• International journal of advanced smart convergence
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• 제12권1호
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• 동서간호학연구지
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• 제23권2호
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• pp.107-114
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• 2017

Purpose: The purpose of this study is to investigate the influencing factors of self-leadership, academic self-efficacy and instructor-student interaction on self-directed learning in nursing college students. Methods: This study used a descriptive survey design. Participants were 190 nursing college students at three universities in Jeollanam-do and Gyeongsangnam-do. Data were collected from May 10 to June 12, 2016 using self-report questionnaires. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and multiple regression with SPSS 19.0. Results: The results showed that the self-leadership of the nursing students was 3.49, academic self-efficacy, 3.17, instructor-student interaction, 3.71, and self-directed learning, 3.43, respectively. Self-directed learning of nursing college students was positively associated with self-leadership(r=.65, p<.001), academic self-efficacy(r=.61, p<.001) and instructor-student interaction (r=.36, p<.001). 001). Self-leadership, academic self - efficacy, major satisfaction, GPA 4.0 or above explained 65% of the total variance in self-directed learning among nursing college students. Conclusions: The findings of this study indicated that nursing interventions for increasing self-leadership, academic self-efficacy and major satisfaction should be developed to improve self-directed learning of nursing students. Additional studies for changes in the overall teaching and learning environment to promote the self-directed learning environment in nursing education should be conducted.

• 컴퓨터교육학회논문지
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• 제18권3호
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• pp.59-68
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• 2015

본 연구는 온라인 프로그래밍 학습 환경에 참여하는 학습자의 특성 및 학습양식과 성취도간의 관계를 분석하는데 목적이 있다. 분석을 위해, 중 고등학생 104명을 대상으로 Grasha-Reichmann의 학습양식 검사를 실시한 후, 12주간 C언어 프로그래밍 학습과 과제를 수행하였다. 먼저, 학습자 특성에 따른 학습양식 차이 결과, 성별에서 남학생이 여학생보다 의존형이 높았고, 학교급에서 중학생이 경쟁형과 회피형이 고등학생보다 높았다. 성취수준에서는 독립형과 참여형이 차이가 있었다. 학습양식과 성취도와의 관계를 분석한 결과, 독립형(r=.253, p<.01)과 참여형(r=.303, p<.01)이 정적 상관을 보여 두 분석 결과 독립형과 참여형이 성취도와 연관이 있는 학습양식임을 확인할 수 있었다. 또한 학습자들의 주 학습양식에 따른 학습 소감을 조사하여 학습유형별 특징을 분석하였으며, 조사 결과를 통해 학습양식별 온라인 프로그래밍 교수 학습 전략을 도출할 수 있었다.

• 디지털융복합연구
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• 제13권9호
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• pp.11-19
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• 2015

본 연구는 간호대학생을 대상으로 학습효과를 향상시키기 위한 학습전략으로써 블렌디드 러닝을 적용하여 자기주도학습력, 학습동기, 학습만족도와의 상관관계를 확인하고 학습만족도에 어떠한 영향을 미치는지 규명하기 위해 시도된 조사연구이다. 연구대상자는 D대학교 성인간호학 수업에 참여하는 학생 140명으로 7주 동안 면대면 수업과 6주 이러닝 수업을 진행한 후 2014년 6월 9일부터 6월 14일까지 자료를 수집하였다. 연구결과, 일반적 특성에 따른 변수들의 차이는 없었으며, 학생들의 학습만족도는 자기주도학습력과 유의한 상관관계가 있었다(r=.25, p=.003). 자기주도학습력이 학습만족도에 영향을 미치는 관련 요인으로 설명력은 22.1%로 나타났다(F=20.74, p<.001). 본 연구를 통해 간호대학생의 학습만족도를 향상시키기 위해서는 자기주도학습력을 고려한 블렌디드 러닝 콘텐츠 개발이 요구되고 다양한 블렌디드 러닝 운영방법에 관한 연구가 필요하다고 본다.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• The Plant Pathology Journal
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• 제39권4호
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.225-230
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• 1992

In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.

• 대한통합의학회지
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• 제10권2호
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• pp.145-153
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• 2022

Purpose : This study aimed to investigate the effects of blended learning on learning satisfaction in nursing students. Methods : This study was conducted with 160 current nursing students in the department of nursing at K university located in city B. All students included in the study understood the purpose of the study and provided informed consent to participate. Data were collected between May 3 and July 9, 2021 and analyzed using SPSS/WIN 22.0. The frequency, percentage, mean, and standard deviation were created, and t-test, ANOVA, and Scheffé test for post hoc analysis were performed. Correlations were analyzed using Pearson's correlation coefficient. The factors influencing learning satisfaction were analyzed using multiple regression. Results : Significant differences were observed for academic stress according to the online classes environmental satisfaction (F=4.10, p=.001), online classes experience (t=4.11, p=.001) and self-directed learning ability according to the grade (F=4.10, p=.001), online classes environmental satisfaction (t=4.11, p=.001). The academic stress of nursing students who experienced blended learning was significantly negatively correlated with self-directed learning ability (r=-.480, p<.001), and learning satisfaction (r=-.236, p<.001). self-directed learning ability showed a significant positive correlation with learning satisfaction (r=.524, p<.001). The regression model for the factors affecting the learning satisfaction of the subjects was statistically significant (F= 3.027, p<.001). The major influential factors of learning satisfaction were grade (𝛽=.154, p=.013), satisfaction with school life (𝛽=.168, p=.032), and satisfaction with non-contact learning environment (𝛽=-.141, p=.028). The explanatory power was 28 %. Conclusion : These results indicate that it is necessary to reduce academic stress and increase self-directed learning ability to enhance learning satisfaction in nursing students through blended learning. In addition, the development and operation of a tailored intervention program is required to help improve learning satisfaction.

• Journal of Positioning, Navigation, and Timing
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• 제11권3호
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권9호
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• pp.281-290
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• 2020

시각-언어 이동 문제는 시각 이해와 언어 이해 능력을 함께 요구하는 복합 지능 문제이다. 본 논문에서는 시각-언어 이동 에이전트를 위한 새로운 학습 모델을 제안한다. 이 모델은 데모 데이터에 기초한 모방 학습과 행동 보상에 기초한 강화 학습을 함께 결합한 복합 학습을 채택하고 있다. 따라서 이 모델은 데모 데이터에 편향될 수 있는 모방 학습의 문제와 상대적으로 낮은 데이터 효율성을 갖는 강화 학습의 문제를 상호 보완적으로 해소할 수 있다. 또한, 제안 모델에서는 기존의 목표 기반 보상 함수들의 문제점을 해결하기 위해 설계된 새로운 경로 기반 보상 함수를 이용한다. 본 논문에서는 Matterport3D 시뮬레이션 환경과 R2R 벤치마크 데이터 집합을 이용한 다양한 실험들을 통해, 제안 모델의 높은 성능을 입증하였다.