• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.205-208
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• 2010

본 논문에서는 Zernike 모멘트와 backpropagation신경망을 이용한 온라인 필기체 숫자 인식 방법을 소개한다. 마우스로 통해 입력된 숫자 정보는 전처리를 통해 시간에 순서적이고, 연속적인 좌표 정보로 변환된다. 전처리된 입력 좌표는 Zernike 모멘트(moment)와 각도 특징(angulation feature)을 이용하여 각 숫자가 가지는 고유의 특징을 만들어 낸다. 이러한 특징은 크기, 모양, 틀어진 정도에 상관없이 항상 일정한 성질을 가진다. 제안된 방법으로 추출된 특징은 패턴 구분을 위해 back propagation 신경망의 입력으로 사용된다. 본 논문은 200개의 필기체 숫자 데이터베이스를 이용하여 실험을 한 결과, 제시된 방법은 적은 학습데이터만으로 학습이 가능할 뿐만 아니라 좋은 인식률을 보여준다.

• The Journal of Korean Physical Therapy
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• 제15권1호
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• pp.155-170
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• 2003

Lumbar segmental instability is considered to represent a significant sub-group within the chronic low back pain population. This condition has a unique clinical presentation that displays its symptoms and movement dysfunction within the neutral zone of the motion segment. The loosening of the motion segment secondary to injury and associated dysfunction of the local muscle system renders it biomechanically vulnerable in the neutral zone. There in evidence of muscle dysfunction related to the control of the movement system. There is a clear link between reduced proprioceptive input, altered slow motor unit recruitment and the development of chronic pain states. Dysfunction in the global and local muscle systems in presented to support the development of a system of classification of muscle function and development of dysfunction related to musculoskeletal pain. The global muscles control range of movement and alignment, and evidence of dysfunction is presented in terms of imbalance in recruitment and length between the global stability muscles and the global mobility muscles. The local stability muscles demonstrate evidence of failure of aeequate segmental control in terms of allowing excessive uncontrolled translation or specific loss of cross-sectional area at the site of pathology Motor recruitment deficits present as altered timing and patterns of recruitment. The evidence of local and global dysfunction allows the development of an integrated model of movement dysfunction. The clinical diagnosis of this chronic low back pain condition is based on the report of pain and the observation of movement dysfunction within the neutral zone and the associated finding of excessive intervertebral motion at the symptomatic level. Four different clinical patterns are described based on the directional nature of the injury and the manifestation of the patient's symptoms and motor dysfunction. A specific stabilizing exercise intervention based on a motor learning model in proposed and evidence for the efficacy of the approach provided.

• 종교문화비평
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• 제32호
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• pp.257-287
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• 2017

본고는 트렌토 공의회 이후 유럽 천주교에서 불가타 성경이 어떤 방식으로 사용되었는지를 이해하는 것을 일차적인 목적으로 하였다. 불가타 성경은 유럽 천주교에서 지고한 가치를 지녔으며, 천주교 내에서 진행되는 각종 신앙생활에서 중심축을 형성하였다. 이러한 점은 미사용 전례 텍스트와 피정용 묵상 해설서에 수록된 불가타 성경 구절들을 분석함으로써 확인할 수 있었다. 그러나 트렌토 공의회 이후 불가타 성경의 존재 양상을 규명하기 위해서는 아직 해결해야 할 의문점들이 산적해 있고, 또한 넘어야 할 과제들이 많다. 먼저 프랑스에서 나온 피정용 성경 해설서에는 성경이 모두 불어로 번역되어 있었다. 그러므로 불가타 성경의 유럽 자국어 번역이라는 문제는 대단히 복잡하게 진행되었을 것이다. 하지만 이에 대한 교황청의 입장이 담긴 교령들은 아직 발견되지 않았다. 유럽 자국어로의 번역 및 비유럽 선교지 언어로의 번역에 대한 교령들을 찾는 일은 지속적으로 탐구해야 할 과제이다. 한편 전례 텍스트 가운데 미처 다루지 못한 부분도 존재한다. 그것은 바로 ${\ll}$로마 성무일도${\gg}$이다. 트리덴티노 전례서 가운데 가장 먼저 제정된 ${\ll}$로마 성무일도${\gg}$에는 저녁 기도 이후에 밤 시간을 구분하여 바치는 기도에서 성경 독서를 행하였다. 오늘날에 와서는 독서 기도라고 부른다. 주로 수도원에서 매일 반복하여 바치는 성무일도의 밤 기도에서 성경 구절의 편성을 살펴보는 것도 트렌토 공의회 이후 유럽 천주교에서 불가타 성경의 존재 양상을 따지는 데 중요한 주제가 될 것이다. 16세기 이후 유럽 천주교에서 불가타 성경은 어떻게 사용되었는지를 검토하는 과정에서 발견한 점은 예수회가 천주교 내에서 성경에 대한 집중적인 관심을 불러일으키는 데 큰 공헌을 하였다는 사실이다. 따라서 천주교의 신앙생활에서 성경의 중요성을 강조하는 예수회의 정신은 동아시아 선교지에서도 그대로 이어졌을 것으로 보인다. 특히 중국 선교지에서 예수회 선교사들은 불가타 성경을 한문으로 번역하여 보급하는 일에 지대한 관심을 가지고 있었다. 이런 관점에서 본다면 유럽 천주교에서 불가타 성경이 정경으로 선포되고 라틴어 성경의 자국어 번역이 논란거리가 되었던 상황, 트렌토 공의회에서 결정된 미사용 전례 텍스트에 불가타 성경 구절이 체계적으로 배치된 점, 마지막으로 피정이라는 종교적 수련에서 사용되는 묵상 텍스트가 불가타 성경을 중심으로 편성된 것 등은 유럽 천주교를 넘어 중국과 같은 선교지역의 천주교회에 그대로 영향을 주었을 것이다.

• ETRI Journal
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• 제42권1호
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• pp.90-100
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• 2020

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

• 대한간호학회지
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• 제29권2호
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• pp.346-360
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• 1999

The purpose of this research was to identify nursing interventions performed by hospital nurses in Korea. The sample consisted of 311 nurses working in three hospitals. The Nursing Interventions Use Questionnaire developed by the Iowa Intervention Project team was used for data collection. The instrument was translated to Korean using the method of back-translation. Eighteen interventions were performed at least daily. Interventions in the Physiological : Basic domain were most frequently used at least daily. No interventions in the Family and Behavioral domains were used by nurses at least once a day. The most frequently used interventions was Documentation, followed by the interventions Medication : Parenteral, Intravenous(IV) Insertion, Temperature Control, and Shift Report. The intervention performed least often was Reproductive Technology Management. Nurses working in intensive care units on the whole performed interventions most often, while nurses working in obstetric, gynecological, and pediatric units performed them least often. The nurses working in intensive care unit, medical and surgical care units performed the interventions in the Physiological : Basic domain more often than the nurses working in obstetric, gynecological, and pediatric units. The nurses working in obstetric, gynecological, and pediatric units used the interventions in the Family domain more often than the nurses working in the other three units. This study contributes to the documentation of nursrs' work in Korea. Further study will be needed to validate nursing activities of each NIC intervention.

• 대한간호학회지
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• 제48권1호
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• pp.70-84
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• 2018

Purpose: This study was conducted to evaluate the validity and reliability of the Korean version of the clinical learning environment, supervision and nurse teacher evaluation scale (CLES+T) that measures the clinical learning environment and the conditions associated with supervision and nurse teachers. Methods: The English CLES+T was translated into Korean with forward and back translation. Survey data were collected from 434 nursing students who had more than four days of clinical practice in Korean hospitals. Internal consistency reliability and construct validity using confirmatory and exploratory factor analysis were conducted. SPSS 20.0 and AMOS 22.0 programs were used for data analysis. Results: The exploratory factor analysis revealed seven factors for the thirty three-item scale. Confirmatory factor analysis supported good convergent and discriminant validities. The Cronbach's alpha for the overall scale was .94 and for the seven subscales ranged from .78 to .94. Conclusion: The findings suggest that the 33-items Korean CLES+T is an appropriate instrument to measure Korean nursing students'clinical learning environment with good validity and reliability.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 추계 종합학술대회 논문집
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• pp.124-128
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• 2006

본 연구는 지금까지 연구되어진 사회자본 척도에 대한 선행연구 고찰을 토대로 스포츠 참여자의 사회자본 척도를 개발하는데 그 목적이 있다. 이와 같은 목적을 달성하기 위해 다양한 사회자본 척도 가운데 Fukuyama(1996), Shane(2005), 김정란(2003), 박희봉과 김명환(2000), 정기환과 심재만(2004)의 SC를 토대로 스포츠 참여자의 사회자본 척도(SCSPS)를 개발하였으며, 본 연구에서 도출된 결과는 다음과 같다. 첫째, 개발된 스포츠 참여자의 사회자본 척도(SCSPS)는 연결망, 규범, 신뢰, 지식공유, 지위향상 등 5개의 속성으로 구분되었다. 둘째, 개발된 스포츠 참여자의 사회자본 척도(SCSPS)를 구조방정식모형(SEM)을 이용하여 분석한 결과 연구자가 설정한 부합지수 기준에 적합하였다. 셋째, 스포츠 참여자의 사회자본 척도(SCSPS)는 신뢰도와 타당도 검증에 있어 양호한 것으로 나타났다.

• 식물병연구
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• 제22권2호
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• pp.116-121
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• 2016

In 2015, stem blight of Rubus crataegifolius was observed in Pohang, Korea. The symptoms began as dark red spots in the stem, which led to stem blight, then leaf blight, and eventually resulted in death. A fungal isolate was obtained from a symptomatic stem and incubated on a potato dextrose agar plate. The isolated fungus produced white, cloudy mycelia turned black in 3 days. Based on the morphological characteristics, the causal fungus was assumed to be Botryosphaeria sp. A pathogenicity test was conducted according to Koch's postulates. To identify the causal agent, the combined sequence of the internal transcribed spacer, ${\beta}$-tubulin, and translation elongation factor $1{\alpha}$ genes were used for phylogenetic analysis. Approximately 1,200 bp of the combined sequence clearly suggested that the isolated pathogen was Botryosphaeria parva. This is the first report on stem blight in R. crataegifolius caused by B. parva in Korea.

• 한국IT서비스학회지
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• 제19권5호
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• pp.83-91
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• 2020

Language Models such as BERT has been an important factor of deep learning-based natural language processing. Pre-training the transformer-based language models would be computationally expensive since they are consist of deep and broad architecture and layers using an attention mechanism and also require huge amount of data to train. Hence, it became mandatory to do fine-tuning large pre-trained language models which are trained by Google or some companies can afford the resources and cost. There are various techniques for fine tuning the language models and this paper examines three techniques, which are data augmentation, tuning the hyper paramters and partly re-constructing the neural networks. For data augmentation, we use no-answer augmentation and back-translation method. Also, some useful combinations of hyper parameters are observed by conducting a number of experiments. Finally, we have GRU, LSTM networks to boost our model performance with adding those networks to BERT pre-trained model. We do fine-tuning the pre-trained korean-based language model through the methods mentioned above and push the F1 score from baseline up to 89.66. Moreover, some failure attempts give us important lessons and tell us the further direction in a good way.

• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.305-313
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• 2011

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.