• Journal of KIISE:Computer Systems and Theory
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• v.26 no.6
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• pp.627-635
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• 1999

물체의 3차원 모델을 음영처리하기 위해서는 물체 표면의 각 점에서 법선 벡터를 계산해야 한다. 복섹 기반의 볼륨 데이터는 표면에 대한 기하학적 정보가 없기 때문에 이웃 점들의 상대적인 위치나 데이터 값의 차이로부터 법선 벡터를 추정할 수 밖에 없다. 기존에 고안된 법선 벡터추정 연산자는크기가 고정되어 있기 때문에 제한된 영역에서만 법선 벡터를 정확하게 계산하고 나머지 영역에서는 오류를 유발한다. 이 논문에서는 표면을 구성하는 점들의 공간적 배치나 그 점들의 데이터값에 따라 크기가 변하는 가변 중심 편차 연산자와 이를 이용한 법선 벡터 추정 방법을 제안한다. 이 연산자를 사용하면 기존연산자들보다 정확하게 법선 벡터를 추정할 수 있으며, 동일한 화질인 경우 계산 시간이 상당히 단축된다.

• Journal of The Korean Association For Science Education
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• v.33 no.2
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• pp.486-500
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• 2013

This study is conducted to examine how epistemic reasoning and argument structures of students vary according to data sources used in the process of argumentation implemented in the context of inquiry. To this end, three argument tasks using first-hand data and three argument tasks using second-hand data were developed and applied to the unit on 'Nutrition of Plants' for first year middle school students. According to the results of this study, epistemic reasoning of students manifested during the process of argumentation and varied according to data sources. While most students composed explanations with phenomenon-based or relation-based reasoning in argumentation using first-hand data, all the small groups composed explanations that included model-based reasoning in argumentation using second-hand data. In the case of arguments including phenomenon-based or relation-based reasoning, students described only observable characteristics, with warrants omitted from arguments in many cases. On the other hand, in the case of arguments that included model-based reasoning, explanations were composed by combining the results of observations with theoretical knowledge, with warrants more apparent in their arguments.

• KNOM Review
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• v.22 no.2
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• pp.29-38
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• 2019

The rapid growth of IoT (Internet of Things) owing to the advancement and spread of technologies such as wireless networks, communication modules, sensors, smart terminals, etc. enables the development of new services in diverse public and private sectors. In particular, research on IoT technology and its applications has increased in the field of science. To establish an IoT infrastructure in this field, KREONET launched the wireless IoT network, called ScienceLoRa, based on low power wide area network (LPWAN). ScienceLoRa aims to collect a variety of data from sensors and utilize and analyze the collected data for research in a variety of scientific fields. In this article, the authors present the concept, current status, applications and future plans of ScienceLoRa.

• Journal of The Korean Association For Science Education
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• v.40 no.6
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• pp.657-670
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• 2020

The knowledge-information-processing competency is the most essential competency in a knowledge-information-based society and is the most fundamental competency in the new problem-solving ability. Data-driven science inquiry, which emphasizes how to find and solve problems using vast amounts of data and information, is a way to cultivate the problem-solving ability in a knowledge-information-based society. Therefore, this study aims to develop a teaching-learning model and strategy for data-driven science inquiry and to verify the validity of the model in terms of knowledge information processing competency. This study is developmental research. Based on literature, the initial model and strategy were developed, and the final model and teaching strategy were completed by securing external validity through on-site application and internal validity through expert advice. The development principle of the inquiry model is the literature study on science inquiry, data science, and a statistical problem-solving model based on resource-based learning theory, which is known to be effective for the knowledge-information-processing competency and critical thinking. This model is titled "Exploratory Scientific Data Analysis" The model consisted of selecting tools, collecting and analyzing data, finding problems and exploring problems. The teaching strategy is composed of seven principles necessary for each stage of the model, and is divided into instructional strategies and guidelines for environment composition. The development of the ESDA inquiry model and teaching strategy is not easy to generalize to the whole school level because the sample was not large, and research was qualitative. While this study has a limitation that a quantitative study over large number of students could not be carried out, it has significance that practical model and strategy was developed by approaching the knowledge-information-processing competency with respect of science inquiry.

• Journal of The Korean Association For Science Education
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• v.39 no.2
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• pp.307-320
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• 2019

This study explores how an elementary school homeroom teacher who continued to lecture, can use diversified science teaching methods for learner-centered instruction. Using an auto-ethnographic approach over the course of a year, self-memory data, facebook diaries, class diaries, and interview data of an elementary teacher's day-to-day preparations and practice of elementary science, in the context of a Korean elementary school, were collected. The data was analyzed through cultural historical activity theory, examining how the interplay of key elements (i.e., the subject as a homeroom teacher with instructional expertise, norms, community, division of labor, tools, and goals) was characterized within and across distinct two-activity systems, and how these elements shaped the teacher's teaching methods into either lecture format or diversified teaching. The study revealed that a non-cooperative community, lack of division of labor, and norms that neglect preparation for science class were the elements that perpetuated the lecture format, and that a contradiction between goals and tools occurred in the activity system. However, these elements were able to be transformed into a cooperative community, shared labor, and norms that saved preparation time for both science class and diversified teaching methods, and those changed elements facilitated the teacher in using diversified teaching methods (e.g., experiments, subject-integrated classes, field work), thereby mitigating the contradiction. This study also discusses that diversified teaching methods can be facilitated when dealing with norms, community, and division of labor elements in an elementary school context as well as improving individual teachers' instructional expertise.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.3
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• pp.223-231
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• 2022

Spectroscopies are the most widely used for understanding the crystallographic, chemical, and physical aspects of materials; therefore, numerous commercial and non-commercial software have been introduced to help researchers better handling their spectroscopic data. However, not many researchers, especially early-stage ones, have a proper background knowledge on the choice of fitting functions and a technique for actual fitting, although the essence of such data analysis is peak fitting. In this regard, we present a practical guide for peak fitting for data analysis. We start with a basic-level theoretical background why and how a certain protocol for peak fitting works, followed by a step-by-step visualized demonstration how an actual fitting is performed. We expect that this contribution is sure to help many active researchers in the discipline of materials science better handle their spectroscopic data.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.23-30
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• 2015

As BigData is issued, many applications that operate based on the results of data analysis have been developed, typically applications are products recommend service of e-commerce application service system, search service on the search engine service and friend list recommend system of social network service. In this paper, we suggests a decision frequent pattern tree that is combined the origin frequent pattern tree that is mining similar pattern to appear in the data set of the existing data mining techniques and decision tree based on the theory of computer science. The decision frequent pattern tree algorithm improves about problem of frequent pattern tree that have to make some a lot's pattern so it is to hard to analyze about data. We also proposes to model for a Mapredue framework that is a programming model to help to operate in distributed environment.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.3
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• pp.161-171
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• 2010

As embedded systems adopt monolithic kernels, NAND flash memory is used for swap space of virtual memory systems. While flash memory has the advantages of low-power consumption, shock-resistance and non-volatility, it requires garbage collections due to its erase-before-write characteristic. The efficiency of garbage collection scheme largely affects the performance of flash memory. This paper proposes a novel garbage collection technique which exploits data redundancy between the main memory and flash memory in flash memory-based virtual memory systems. The proposed scheme takes the locality of data into consideration to minimize the garbage collection overhead. Experimental results demonstrate that the proposed garbage collection scheme improves performance by 37% on average compared to previous schemes.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.3
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• pp.75-83
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• 2016

There are various types of disasters now, and accordingly it is practically difficult to manage all types of disasters effectively. If we are able to reconstruct the disaster event and investigate the cause, it may be possible to prepare the recurrence of similar patterns of disasters. The vehicle-based system equipped with state-of-the-art sensors has been proposed in order to reconstruct the disaster site as much as possible and help the disaster investigator to analyze the cause of the disaster by providing high-quality information. However, the data quality obtained from the sensors can be lowered due to unpredictable circumstances of disaster site. In this aspect it is essential to provide practical procedures that assess and analyze the performance of the equipment on site. In this paper, we selected critical elements of performance that can evaluate the vehicle-based image acquisition system, since it is the most critical piece of information in the disaster sites. The quality of the images obtained from vehicle-based image system was analyzed and verified on the test site. From the results of spatial resolution based on GRD(Ground Resolved Distance), we were able to identify maximum 5mm of spatial resolution at a distance of 70m distance. The result of field test is expected to be used for data acquisition plan in future disaster situations.