• 기본간호학회지
• /
• 제14권3호
• /
• pp.323-330
• /
• 2007

Purpose: The purpose of this study was to assess the difference in knowledge and learning needs of the coronary artery disease according to the general characteristics of the patients with type 2 diabetes mellitus. Method: The participants were 188 patients who had diabetes mellitus. Data were obtained between January and April 2006 by asking the knowledge and learning needs of the coronary artery disease. Data were analyzed using SAS program. Results: Participants' knowledge level was high in the cause and prevention, but the level was low in the symptom and occurrence of pain. Learning need for "Influence of smoking on heart disease" and "The structure and functions of the heart" was great, but learning need for "Complete diagnosis" and "Management of pain and pressure on the sternum" was little. No significant differences were found in the knowledge level of the coronary artery disease according to the general characteristics. Learning needs were greater in participants under 60 years of age (p=0.011) and in those with low education level (p=0.049). There was a significant correlation between knowledge and learning needs of the coronary artery disease (p=0.003). Conclusion: In planing the education programs, the general characteristics of the patients should be considered.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.1042-1045
• /
• 2003

In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제5권1호
• /
• pp.76-82
• /
• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
• /
• pp.963-968
• /
• 1990

An iterative learning control scheme for tracking control of a class of uncertain nonlinear systems is presented. By introducing a model reference adaptive controller in the learning control structure, it is possible to achieve zero tracking of unknown system even when the upperbound of uncertainty in system dynamics is not known apriori. The adaptive controller pull the state of the system to the state of reference model via control gain adaptation at each iteration, while the learning controller attracts the model state to the desired one by synthesizing a suitable control input along with iteration numbers. In the controller role transition from the adaptive to the learning controller takes place in gradually as learning proceeds. Another feature of this control scheme is that robustness to bounded input disturbances is guaranteed by the linear controller in the feedback loop of the learning control scheme. In addition, since the proposed controller does not require any knowledge of the dynamic parameters of the system, it is flexible under uncertain environments. With these facts, computational easiness makes the learning scheme more feasible. Computer simulation results for the dynamic control of a two-axis robot manipulator shows a good performance of the scheme in relatively high speed operation of trajectory tracking.

• 한국초등과학교육학회지:초등과학교육
• /
• 제35권1호
• /
• pp.54-64
• /
• 2016

The purpose of this study is to develop instructional strategy which utilizes thinking maps focused on drawing as a measure to enhance science learning motivation, self-directed learning activity and science academic achievement of learners, and to examine the effects of its application. The target unit for this study is 'life cycle of plants' in the fourth grade of elementary school. Two classes of 4th grades of elementary school were selected and divided into two groups. The learners of experimental group have completed thinking map by drawing a picture to express the results to be observed and measured, and used it to arrange the learning contents. The result of this study is as follows. First, it is proven that using thinking maps focused on drawing actually helped improving the motivation of learners to study science. Second, it is proven that this strategy was effective to change their self-directed learning ability in positive ways. Third, it contributed to the improvement of learners' science academic achievement. We found out that the application of this strategy enabled them to enjoy the mapping using drawing, to be immersed in learning, to better recognize the scientific concepts and the structure of learning contents, and to have a positive awareness of the usefulness of thinking maps focused on drawing.

• Educational Technology International
• /
• 제19권2호
• /
• pp.175-197
• /
• 2018

Flipped classroom (FC) has gained attention as an active learning approach. Designing effective out-of-class activities to help prepare students for in-class activities is fundamental for successful implementation of FC. This study investigated the effectiveness of Goal-Based Scenarios (GBS) for out-of-class learning in FC. Four out of twelve units in a medical humanities course for Year 2 medical students was redesigned into a FC format, where e-learning modules were designed using a GBS approach for out-of-class activities and classroom debates were implemented for in-class activities. The other eight units were delivered in a conventional classroom debate format, which included reading text materials as pre-class assignments. A formative evaluation study was conducted using questionnaires and interview methods and students' academic achievements were evaluated by comparing their pre- and post-test scores between FC and conventional units. Students had positive perceptions of the e-learning modules in GBS approach and preferred the structure of learning in the FC format. Students' pre-test scores were slightly higher in the FC units, yet their post-test scores were comparable with conventional units. This study illustrates students' perceptions that the learning was bettered structured in FC and that the out-of-class learning using the GBS approach helped them better prepared for in-class activities.

• Educational Technology International
• /
• 제16권2호
• /
• pp.141-166
• /
• 2015

The purpose of this study was to examine the effects of using visual stimulation and gesture, namely embodied language learning, on learning achievement and learner's course interest in the EFL classroom. To investigate the effectiveness of the proposed purpose, thirty two third-grade elementary school students participated and were assigned into four English learning class conditions (i.e., using animated graphic and gestures condition, using only animated graphic condition, using still pictures and gesture condition, and control condition). The research questions for this study are addressed below: (1) What differences are there in post and delayed learning achievement between imitating gesture group and non-imitating one and between animated graphic group and still picture one? (2) What differences are there in course interest between imitating gesture group and non-imitating one and between animated graphic group and still picture one? The Embodiment-based English learning system for this study was designed by using Microsoft's Kinect sensing devices. The results of this study revealed that students of imitating gesture group memorized and retained better words and sentence structure than those of the other groups. As for learner's course interest measurement, imitating gesture group showed a highly positive response to attention, relevance, and satisfaction for curriculum and using animated graphic influenced satisfaction as well. This finding can be attributed to the embodied cognition, which proposes that the body and the mind are inseparable in the constitution of cognition and thus students using visual simulation and imitating related gesture regard the embodied language learning approach more satisfactory and acceptable than the conventional ones.

• 한국문헌정보학회지
• /
• 제58권1호
• /
• pp.181-198
• /
• 2024

교수·학습 패러다임의 전환에 따라 비정형 학습에 대한 요구가 높아지고 있다. 본 연구에서는 학습자 주도로 일상적으로 이루어지는 전문가학습공동체를 사서를 위한 교육훈련 프로그램에 적용해보고자 관련 문헌과 사례를 분석하였다. 국내외의 사서와 타 분야의 전문가학습공동체 7개의 운영 사례를 선정하여 학습공동체 구성, 운영 형식과 방법, 학습 내용, 지원 체계 등을 분석하였다. 문헌 검토와 사례 분석을 종합하여 (가칭)사서학습공동체의 개념을 정의하고, 사서학습공동체 구성 및 운영을 위한 시사점으로 구성의 자발성과 다층위성, 학습공동체 운영 형식의 다양성, 학습 내용의 현장성을 도출하였다. 사서학습공동체의 원활한 운영과 활성화를 위한 지원 체계로는 운영기관의 프로그램 운영의 지속성, 소속기관의 지지와 협조, 교육 및 연수 프로그램, 플랫폼 구축 및 운영, 활성화를 위한 성과 확산과 환류의 필요성을 제시하였다.

• 대한수학교육학회지:학교수학
• /
• 제5권3호
• /
• pp.361-384
• /
• 2003

본 연구는 연립일차방정식에 관한 문장제에서 IDEAL 문제 해결 모형을 바탕으로 "구조-표현"을 강조한 교수-학습을 실시하였을 때 학생들의 문제해결 과정을 탐구하였다. 연구 결과, 구조-표현을 강조한 학급의 학생들이 이를 강조하지 않은 학급의 학생들보다 문제해결 능력이 향상되었으며, 동치문제, 동형문제, 유사문제를 더 정확하게 구별하였다. 또한, 구조-표현을 강조한 학급의 학생들이 그렇지 않은 학급의 학생들보다 문맥에 대한 이해 및 불완전한 정보 추출에서의 오류, 미지수간의 내적 관계에 대한 수학적 기호표현으로의 불완전한 전이 오류, 적절하지 않은 방정식 생성 오류의 발생 빈도가 적었다. 그리고, IDEAL 문제 해결 모형의 문제의 확인 단계(I)와 문제의 정의 단계(D)에서 학생들이 문제 해결 계획을 수립하기 위해 문제를 읽고 이해하여 문제를 해결하는 과정을 중점적으로 분석한 결과, 직접 변환 모델과 구조 도식 모델이 나타났다.

• 한국터널지하공간학회 논문집
• /
• 제22권5호
• /
• pp.515-528
• /
• 2020

통상적으로 콘크리트 지하 구조물은 수십 년 이상 사용할 수 있도록 설계되지만 최근 들어 구조물 중 상당수가 당초의 기대 수명에 근접하고 있는 실정이다. 그 결과 구조물 고유의 기능이 상실되고 다양한 문제가 야기될 수 있어 신속한점검과 보수가 요구되고 있다. 이를 위해 지금까지는 지하 구조물 유지관리를 위하여 인력 기반의 점검과 보수가 진행되었으나 최근에는 인공지능과 영상 기술의 융합을 통한 객관적인 점검 기술 개발이 활발하게 이루어지고 있다. 특히 딥러닝을 활용한 영상 인식 기술을 적용하여 지도학습 기반의 콘크리트 균열 탐지 알고리즘 개발에 관한 연구가 다양하게 진행되고 있다. 이러한 연구들은 대부분 지도학습 형태 영상 인식 기술로 많은 양의 데이터를 바탕으로 개발이 되는데, 그 중에도 많은 수의 라벨 영상(Label image)이 요구된다. 이를 확보하기 위해서는 현실적으로 많은 시간과 노동력이 필요한 실정이다. 본 논문에서는 이와 같은 문제를 개선하고자 적대적 학습 기법을 적용하여 균열 영역 탐지 정확도를 평균적으로 0.25% 향상시키는 방법을 기술하고자 한다. 이 적대적 학습은 분할(Segmentation) 신경망과 판별자(Discriminator) 신경망으로 구성되어 있고, 가상의 라벨 영상을 경쟁적인 구조로 생성하여 인식 성능을 높이는 알고리즘이다. 본 논문에서는 이 같은 방법을 활용하여 효율적인 심층 신경망 학습 방법을 제시하였고, 향후에 정확한 균열 탐지에 활용될 것으로 기대한다.