• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.743-746
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• 1997

The Master-Slave manipulator is the generally used remote handling equipment in the hot cell, in which the high level radioactive materials such as spent fuels are handled. To analyze the motion and to implement the training system by virtual reality technology, the simulator for M-S manipulator using the computer graphics is developed. The parts are modelled in 3-D graphics, assembled, and kinematics are assigned. The inverse kinematics of the manipulator is defined, and the slave of manipulator is coupled with master by the manipulator's specification. Also, the virtual workcell is implemented in the graphical environment which is the same as the real environment. This graphic simulator of manipulator can be effectively used in designing of the maintenance processes for the hot cell equipment and enhance the reliability of the spent fuel management.

• 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.

• Annual Conference of KIPS
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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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Annual Conference of KIPS
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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.

• 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.