• 의학교육논단
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• 제15권1호
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• pp.46-53
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• 2013

The purpose of this study was to analyze the relationship among medical students' learning motivation, characteristics of multiple intelligence, and academic achievement. The participants were 144 medical students. The data were collected by administering learning motivation tests (self-confidence, self-efficacy, level of task, emotion of learning, learning behavior, failure tolerance, task difficulty, and academic self-efficacy), a multiple intelligence test (linguistic intelligence, logical-mathematical intelligence, musical intelligence, bodily-kinesthetic intelligence, spatial intelligence, interpersonal intelligence, intrapersonal intelligence, and naturalistic intelligence), and two semesters of grades. There is a correlation between multiple intelligences and learning motivation. Among academic self-efficacy of academic motivation, the self-control efficacy (0.28) and behavior (0.18) subscales are significantly positively correlated with academic achievement. However, the emotion subscale (-0.18) was significantly negatively correlated. Learning motivation was correlated with two of the eight multiple intelligence profiles: the intrapersonal intelligence (0.18) and bodily-kinesthetic intelligence (-0.19). The structural equation modeling analysis showed that the behavior and self-control efficacy subscales of intrapersonal intelligence had an impact on academic achievement. An analysis according to the academic achievement group showed significant differences in self-control efficacy and emotion subscales with intrapersonal intelligence. A positive relationship can be observed between learning motivation and some characteristics of multiple intelligence of medical school students. In light of the findings, it is worth examining whether we can control medical students' learning motivation through educational programs targeting self-control efficacy and intrapersonal intelligence.

• 대한가정학회지
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• 제49권8호
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• pp.85-95
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• 2011

The purpose of this study was to examine the relationship between preschool teachers' multiple intelligence and their teaching and learning plans. For this purpose, multiple intelligences test(K-MIDAS) was conducted on 80 teachers in kindergartens located in Seoul and Gyeongsangnam-do and they were asked to map out teaching and learning plans about topics. The data were analysed with descriptive statistics and Pearson's correlation using SPSS PC program(16.0 version). Major findings were as follows: Teachers had the highest levels in interpersonal intelligence, followed by musical intelligence and linguistic intelligence; interpersonal intelligence and linguistic intelligence accounted for an especially high proportion of their teaching and learning plans. The higher a preschool teacher's physical activity intelligence, the greater the proportion of physical exercise, music, and logic and mathematics in their teaching plans. It was also found that preschool teachers with higher levels linguistic intelligence made more plans on self-understanding, whereas preschool teachers with higher levels of intelligence in the observation and investigation of nature made more plans on spatial area.

• 정보화정책
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• 제24권4호
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• pp.79-92
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• 2017

도시 기능이 복합적으로 발전함에 따라 스마트 시티에 대한 관심이 높아지고 있다. 스마트 시티란 정보통신기술을 활용하여 교통, 안전, 복지, 생활 등 도시 문제를 효과적으로 해결하는 것을 말한다. 최근 세계 각국은 빅데이터, 사물인터넷, 인공지능 기술을 스마트 시티에 도입하는 시도를 하고 있으나 종합적인 도시 서비스로는 발전하지 못하고 있다. 본 논문에서는 국내외 스마트 시티 추진 현황을 살펴보고 핵심 문제로 부각된, 데이터 공유문제, 서비스 호환성 문제를 해결하는 방안을 제시하였다. 이를 위해 딥러닝 기술을 스마트 시티 서비스에 접목한 "딥러닝 시티 프레임워크"를 제안하고 도시 여러 영역의 시공간 데이터를 안전하게 공유하고 여러 도시의 학습 데이터를 융합하는 새로운 스마트 시티 추진 전략을 제시하였다.

• 한국농촌건축학회논문집
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• 제21권4호
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• pp.37-44
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• 2019

A reduction in the number of high school students in Eup and Myeon districts is very severe. This issue leads to the problem with educational programs of school and an academic achievement gap. Therefore, the purpose of this study is to analyze the characteristics of areas and layout types of the floor plan of high school facilities in Eup and Myeon districts of Jeju Island where the number of students reduces and to provide a fundamental material for establishing school environments. The floor plan of school facilities was categorized into learning, support, common, and other areas and the characteristics of each area were analyzed. As a result, it was necessary to make spatial and facility improvements in common area and support area. The layout type of each area was classified into centralized type, distributed type, and mixed type, and then each type was analyzed. As a result, the main building had low points of the floor plan for learning area and common area. In order to respond to the number of students, it is required to establish reasonable spatial plan criteria and guidelines under the supervision of Office of Education and furthermore to make an effort to create futuristic educational facilities.

• 한국초등과학교육학회지:초등과학교육
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• 제37권3호
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• pp.309-322
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• 2018

There is no doubt that science -and, therefore, science education- is central to the lives of all (NGSS, 2013). This manuscript focuses on ideas in astronomy that are at the foundation of elementary students' understanding of the discipline: the apparent motion of the sun explaining the day / night cycle on Earth. According to prior research demonstrating that neither children nor adults hold a scientific understanding of the big ideas of astronomy (NRC, 1996), understanding of concepts may base students' progress towards more advanced understanding in the domain of astronomy. We have analyzed the logic of the domain and synthesized prior research assessing children's spatial representation from an earth- and a space based perspective to develop a set of learning trajectories that describe how students' initial ideas about apparent celestial motion as they take school science can be build upon. In this study elementary students' representations were compared by their resident context including urban and rural. This study may present a first look at the use of a learning progression framework in analyzing the structure of astronomy education. We discuss how this work may eventually lead towards the development and empirical testing of how children learn to describe and explain apparent patterns of celestial motion.

• 제어로봇시스템학회논문지
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• 제19권11호
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• pp.1022-1028
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• 2013

It is well knowns that based on the CSP (Common Spatial Pattern) algorithm, the linear projection of an EEG (Electroencephalography) signal can be made to spaces that optimize the discriminant between two patterns. Sharing disadvantages from linear time invariant systems, CSP suffers from the non-stationary nature of EEGs causing the performance of the classification in a BCI (Brain-Computer Interface) system to drop significantly when comparing the training data and test data. The author has suggested a simple idea based on the parallel model of CSP filters to improve the performance of BCI systems. The model was tested with a simple CSP algorithm (without any elaborate regularizing methods) and a perceptron learning algorithm as a classifier to determine the improvement of the system. The simulation showed that the parallel model could improve classification performance by over 10% compared to conventional CSP methods.

• 지적과 국토정보
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• 제45권1호
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• pp.45-58
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• 2015

현 정부는 국가직무능력표준(NCS)을 국정과제로 채택하고 학벌과 자격중심의 사회에서 능력중심 사회로 변모를 꾀하고 있다. 현행 지적분야의 NCS는 측량, 공간정보구축과 함께 분류체계상 세분류에 속해 있어 지적행정, 지적측량, 국토조사 등 다양한 내용들을 다루기는 한계가 있다. 이에 본 연구는 지적분야의 NCS에 대한 현황 파악 및 문제점을 검토하고 그에 대한 개선방안으로 현행 국가직무능력표준 분류체계 개선, 지적분야 산업체와의 연계를 통한 실습모델 도입, NCS의 수준별 능력단위 및 능력단위 요소를 기반으로 하는 교과과정 개발, NCS 개발진과 학습모듈 집필진의 일원화 등 개선방안을 제시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• 대한원격탐사학회지
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• 제37권4호
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.