• Title/Summary/Keyword: Remote training

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GIS DETECTION AND ANALYSIS TECHNIQUE FOR ENVIRONMENTAL CHANGE

  • Suh, Yong-Cheol;Choi, Chul-Uong;Kim, Ji-Yong;Kim, Tae-Woo
    • Proceedings of the KSRS Conference
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    • 2008.10a
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    • pp.163-168
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    • 2008
  • KOMPSAT-3 is expected to provide data with 80-cm spatial resolution, which can be used to detect environmental change and create thematic maps such as land-use and land-cover maps. However, to analyze environmental change, change-detection technologies that use multi-resolution and high-resolution satellite images simultaneously must be developed and linked to each other. This paper describes a GIS-based strategy and methodology for revealing global and local environmental change. In the pre-processing step, we performed geometric correction using satellite, auxiliary, and training data and created a new classification system. We also describe the available technology for connecting global and local change-detection analysis.

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A Development of the Remote Teacher's Training Cyber System Applied by CBD Methodology (CBD 방법론을 적용한 원격교원연수 시스템의 개발)

  • Her, Young;Kim, Won-Young;Kim, Chi-Su;Kim, Jin-Soo
    • Proceedings of the Korea Information Processing Society Conference
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    • 2000.10a
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    • pp.485-488
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    • 2000
  • 인터넷을 통한 웹 기반 교수-학습시스템은 상호작용 중진을 통한 학습자 위주의 교육환경으로 지속적인 변화를 해왔고, 그 결과 학습자는 시간과 공간의 제약에서 벗어나 다양한 교육정보를 접할 수 있는 기회를 가지게 되었다. 본 논문에서는 교사를 대상으로 하는 원격교육 시스템에 한정하여 시스템개발에서 운영에 이르기까지의 모든 과정에 대한 경제적, 교육적 효율성 확보를 목적으로 하였다. 따라서 시스템 개발에서는 주요 모듈의 컴포넌트화를 시도하여 재사용성 증대를 통한 설계 기간 및 비용의 감소를 이끌어내어 개발의 효율성을 높이고, 교육학적 기반으로 구성주의 원리를 적용함으로써 교육의 효율성을 최대로 끌어올릴 수 있도록 설계하였다.

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LAND COVER CHARACTERISTICS OF MOUNTAIN REGIONS IN NORTH KOREA (북한 산악지역의 개간지 및 산림 특성에 관한 연구)

  • Cha, Su-Young;Park, Chong-Hwa
    • Proceedings of the KSRS Conference
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    • 2008.03a
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    • pp.109-112
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    • 2008
  • 현재 북한 토지피복 특성 중의 하나인 과도한 산지의 농지로의 전용은 홍수 등 자연재해를 일으키는 원인이 되고 있지만 북한에 대한 참조자료의 부족으로 피해규모나 상황에 대한 이해가 부족하다. 본 연구는 북한 양강도 산간지역을 대상으로 개간농지와 산림의 토지피복특성을 가을시기(2005 년 10 월 25 일) Quickbird (<0.6m) 위성영상의 육안분석과 분광특성을 이용하여 정확한 토지피복분류에의 기초 정보를 제공하는 것을 목적으로 한다. 토지피복 유형별 Training area 의 ROI(Region of Interest)의 면적은 2500pixel 로 하였고, 이것을 다시 .shp 파일로 변환하여 GoogleEarth 에서 표고 및 경사 등 보다 자세한 지형지물을 확인하였다. Quickbird 영상의 NDVI 분석을 통해 0.2 정도에서 식생과 농경지로 구분하는 임계값(Threshold)을 추정할 수 있었지만 늦게까지 추수를 끝내지 않은 농작물이나 이모작 농작물의 경우는 산림과 혼재되어 나타나고 있었다. 또한, 산림의 북사면은 수역 다음으로 낮은 NDVI 값을 나타내어 지형의 영향이 나타나고 있음을 알 수 있었다.

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Haptic Display in Multi-user Virtual World (다중 참여자 가상환경에서의 촉각상호작용기술)

  • Choi, Hyouk-Ryeol;Ryew, Sung-Moo
    • Journal of the Korean Society for Precision Engineering
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    • v.16 no.5 s.98
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    • pp.112-123
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    • 1999
  • Virtual reality is becoming a powerful tool for various applications such as training, entertainment, surgery, tele-robotics etc. One potential use for virtual reality is to allow several users to interact in a single virtual environment, for example several students sitting in front of different computers connected over a network. In this paper, we present a loosely coupled architecture of haptic display in the multi-user virtual world. The method of controlling haptic devices as well as the way of configuring individual haptic display system are addressed. We will develop an experimental virtual reality system for two remote users and conclude with an experimental work for the task of a multi-player ping-pong and grasping of a common object.

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A Neuro-Fuzzy Model Approach for the Land Cover Classification

  • Han, Jong-Gyu;Chi, Kwang-Hoon;Suh, Jae-Young
    • Proceedings of the KSRS Conference
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    • 1998.09a
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    • pp.122-127
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    • 1998
  • This paper presents the neuro-fuzzy classifier derived from the generic model of a 3-layer fuzzy perceptron and developed the classification software based on the neuro-fuzzl model. Also, a comparison of the neuro-fuzzy and maximum-likelihood classifiers is presented in this paper. The Airborne Multispectral Scanner(AMS) imagery of Tae-Duk Science Complex Town were used for this comparison. The neuro-fuzzy classifier was more considerably accurate in the mixed composition area like "bare soil" , "dried grass" and "coniferous tree", however, the "cement road" and "asphalt road" classified more correctly with the maximum-likelihood classifier than the neuro-fuzzy classifier. Thus, the neuro-fuzzy model can be used to classify the mixed composition area like the natural environment of korea peninsula. From this research we conclude that the neuro-fuzzy classifier was superior in suppression of mixed pixel classification errors, and more robust to training site heterogeneity and the use of class labels for land use that are mixtures of land cover signatures.

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Improved Classification Algorithm using Extended Fuzzy Clustering and Maximum Likelihood Method

  • Jeon Young-Joon;Kim Jin-Il
    • Proceedings of the IEEK Conference
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    • summer
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    • pp.447-450
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    • 2004
  • This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

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Classification ofWarm Temperate Vegetations and GIS-based Forest Management System

  • Cho, Sung-Min
    • International journal of advanced smart convergence
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    • v.10 no.1
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    • pp.216-224
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    • 2021
  • Aim of this research was to classify forest types at Wando in Jeonnam Province and develop warm temperate forest management system with application of Remote Sensing and GIS. Another emphasis was given to the analysis of satellite images to compare forest type changes over 10 year periods from 2009 to 2019. We have accomplished this study by using ArcGIS Pro and ENVI. For this research, Landsat satellite images were obtained by means of terrestrial, airborne and satellite imagery. Based on the field survey data, all land uses and forest types were divided into 5 forest classes; Evergreen broad-leaved forest, Evergreen Coniferous forest, Deciduous broad-leaved forest, Mixed fores, and others. Supervised classification was carried out with a random forest classifier based on manually collected training polygons in ROI. Accuracy assessment of the different forest types and land-cover classifications was calculated based on the reference polygons. Comparison of forest changes over 10 year periods resulted in different vegetation biomass volumes, producing the loss of deciduous forests in 2019 probably due to the expansion of residential areas and rapid deforestation.

Smart port Remote Control Training Content using Virtual Reality and IoT sensors (가상현실 기술과 IoT 센서를 활용한 스마트 항만 원격조종)

  • Yoon, Su-Bin;Kim, Hyun-A;Suh, Da-Hyun;Lee, Yeong-Ju;Park, Gyu-Hee;Park, Young Sub
    • Proceedings of the Korea Information Processing Society Conference
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    • 2020.11a
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    • pp.189-192
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    • 2020
  • 가상현실 기술과 IoT 센서와 연결하여 스마트 항만 원격조정하는 프로그램을 설계하였다. 스마트 항만은 항만 자동화 추세에 맞춰진 현대의 항만 시스템이며 그에 따른 기술력 확보가 국가의 경쟁력을 높이는 핵심이 된다. 아두이노로 하드웨어를, 유니티로 소프트웨어 부분을 설계하여 진행했다.

Emerging Machine Learning in Wearable Healthcare Sensors

  • Gandha Satria Adi;Inkyu Park
    • Journal of Sensor Science and Technology
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    • v.32 no.6
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    • pp.378-385
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    • 2023
  • Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

A Study on the Characterisitics of Modoo-Oriented Training Model of a Mixed Type in Non-Face-To-Face Tele-Practical Classes (비대면 원격 모바일 홈페이지 실습수업에서 혼합형 방식의 모두(modoo) 활용 중심 수업의 특성 연구)

  • Lee, Hee-Young
    • Journal of the Korea Convergence Society
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    • v.12 no.8
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    • pp.105-113
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    • 2021
  • Due to the recent coronavirus outbreak, many universities in Korea have started to implement remote education. Accordingly, the Ministry of Education has stated its plans to continuously encourage and maintain remote learning as the future innovation model for education and suggested the need for a diverse range of remote learning models. However, studies on the development of practical learning models have not been carried out actively until now. Particularly, there are not many case studies in the field of design, especially regarding mobile website development. As means to improve the newly designed practice environment, this study therefore proposes the "modoo" project that offers domain creation and online marketing services. As a result of this study, the researcher suggests the use of a mixed(blending) teaching method and realized that the effectiveness of education multiplies when project-based learning and flipped learning is combined appropriately. The research methodology was divided into two big sections, education content and operations, and the effect was evaluated using the course evaluations. The study results confirmed that the applicability will increase given that learning satisfaction levels increased by more than 5% compared to face-to-face learning.