• Journal of Educational Research in Mathematics
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• v.20 no.4
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• pp.459-476
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• 2010

The purpose of this study is to suggest a new direction in using LOGO as a gifted education program and to seek an effective approach for LOGO teaching and learning, by analyzing the strategic thinking of mathematically gifted elementary students. This research is exploratory and inquisitive qualitative inquiry, involving observations and analyses of the LOGO Project learning process. Four elementary students were selected and over 12 periods utilizing LOGO programming, data were collected, including screen captures from real learning situations, audio recordings, observation data from lessons involving experiments, and interviews with students. The findings from this research are as follows: First, in LOGO Project Learning, the mathematically gifted elementary students were found to utilize such strategic ways of thinking as inferential thinking in use of prior knowledge and thinking procedures, generalization in use of variables, integrated thinking in use of the integration of various commands, critical thinking involving evaluation of prior commands for problem-solving, progressive thinking involving understanding, and applying the current situation with new viewpoints, and flexible thinking involving the devising of various problem solving skills. Second, the students' debugging in LOGO programming included comparing and constrasting grammatical information of commands, graphic and procedures according to programming types and students' abilities, analytical thinking by breaking down procedures, geometry-analysis reasoning involving analyzing diagrams with errors, visualizing diagrams drawn following procedures, and the empirical reasoning on the relationships between the whole and specifics. In conclusion, the LOGO Project Learning was found to be a program for gifted students set apart from other programs, and an effective way to promote gifted students' higher-level thinking abilities.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.7 no.1
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• pp.128-131
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• 1996

소아들의 음성장애의 대부분은 음성과 기능에 의한 성대의 오용 및 남용으로부터 기인한다. 그러나 음성장애에 대한 인식의 부족으로 인하여 소아의 음성장애 발견율은 극히 낮은 수준에 머므르고 있다. 치료에 있어서도 행동의 통제가 그다지 쉽지 않기 때문에 많은 어려움이 따른다. 본 연구는 소아의 음성장애의 발생경위와 문제의 설명과 차팅에 의한 남용의 감소에 초점을 둔 치료사례이다. 5세 아동의 심한 음성장애로 의뢰되어졌다. 음성증후는 목쉰소리, 기식화된 소리, 일시적인 발성일탈(phonation break) 등이 두드러졌다. 이비인후과 전문의의 스트로보스코프(stroboscope) 검진결과 초기 양측성 성대결절이 성대 앞쪽으로부터 1/3 지점에서 발견되었으며 비지피치(Visi-Pitch)로 측정된 퍼터베이션(perturbation 수치가 16.8이었다. 사례사 조사 과정에서 아동이 활동적이며 자주 고함을 지르고 자동차놀이를 주로하며 많은 시간을 보낸다는 것이 밝혀졌다. 중재 프로그램은 우선 문제를 설명하고 인식시키는 것과 고함을 지르거나 자동차놀이를 하면서 자동차 소음을 흉내내는 등의 음성남용 회수를 감소시키는 것에 초점을 두었다 17회의 치료로 성대결절이 감소되었고 퍼터베이션은 3.8로 저하되었으며 아동 스스로 음성남용을 통제할 수 있다는 임상적 판단하에 치료를 종결하였다. 1개월 후 추후 검사에서 남용적인 습관이 여전히 나타나지 않았으며 음성상태도 양호하였다. 소아 음성장애의 치료는 소아가 이해할 수 있는 용어로 문제의 인식을 확실히 시키고 행동을 체계적으로 통제할 수 있는 치료프로그램을 실행시키는 임상가의 자질이 대단히 중요하다. 그러나 그에 못지 않게, 많은 시간을 아동과 함께 보내는 부모나 교사의 협력이 치료의 성패를 좌우할 수 있다.조구와 처리구간에 차이가 없었다. 그러나 유지방 함량의 경우 대조구가 3.22%, 처리구가 3.37%로 처리구가 0.15% point 증가했으나 유의차는 없었다. Fan ＋ sprinkler가 설치된 시설에서 사육된 젖소에서 생산된 우유내 체세포수는 대조구에 비해 26.63%가 감소되었으나 유의차는 발견할 수 없었다. 이상의 시험 결과에서 볼 때 fan ＋ sprinkler를 이용한 방서 방법은 고온 stress를 완화시켜 유생산성의 증가에 기여할 수 있는 하나의 방법이라고 본다.을 알 수가 있었다.없다. 본 논문의 대상은 바로 이러한 합성 동사성명사의 논항구조와 동사성명사에 의해 하위범주화된 논항들의 문법적 실현양상이다..그 결과 심리적 특성과 사용자 수용 특성은 아바타 수용도에 부분적으로 영향을 미친다는 것 결과가 나타났다.웨어 프로세스 평가와 개선 모델의 개발을 위한 기초적인 자료를 제공할 것으로 예상된다 또한, 본 연구 결과는, 우리나라 소프트웨어 조직들이 실제로 무엇을 필요로 하는지를 밝힘으로써, 우리나라의 소프트웨어 산업을 육성하기 위한 실효성 있는 정책 입안을 위한 기초 자료를 제공할 것으로 예상된다.다.를 검증하려고 한다. 협력체계 확립, ${\circled}3$ 전문인력 확보 및 인력구성 조정, 그리고 ${\circled}4$ 방문보건사업의 강화 등이다., 대사(代謝)와 관계(關係)있음을 시사(示唆)해 주고 있다.ble nutrient (TDN) was highest in booting stage (59.7%); however no significant difference was found among other stages. The concentrations of Ca and P were not

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.31-42
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• 2010

GPM(Generic Product Model) developed by Hitachi in Japan is a common data model to integrate and share life cycle data of nuclear power plants. GPM consists of GPM core model, an abstract model, implementation language for the model and reference library written in the language. GPM core model has a feature that it can construct a semantic network model consisting of relationships among objects. Initial GPM developed and provided GPML as an implementation language to support the feature of the core model, but afterwards the GPML was replaced by GPM-XML based on XML to achieve data interoperability with heterogeneous applications accessing a GPM data model. However, data models written in GPM-XML are insufficient to be used as a semantic network model for lack of studies which support GPM-XML and enable the models to be used as a semantic network model. This paper proposes OWL as the implementation language for GPM core model because OWL can describe ontologies similar to semantic network models and has an abundant supply of technical standards and supporting tools. Also, OWL which can be expressed in terms of RDF/XML based on XML guarantees data interoperability. This paper uses OWL DL, one of three sublanguages of OWL, because it can guarantee complete reasoning and the maximum expressiveness at the same time. The contents of this paper introduce the way how to overcome the difference between GPM and OWL DL, and, base on this way, describe how to convert the reference library written in GPML into ontologies based on OWL DL written in RDF/XML.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Cross-Cultural Studies
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• v.53
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• pp.163-192
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• 2018

The purpose of this paper was to examine universal dependency UD application cases of Korean and Japanese with similar morphological characteristics. In addition, UD application and improvement methods of Korean were examined through comparative analysis. Korean and Japanese are very well developed due to their agglutinative characteristics. Therefore, there are many difficulties to apply UD which is built around English refraction. We examined the application of UPOS and DEPREL as components of UD with discussions. In UPOS, we looked at category problem related to narrative such as AUX, ADJ, and VERB, We examined how to handle units. In relation to the DEPREL annotation system, we discussed how to reflect syntactic problem from the basic unit annotation of syntax tags. We investigated problems of case and aux arising from the problem of setting dominant position from Korean and Japanese as the dominant language. We also investigated problems of annotation of parallel structure and setting of annotation basic unit. Among various relation annotation tags, case and aux are discussed because they show the most noticeable difference in distribution when comparing annotation tag application patterns with Korean. The case is related to both Korean and Japanese surveys. Aux is a secondary verb in Korean and an auxiliary verb in Japanese. As a result of examining specific annotation patterns, it was found that Japanese aux not only assigned auxiliary clauses, but also auxiliary elements to add the grammatical meaning to the verb and form corresponding to the end of Korean. In UD annotation of Japanese, the basic unit of morphological analysis is defined as a unit of basic syntactic annotation in Japanese UD annotation. Thus, when using information, it is necessary to consider how to use morphological analysis unit as information of dependency annotation in Korean.

• Korean Journal of Heritage: History & Science
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• v.37
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• pp.243-266
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• 2004

This paper is based on fieldwork conducted from July 6, 2003 to July 24 of 2003 among the Tungusgroups Hezhe, Daur, Oloqun, Owenke, and Mongolian in the areas of Heilongjiang and Inner Mongolia Provinces. Recognizing the need for more in-depth study among these groups, the present research shows that the Tungus people are archeologically, historically, and linguistically different from Korean Han ethnic group and challenges the link between Korean and Tungus groups since the Bronze Age. The comparison between the "House Spirit" belief of the Tungus people and Koreans reveals certain commonalities in the "Maru," "Kitchen," and "Samshin Spirit" practices. There are two possible reasons for such commonalities. Historically, the Korean Han ethnic group and the Tungus people were geographically intimate, and contact or transmission between the two groups occurred naturally. Also, immigration of refugees from the fallen Koguryo and Puyo to the Tungus region added another dimension of cultural contact. In contrast to the common features shared between the two groups, there also exists differences between the two groups House Spirit blief. The Korean Han group's "House Spirit" belief is based on the agricultural practices that separates the inside sacred and outside secular world of the houses, whereas the Tungus ethnic group's "House Spirit" belief is based on mobile herding life style with a less distinction between in and outside of house. Additionally, each Korean "House Spirit" has its own distinctive personality, and each spirit is placed and worshipped according to its function. In the Tungus group, all the "House Spirits" are located and worshipped in "malu," and some of the spirits are non-conventional house spirits. Moreover, Korean "House Spirits" form a kinship structure, placing Songju, the highest spirit, at the center. In the Tungus practice, such structure is not found. The tight cohesive family formation among the house spirits in the Korean "House Spirit" belief is also the most distinctive feature in its comparison with Chinese belief. In China, the highest spirit is Jiang Taigong or Qiwu, and the house spirits do not have kinship relations. Korean's Outhouse Spirit and Chowangshin are related to the Han Chinese's counterpart on certain levels? however, their basic structures are different. It is clear that the correlation of "Malu" "Chowangshin" and "Samshin" between Korea and Tungus indicate important role of Tungus cultural elements within Korea's "House Spirit" belief.