task and evaluated this system through user experiment. 45 participants performed three information search tasks on both IR systems with a static and a dynamic ranking model. Three Information search tasks are fact finding search task, problem solving search task and decision making search task. Participants evaluated top five search results on 7 likert scales of relevance. We observed that the IR system with a dynamic ranking model provided more relevant search results compared to the system with a static ranking model. This research has significance in designing IR system for information search tasks, in testing the validity of user-oriented relevance judgement model by implementing an IR system for actual information search tasks and in relating user research to the improvement of an IR system.

• Journal of Software Engineering Society
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• v.24 no.1
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• pp.9-17
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• 2011

Software reuse the concept of developing software by using existing software assets, rather than developing it from scratch. Developers may face difficulties of reusing existing software assets because existing assets are normally developed by other developers for different purposes. Developers tend to seek appropriate knowledge about effectively reusing software assets from the developers who have faced and solved similar problems in reusing software assets previously. In other words, the reuse-related knowledge of domain experts or other developers usually provides important clues to solve reuse-related problems. Such reuse-relalted knowledge can help developers to reduce the time and effort to identify and solve the difficulties and problems that may arise in reusing software assets and in minimizing the risks of reusing them by allowing them to reuse reliable software assets in an appropriate way and by recognizing similar requirements or constraints of resuing the assets. In this paper, we describe a model to represent reuse-related knowledge in a formal way, and explain the architecture and a prototype implementation of Software Reuse Wiki (SRW) that enables collaborative organization and sharing of software reuse-related knowledge. We have conducted an experiment pertaining to problem solving in reusing assets based on reuse-related knowledge. We also discuss about our evaluation plan for showing the benefits and contributions of reuse knowledge representation model and management methods in SRW. We expect that SRW can contribute to facilitate users' participations and make efficient sharing and growing of reuse-related knowledge. In addition, the representation model of reuse-related knowledge and management methods can make developers acquire more reliable and useful reuse-related knowledge in a straightforward manner without spending additional efforts to find solutions to solve reuse-related problems.

• Korean Journal of Environment and Ecology
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• v.33 no.3
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• pp.333-340
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• 2019

As the number of students in the multicultural background grows, the interest in their education is also increasing. The purpose of this study is to investigate the effect of forest healing factors on the improvement of language ability and ego-resilience of students from multicultural families. We conducted an after-school forest healing program of ten male and ten female middle school students of a multicultural preparatory school located in ${\bigcirc}{\bigcirc}$-dong in Cheongju, Chungnam Province. The experiment consisted of a total of 12 weekly one-hour (60 minutes) programs from April 12, 2018 to June 26, 2018. The forest healing program is an activity that uses the various environmental factors that exist in the forest to increase the immunity of the human body and restore physical and mental health. To determine the difference in ego-resilience before and after the program, we conducted a paired t-test and analyzed with the SPSS 18.0 program. The results showed that the ego-resilience significantly improved in all sub-factors including the positive thinking ability, problem-solving ability, intimacy ability, emotional adjustment ability, and autonomic behavior ability (p<.001). The descriptive statistics of the language ability showed the improvement in writing errors, pronunciation errors, sentence errors, tense errors, and errors in research and connection. We expect the results of this study can be used as the basic data to improve ego-resilience and language acquisition ability of middle-entry children and students from multicultural families.

• Journal of The Korean Association For Science Education
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• v.26 no.3
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• pp.393-405
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• 2006

The purpose of this study was to analyze the effects of Semantic Network Program (SNP) instruction on learning achievement and motivation in high school biology classes. For this study, a SNP was designed by applying the recommendations in regard to student attention and satisfaction factors in Keller's ARCS theory. SNP instruction was conducted with an experimental group and a control group, each consisting of 62 high school biology class student. A pretest-posttest control group design was employed. The pre-test was used to analyze the learning achievement test, learning motivation test, and semantic forming test. For 4 weeks the experiment group was instructed using the developed SNP which centered on Keller's attention and satisfaction factors, and the control group was instructed via teacher-centered lectures based on the textbook. It was found that SNP instruction efficiently increased students' biology learning achievement (p<.001). It was also discovered that SNP instruction was effective in increasing Keller's motivation strategies on attention and satisfaction factors (p<.001). In addition, SNP instruction positively affected students' semantic formation (p<.001) and learning content retention (p>.05) in the heredity unit by aiding students in the area of active multimedia learning. An in depth interview with students in the class using SNP instruction showed that material learned via this method in biology had longer retention of problem-solving methods. Consequently, SNP instruction according to motivation strategies may high school biology teachers with meaningful teaching-learning methods strategies for the unit on heredity.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.