• Title/Summary/Keyword: 신경림

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Classification of Natural and Artificial Forests from KOMPSAT-3/3A/5 Images Using Artificial Neural Network (인공신경망을 이용한 KOMPSAT-3/3A/5 영상으로부터 자연림과 인공림의 분류)

  • Lee, Yong-Suk;Park, Sung-Hwan;Jung, Hyung-Sup;Baek, Won-Kyung
    • Korean Journal of Remote Sensing
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    • v.34 no.6_3
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    • pp.1399-1414
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    • 2018

  • Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.

  • https://doi.org/10.7780/kjrs.2018.34.6.3.7 인용 PDF KSCI HTML

VCM based on Compression Neural Network for Multi-task (Multi-task 수행을 위한 압축 심층신경망 기반 VCM)

  • Lee, Haelim;Lee, Jooyoung;Cho, Seunghyun
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2021.06a
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    • pp.43-46
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    • 2021

  • 최근 기계 임무수행에 사용되는 데이터양이 증가함에 따라 기계를 위한 효율적인 영상 압축방식의 필요성이 높아졌다. 기존의 비디오 코덱은 HVS (Human Visual System) 특성을 고려한 기술이기 때문에 부호화 과정에서 기계 임무수행에 필요하지 않은 정보를 효과적으로 제거할 수 없다. 반면 심층신경망 기반 압축네트워크의 경우, 원본 영상으로부터 기계 임무수행에 필수적인 데이터만을 추출하여 부호화 하도록 학습할 수 있는 장점이 있다. 본 논문에서는 압축 심층신경망과 기계 임무수행 네트워크로 구성되는 VCM (Video Coding for Machine) 프레임워크를 제안하고 학습에 의한 압축효율 향상을 검증한다. 이를 위해 압축 심층신경망을 객체탐지 임무수행 네트워크와 함께 학습시킨 결과, VVC (Versatile Video Coding) 대비 평균 61.16%의 BD-rate 감소가 확인되었다. 뿐만 아니라, 학습된 압축 심층신경망은 객체분할 임무수행에서도 VVC 대비 평균 58.43%의 BD-rate 감소를 보여 다중 기계 임무의 효율적 수행이 가능함을 확인할 수 있었다.

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A case study of normal pressure hydrocephalus patient with gait disturbance using conservative Korean medical treatment (정상압수두증에 의한 보행장애 환자 치험 1례)

  • Jung, Min-ho;Lee, Mi-rim;Ey, Yoo-lee;Cho, Ki-ho;Mun, Sang-Kwan;Jung, Woo-sang
    • The Journal of the Society of Stroke on Korean Medicine
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    • v.17 no.1
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    • pp.45-54
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    • 2016

  • A case of a 75-year-old Korean female with gait disturbance due to Normal pressure hydrocephalus (NPH) is presented. She was treated with acupuncture, electroacupuncture and herbal medicine - 柴苓湯(Shirhyung-Tang, Chai-ling-tang, Sairei-to) We used iNPH grading scale, and specified further the grade of gait disturbance category. After Korean medical treatment, there was notable improvement in gait disturbance on our specified scale. Cognitive impairment, tremor and rigidity were improved on each scale alongside. Korean medical treatment may be effective in treating NPH patients.

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Distance Estimation Method of UWB System Using Convolutional Neural Network (합성곱 신경망을 이용한 UWB 시스템의 거리 추정 기법)

  • Nam, Gyeong-Mo;Jeong, Eui-Rim
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.344-346
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    • 2019

  • In this paper, we propose a distance estimation method using the convolutional neural network in Ultra-Wideband (UWB) systems. The training data set used to learn the deep learning model using the convolutional neural network is generated by the MATLAB program and utilizes the IEEE 802.15.4a standard. The performance of the proposed distance estimation method is verified by comparing the threshold based distance estimation technique and the performance comparison used in the conventional distance estimation.

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