• 한국수학교육학회지시리즈A:수학교육
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• 제51권3호
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• pp.301-321
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• 2012

Since research has barely been done on the minority with low-achievement & low-SES in probability, this research attempted to search the change of their thinking level in the classes of probability and motivate them on the mathematical learning to feel confident in mathematics. We can say that the problems of the educational discriminations are due to the overlook on the individual conditions, situations, and environments. Therefore, in order to resolve some discrimination, 4 students who belonged to the minority group, engaged in the research, based on 10 units of the instructional materials designed for the research. As a result, for the student's thinking level, it was observed that they were improved from the 1st to the 3rd level in probability. Also, the researcher found that the adequate use of the encouragement, the praise, the direct explanation, and the scaffolding enabled them to prompt their learning motives and the increased responsibility on the learning. As time passed, the participants could share their mathematical knowledge and its concept with others, in the increased confidence.

• 대한수학교육학회지:학교수학
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• 제12권1호
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• pp.17-26
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• 2010

본 연구의 목적은 커뮤니티 환경 아래에서 수학 교사들을 이해하고 고등학교와 대학 간의 협력을 통한 전문성 발달에 관한 연구에 기여하기 위함이다. 본 연구에서는 학습 커뮤니티를 형성하는 데 있어서 수학 교사들이 어떤 어려움을 가지는 가에 관해 조사하였다. 미국의 남동부 지역의 한 프로젝트의 자료를 이용하였으며, 세 명의 예비 교사와 세 명의 멘토 교사,그리고 한 명의 대학에서 나온 교사가 학습 커뮤니티에 속하여 함께 일하였다. 연구 자료는 관찰노트, 인터뷰, 설문지, 이메일 내용 등이 포함되고, 사례 연구와 내러티브 분석법으로 자료를 분석하였다. 연구 결과, 참가자간의 파워 이슈, 논의할 주제 선택이나 서로에 대해 비판하기, 목표를 공유하거나 시간과 멤버 수 조절하는 것 등의 어려움을 나타내었다.

• International journal of advanced smart convergence
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• 제8권1호
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• pp.166-175
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• 2019

Grit, a concept conceived in 2007 by Duckworth and others in the United States, is based on positive psychology that focuses on growth and development through individual strengths. Recently, "Grit", which means patience and enthusiasm for long-term goals, has emerged as a key factor of personality strength. In Korea, Joo-hwan Kim (2013) was the first to conceptualize and study the subject of Grit. However, there have been no overview studies that systematically summarize the overall trends and flow in the research of Grit so far. There have been 147 research papers on Grit published so far in Korea. The purpose of this study was to conduct trend analysis on the subject of Grit by analyzing forty-three (43) master's and doctoral dissertations, thus presenting the direction of future research on Grit through careful analysis. In the studies conducted, it was found that Grit is a very significant variable linked to self-efficacy. It is also a subjective belief that can help an individual achieve his/her educational goals, and go through failure resynchronization. In addition, Grit is very significant as a practical core indicator of how fusion talent is fostered for the fourth industrial revolution. Therefore, there is a need for more in-depth research from the viewpoints of workplace learning, experiential learning, or informal learning, as well as research into Grit characteristics.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.794-798
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• 2009

We introduce an implementation of plug-ins on PLUTO. These plug-ins discriminate inflammatory nodules from other types of nodules in chest X-ray CT images. The PLUTO is a common platform for computer-aided diagnosis systems on Microsoft Windows series and it is easy to add new functions as plug-ins. We coded two plug-ins. One of the them calculates features based on medical knowledge. The other plug-in calculates parameters to classify the type of nodules, and it also classifies nodules into inflammatory nodules and others using SVM. These plug-ins are coded using MIST library which is produced at Nagoya University, Japan. In our previous study, the MIST library was parallelized, so that we can utilize a number of CPUs to calculate features and SVM learning/classifying depending on the amount of computation. Using these plug-ins, it became easy to extract features to discriminate inflammatory nodules from other types of nodules and to change parameters for feature extraction and SVM learning/classifying with GUI interface. The accuracy of the classifying result is 100% with 78 solid nodules which contains 43 inflammatory nodules and 35 other type of nodules.

• Asia pacific journal of information systems
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• 제18권4호
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• pp.105-130
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• 2008

Consumers of e-learning market differ from those of other markets in that they are replaced in a specific time scale. For example, e-learning contents aimed at highschool senior students cannot be consumed by a specific consumer over the designated period of time. Hence e-learning service providers need to attract new groups of students every year. Due to lack of information on products designed for continuously emerging consumers, the consumers face difficulties in making rational decisions in a short time period. Increased uncertainty of product purchase leads customers to herding behaviors to obtain information of the product from others and imitate them. Taking into consideration of these features of e-learning market, this study will focus on the online herding behavior in purchasing e-learning contents. There is no definite concept for e-learning. However, it is being discussed in a wide range of perspectives from educational engineering to management to e-business etc. Based upon the existing studies, we identify two main view-points regarding e-learning. The first defines e-learning as a concept that includes existing terminologies, such as CBT (Computer Based Training), WBT (Web Based Training), and IBT (Internet Based Training). In this view, e-learning utilizes IT in order to support professors and a part of or entire education systems. In the second perspective, e-learning is defined as the usage of Internet technology to deliver diverse intelligence and achievement enhancing solutions. In other words, only the educations that are done through the Internet and network can be classified as e-learning. We take the second definition of e-learning for our working definition. The main goal of this study is to investigate what factors affect consumer intention to purchase e-learning contents and to identify the differential impact of the factors between consumers with purchase experience and those without the experience. To accomplish the goal of this study, it focuses on herding behavior and perceived usefulness as antecedents to behavioral intention. The proposed research model in the study extends the Technology Acceptance Model by adding herding behavior and usability to take into account the unique characteristics of e-learning content market and e-learning systems use, respectively. The current study also includes consumer experience with e-learning content purchase because the previous experience is believed to affect purchasing intention when consumers buy experience goods or services. Previous studies on e-learning did not consider the characteristics of e-learning contents market and the differential impact of consumer experience on the relationship between the antecedents and behavioral intention, which is the target of this study. This study employs a survey method to empirically test the proposed research model. A survey questionnaire was developed and distributed to 629 informants. 528 responses were collected, which consist of potential customer group (n = 133) and experienced customer group (n = 395). The data were analyzed using PLS method, a structural equation modeling method. Overall, both herding behavior and perceived usefulness influence consumer intention to purchase e-learning contents. In detail, in the case of potential customer group, herding behavior has stronger effect on purchase intention than does perceived usefulness. However, in the case of shopping-experienced customer group, perceived usefulness has stronger effect than does herding behavior. In sum, the results of the analysis show that with regard to purchasing experience, perceived usefulness and herding behavior had differential effects upon the purchase of e-learning contents. As a follow-up analysis, the interaction effects of the number of purchase transaction and herding behavior/perceived usefulness on purchase intention were investigated. The results show that there are no interaction effects. This study contributes to the literature in a couple of ways. From a theoretical perspective, this study examined and showed evidence that the characteristics of e-learning market such as continuous renewal of consumers and thus high uncertainty and individual experiences are important factors to be considered when the purchase intention of e-learning content is studied. This study can be used as a basis for future studies on e-learning success. From a practical perspective, this study provides several important implications on what types of marketing strategies e-learning companies need to build. The bottom lines of these strategies include target group attraction, word-of-mouth management, enhancement of web site usability quality, etc. The limitations of this study are also discussed for future studies.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권7호
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• pp.491-502
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• 2010

인간이 교육을 통해 지식을 습득하고 발전시키는 과정에서, 이전 단계에서의 학습 진행 과정은 향후 학습에 영향을 미친다. 이것은 기계 학습에서도 고려되어야 할 사항으로 실제 기계 학습에서 학습순서의 제어가 어떤 효과가 있는지 살펴볼 필요가 있다. 본 연구에서는 MLP의 학습에서 지도자가 목표값을 알려주는 역할은 물론, 학습 대상의 지식 정도를 고려하여 자료들의 학습 순서를 제어하는 추가적 역할도 수행할 때, 학습 과정에 미치는 효과를 실험한다. 실험 방법은 SOM과 MLP를 이용하여 분류 문제에 적용한다. SOM은 지도자가 학습 순서를 결정하기 위한 학습 자료들의 범주화에 이용되고, MLP는 학습 대상이 된다. 제안하는 방법은 SOM을 학습 자료의 전처리 방법이 아닌, 학습 과정 동안 학습 자료의 선택에 이용하는 점에서 여타 연구들과 차이가 있으며, 실험 결과는 학습에 사용되는 자료의 수와 학습 횟수에서 개선 효과가 있음을 보여준다.

• 전자공학회논문지C
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• 제36C권6호
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• pp.44-51
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• 1999

HMM-Net은 은닉 마르코프 모델(HMM)의 계산과정을 신경망 구조로 구현한 것으로, HMM이 갖고 있는 시계열 모델링 능력과 신경망이 갖고 있는 우수한 변별력을 결합한 것이다. HMM-Net 분류기를 학습하는 방법으로는 HMM의 학습에 이용되는 最尤法(ML)과 신경망 학습의 最小二乘誤差法(MMSE)를 적용할 수 있다. 이들 중 ML이 MMSE보다 안정된 학습을 보장하는 반면 초기 학습조건을 적절하게 설정하였을 경우에는 MMSE가 ML보다 우수하다고 알려져 있다[3]. 따라서 이 논문에서는 먼저 ML을 이용하여 초기학습을 수행한 다음 보다 학습성능이 우수한 MMSE로 바꾸어 최적 또는 준최적으로 학습하는 하이브리드 학습법(ML/MMSE)을 제안한다. 실험용 시계열 패턴으로 /0/부터 /9/까지의 고립 숫자음을 이용하여 실험한 결과, 제안한 방법이 학습특성 및 인식률면에서 ML이나 MMSE만을 이용하는 기존의 방법보다 우수하였음을 확인하였다.

• 한국지진공학회논문집
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• 제25권2호
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• 대한간호학회지
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• 제30권2호
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• pp.514-526
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• 2000

The active treatment phase in preparation for bone marrow transplantation(BMT) of che- motherapy regimen and total body irradiation (TBI) containing regimen requires considerable teaching. There have been researches that are related to treatment onto BMT patients and to psychological change during BMT process. However, it was hard to find researches focused on learning needs of patients undergoing BMT. The purpose of this study was to provide the basic data for effective educational program about BMT by investigating the learning needs in patients undergoing BMT. The subjects consisted of 90 BMT patients have been admitted to the department of BMT at three university hospitals. Data were obtained from October 1998 to March 1999 and analyzed by SAS program for unpaired t-test, ANOVA, Duncan test. The results were as follows : 1. Learning needs related to demographic characteristics was identified as below. That of male was higher than that of female. That of under age 29, unmarried, religious and university graduated group was higher than that of opposite group but it didn't show significant difference. Learning needs of group of patients who were employed was significantly higher then that of unemployed patients. 2. According to types of diagnosis, learning needs of myelodysplastic syndrome(MDS) patients was the higher than that of others, but admission frequency was the least. Learning needs of unrelated matched BMT(UBMT) patients was higher than that of autologous BMT patients. However, it didn't show significant difference. With regard to learning needs according to process of BMT, learning needs of Pre- BMT period or Post-BMT period was significantly higher than that of BMT day. 3. Learning needs related to BMT was relatively high (total mean: 3.11 of 4.0). The order of the mean score of leaning needs was shown as follows : Restricted activities after discharge, Relapse symptom, Complications of BMT, Kinds of available drugs at home. Therefore the learning needs that is related to life after discharge and to relapse and complications after BMT was high. 4. Learning needs related to radiation therapy was high (total mean: 3.35 of 4.0). The learning needs in radiation therapy items was the Skin care of radiation therapy and Purpose of radiation therapy. 5. Learning needs related to graft versus host disease(GVHD) therapy was high (total mean: 3.55 of 4.0). The highest learning needs in GVHD therapy items was the Preventive method GVHD. less admission frequency and UBMT patients. It is necessary that education for BMT patients should be focused on life after discharge and on relapse and complications after BMT. Especially education for allogeneic BMT patients should be emphasized on GVHD. For all of these, it is necessary to develop systematic and concrete educational program.