• 제목/요약/키워드: learning using ICT

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Development of Water Environmental Education Program Using Streams - Focused on ENVISION - (소하천 물 환경교육 프로그램 개발 - ENVISION을 중심으로 -)

  • Kim, Jeong-Hwa;Lee, Du-Gon
    • Hwankyungkyoyuk
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    • v.20 no.4
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    • pp.12-26
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    • 2007
  • The purpose of this research is to develop a water environmental education (EE) program using streams, based on the core ideas of ENVISION and materializing elements that were extracted in this research. This research realized the elements and presented a model of the water EE program using a local stream. First, this research developed a basic model of a water EE program using streams by extracting 10 materializing elements and realizing the elements in 4 stage-procedural model. The 10 materializing elements were 1. experiencing the process of inquiry, 2. inquiring local environments, 3. self-directing learning and mutual interaction with colleagues, 4. collecting real data and interpreting, 5. utilizing the ICT(information and communication technology), 6. inquiring with the view point of the 'Environmental Studies for EE', 7. inquiring with the watershed concept, 8. inquiring with the integrating and the holistic view point, 9. pursuing the macroscopic understanding about environment, and 10. connecting the real world phenomena with the environmental concepts and theories. This research materialized these 10 elements in 4 stage model, following the previous ENVISION research, which are 1. preparing stage and visual assessment, 2. writing the report of the inquiry plan, 3. collecting the real data in the environment and performing the investigation, and 4. presenting the inquiry results. Second, with using this basic model, this research developed and presented a model of the specific water EE program using a case stream called 'Baig Cheon' stream, which is a local stream. This research is considered to have a considerable meaning in developing a EE program with ENVISION ideas for the watershed concept and inquiry with environmental science using local streams. The developed model can help the professional development of teachers and teacher education of water EE.

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Deep Learning Based Floating Macroalgae Classification Using Gaofen-1 WFV Images (Gaofen-1 WFV 영상을 이용한 딥러닝 기반 대형 부유조류 분류)

  • Kim, Euihyun;Kim, Keunyong;Kim, Soo Mee;Cui, Tingwei;Ryu, Joo-Hyung
    • Korean Journal of Remote Sensing
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    • v.36 no.2_2
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    • pp.293-307
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    • 2020
  • Every year, the floating macroalgae, green and golden tide, are massively detected at the Yellow Sea and East China Sea. After influx of them to the aquaculture facility or beach, it occurs enormous economic losses to remove them. Currently, remote sensing is used effectively to detect the floating macroalgae flowed into the coast. But it has difficulties to detect the floating macroalgae exactly because of the wavelength overlapped with other targets in the ocean. Also, it is difficult to distinguish between green and golden tide because they have similar spectral characteristics. Therefore, we tried to distinguish between green and golden tide applying the Deep learning method to the satellite images. To determine the network, the optimal training conditions were searched to train the AlexNet. Also, Gaofen-1 WFV images were used as a dataset to train and validate the network. Under these conditions, the network was determined after training, and used to confirm the test data. As a result, the accuracy of test data is 88.89%, and it can be possible to distinguish between green and golden tide with precision of 66.67% and 100%, respectively. It is interpreted that the AlexNet can be pick up on the subtle differences between green and golden tide. Through this study, it is expected that the green and golden tide can be effectively classified from various objects in the ocean and distinguished each other.

The Effects of Programming Learning on the Improvement of Problem Solving Ability Using MCU (MCU를 활용한 프로그래밍 학습이 문제해결력 향상에 미치는 효과)

  • Jin, Sung-Su;Park, Phan-Woo
    • Journal of The Korean Association of Information Education
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    • v.14 no.3
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    • pp.319-328
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    • 2010
  • Computer programming education gives students a chance to use computers independently and actively. This plays a very positive role in acquiring higher cognitive skills such as mathematical skills and creative logical thinking. Thus the purpose of this study is to measure the degrees of students' problem-solving abilities using MCU programming kits based on the ICT Education Guide. The experiment confirms that programming classes using MCU kits have a more positive effect on the students problem-solving abilities than do those using the existing computer textbooks. The sub-constituents of problem-solving abilities - problem recognition, information gathering, analysis, diffuse thinking, decision-making, planning, execution, evaluation and feedback - also show significant statistical differences. Therefore, we can conclude that programming classes using MCU kits are very effective in advancing problem-solving abilities.

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Unsupervised Learning-Based Threat Detection System Using Radio Frequency Signal Characteristic Data (무선 주파수 신호 특성 데이터를 사용한 비지도 학습 기반의 위협 탐지 시스템)

  • Dae-kyeong Park;Woo-jin Lee;Byeong-jin Kim;Jae-yeon Lee
    • Journal of Internet Computing and Services
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    • v.25 no.1
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    • pp.147-155
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    • 2024
  • Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.

Development on Identification Algorithm of Risk Situation around Construction Vehicle using YOLO-v3 (YOLO-v3을 활용한 건설 장비 주변 위험 상황 인지 알고리즘 개발)

  • Shim, Seungbo;Choi, Sang-Il
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.7
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    • pp.622-629
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    • 2019
  • Recently, the government is taking new approaches to change the fact that the accident rate and accident death rate of the construction industry account for a high percentage of the whole industry. Especially, it is investing heavily in the development of construction technology that is fused with ICT technology in line with the current trend of the 4th Industrial Revolution. In order to cope with this situation, this paper proposed a concept to recognize and share the work situation information between the construction machine driver and the surrounding worker to enhance the safety in the place where construction machines are operated. In order to realize the part of the concept, we applied image processing technology using camera based on artificial intelligence to earth-moving work. Especially, we implemented an algorithm that can recognize the surrounding worker's circumstance and identify the risk situation through the experiment using the compaction equipment. and image processing algorithm based on YOLO-v3. This algorithm processes 15.06 frames per second in video and can recognize danger situation around construction machine with accuracy of 90.48%. We will contribute to the prevention of safety accidents at the construction site by utilizing this technology in the future.

Method for predicting the diagnosis of mastitis in cows using multivariate data and Recurrent Neural Network (다변량 데이터와 순환 신경망을 이용한 젖소의 유방염 진단예측 방법)

  • Park, Gicheol;Lee, Seonghun;Park, Jaehwa
    • Journal of Software Assessment and Valuation
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    • v.17 no.1
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    • pp.75-82
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    • 2021
  • Mastitis in cows is a major factor that hinders dairy productivity of farms, and many attempts have been made to solve it. However, research on mastitis has been limited to diagnosis rather than prediction, and even this is mostly using a single sensor. In this study, a predictive model was developed using multivariate data including biometric data and environmental data. The data used for the analysis were collected from robot milking machines and sensors installed in farmhouses in Chungcheongnam-do, South Korea. The recurrent neural network model using three weeks of data predicts whether or not mastitis is diagnosed the next day. As a result, mastitis was predicted with an accuracy of 82.9%. The superiority of the model was confirmed by comparing the performance of various data collection periods and various models.

A Comparative Study on Artificial in Intelligence Model Performance between Image and Video Recognition in the Fire Detection Area (화재 탐지 영역의 이미지와 동영상 인식 사이 인공지능 모델 성능 비교 연구)

  • Jeong Rok Lee;Dae Woong Lee;Sae Hyun Jeong;Sang Jeong
    • Journal of the Society of Disaster Information
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    • v.19 no.4
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    • pp.968-975
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    • 2023
  • Purpose: We would like to confirm that the false positive rate of flames/smoke is high when detecting fires. Propose a method and dataset to recognize and classify fire situations to reduce the false detection rate. Method: Using the video as learning data, the characteristics of the fire situation were extracted and applied to the classification model. For evaluation, the model performance of Yolov8 and Slowfast were compared and analyzed using the fire dataset conducted by the National Information Society Agency (NIA). Result: YOLO's detection performance varies sensitively depending on the influence of the background, and it was unable to properly detect fires even when the fire scale was too large or too small. Since SlowFast learns the time axis of the video, we confirmed that detects fire excellently even in situations where the shape of an atypical object cannot be clearly inferred because the surrounding area is blurry or bright. Conclusion: It was confirmed that the fire detection rate was more appropriate when using a video-based artificial intelligence detection model rather than using image data.

Smart Farm Control System for Improving Energy Efficiency (에너지 효율 향상을 위한 스마트팜 제어 시스템)

  • Choi, Minseok
    • Journal of Digital Convergence
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    • v.19 no.12
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    • pp.331-337
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    • 2021
  • The adaptation of smartfarm technology that converges ICT is increasing productivity and competitiveness in the agriculture. Technologies have been developed that enable environmental monitoring through various sensors and automatic control of the cultivation environment, and researches are underway to advance smartfarm technology using data generated from smartfarms. In this paper, an environmental control method to reduce the energy consumption of a smartfarm by using the environment and control data of the smartfarm is proposed. It was confirmed that energy consumption could be reduced compared to an independent environmental control method by creating an environmental prediction model using accumulated environmental data and selecting a control method to minimize energy consumption in a given situation by considering multiple environmental factors. In the future, research is needed to obtain higher energy efficiency through the advancement of the predictive model and the improvement of the complex control algorithms.

Context-awareness User Analysis based on Clustering Algorithm (클러스터링 알고리즘기반의 상황인식 사용자 분석)

  • Lee, Kang-whan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.7
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    • pp.942-948
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    • 2020
  • In this paper, we propose a clustered algorithm that possible more efficient user distinction within clustering using context-aware attribute information. In typically, the data provided to classify interrelationships within cluster information in the process of clustering data will be as a degrade factor if new or newly processing information is treated as contaminated information in comparative information. In this paper, we have developed a clustering algorithm that can extract user's recognition information to solve this problem in using K-means algorithm. The proposed algorithm analyzes the user's clustering attributed parameters from user clusters using accumulated information and clustering according to their attributes. The results of the simulation with the proposed algorithm showed that the user management system was more adaptable in terms of classifying and maintaining multiple users in clusters.

A Qualitative Analysis on the Characteristics of "Best Practice" in Mathematics (수학과 좋은 수업 사례에 대한 질적 분석)

  • Lee, Dae-Hyun;Choe, Seung-Hyun
    • Journal of the Korean School Mathematics Society
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    • v.9 no.3
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    • pp.249-263
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    • 2006
  • The purpose of this study is to investigate the characteristics of 'best practire' in mathematics and suggest some solutions to several problems emerging in mathematics classes of secondary schools. The study was carried out by using qualitative research methods such as class observations and in-depth interviews with six teachers. Based on the collected data, we could sort out the major patterns which characterize 'the good mathematics teaching' at schools in Korea. The common characteristics of best practice in mathematics are drawn out from the six cases. The common characteristics include revising the curriculum and text books, realistic mathematics education, using ICT and meta-cognition, introduction with motivation and interest, performance assessment and managing differentiated small group. Results implied that six teachers used a variety of instructional methods and strategies which is related with the common characteristics of good mathematics teaching. Also these teachers not only improved their own classroom practices but also participated in various professional community of mathematics education and shared their practical knowledge. In conclusion assorted efforts from the government and the school principals as well as the teachers are prerequisite for practicing and spreading good mathematics teaching across the classrooms.

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