• 대기
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• 제14권1호
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• pp.24-43
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• 2004

This paper describes the detailed characteristics of heavy rainfall events occurred in Chungcheong province on 15 and 16 April and from 6 to 8 August 2002 based on the analysis of raingauge rainfall rate and radar reflectivity from the METRI's X-band Weather Radar located in Cheongju. A synoptic analysis of the case is carried out, first, and then the analysis is devoted to seeing how the radar observes the case and how much information we obtain. The highly resolved radar reflectivity of horizontal and vertical resolutions of 1 km and 500 m, respectively shows a three-dimensional structure of the precipitating system, in a similar sequence with the ground rainfall rate. The radar echo classification algorithm for convective/stratiform cloud is applied. In the convectively-classified area, the radar reflectivity pattern shows a fair agreement with that of the surface rainfall rate. This kind of classification using radar reflectivity is considered to be useful for the precipitation forecasting. Another noteworthy aspect of the case includes the effect of topography on the precipitating system, following the analysis of the surface rainfall rate, topography, and precipitating system. The results from this case study offer a unique opportunity of the usefulness of weather radar for better understanding of structural and variable characteristics of flash flood-producing heavy rainfall events, in particular for their improved forecasting.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.795-808
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• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.

• 대한원격탐사학회지
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• 제10권1호
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• pp.43-53
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• 1994

삼림을 베어서 태우고 난 직 후의 라오스 북부 화전지역의 MOS-1 위성영상을 처리하여 유역분지 단위로 화전지역의 면적을 산출하였으며, 지형도상에서 얻어낸 하계망과의 상관관계를 분석하기 위하여 PC Arc - Info의 GIS software를 이용하였다. 그 결과 화전의 분포비율이 높은 유역분지에서는 1차수하천의 분기율도 높게 나타남을 알수 있으며, 라오스에 있어서 화전이 지표 의 침식과 토지의 황폐화를 초래하여 여러가지 환경문제를 유발시키는 원인이 된다는 것이 규명 되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2851-2865
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• 2014

To improve the rate-distortion performance of distributed video compressive sensing (DVCS), the adaptive sparse basis and nonlocal similarity of video are proposed to jointly reconstruct the video signal in this paper. Due to the lack of motion information between frames and the appearance of some noises in the reference frames, the sparse dictionary, which is constructed using the examples directly extracted from the reference frames, has already not better obtained the sparse representation of the interpolated block. This paper proposes a method to construct the sparse dictionary. Firstly, the example-based data matrix is constructed by using the motion information between frames, and then the principle components analysis (PCA) is used to compute some significant principle components of data matrix. Finally, the sparse dictionary is constructed by these significant principle components. The merit of the proposed sparse dictionary is that it can not only adaptively change in terms of the spatial-temporal characteristics, but also has ability to suppress noises. Besides, considering that the sparse priors cannot preserve the edges and textures of video frames well, the nonlocal similarity regularization term has also been introduced into reconstruction model. Experimental results show that the proposed algorithm can improve the objective and subjective quality of video frame, and achieve the better rate-distortion performance of DVCS system at the cost of a certain computational complexity.

• Smart Structures and Systems
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• 제22권2호
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• pp.231-237
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• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 1부
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• pp.13-18
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• 2008

최근 건강 관리에 대한 관심이 증가하면서 착용형 생체 신호 센서, 재택형 무구속계측 센서에 관한 연구가 활발하게 진행되고 있다. 그러나, 측정 기술의 발전과 달리 측정결과를 제공하는 단계에서는 심장 박동수, 체온 등의 값을 단일된 임계치 기반으로 판단하며, 분석된 결과가 사용자에게 어떤 의미를 주는지에 대한 해석은 제공하지 않고 있다. 따라서 본 논문에서는 사용하기 편한 돌 형태의 휴대형 생체신호 측정센서를 기반으로 사용자 별로 적합한 생체신호 해석 방법을 제안한다. 개인화된 생체 신호 해석을 위해 1 주일간 사용자의 시간대별 데이터를 획득하고 사용자 별 특성에 따라 모델링을 한 후, 모델에 기반하여 사용자에게 맞는 생체 신호 범위를 정하고 이를 판단하는 근거로 활용한다, 센서는 기존의 착용형 생체 신호 센서 및 이를 이용한 응용에 폭넓게 사용될 수 있다.

• 한국정보통신학회논문지
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• 제20권10호
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• pp.1961-1966
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• 2016

최근 활발히 연구되고 있는 압축센싱 (compressive sensing) 이론에 따르면 나이퀴스트 주파수보다 적은 샘플율으로도 원 신호를 충실히 복원할 수 있음이 알려져 있다. 압축, 전송, 저장 등의 여러 분야에서 압축센싱 방법을 적용하려는 시도가 꾸준히 이어지고 있다. 특히 4K, 8K 등으로 요구되는 화소수가 제곱의 형태로 증가되는 영상처리 분야에서는 압축센싱에 기대하는 바가 크다. 본 논문에서는 압축센싱 기법을 적용한 영상의 획득 알고리듬을 제안한다. 영상의 일반적인 특성을 활용하여 높은 에너지 압축 성능을 가지는 DCT와 저상관도의 특성을 갖는 Noiselet 변환을 결합하여 영상 획득 과정을 구성한다. 원 영상은 2차 콘 프로그램 (SOCP)을 풀어 복원할 수 있다. 여러 영상에 대해 획득 및 복원 성능을 측정 및 비교하였으며 제안된 알고리듬이 우수한 복원 성능을 보임을 알 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권4호
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• pp.703-719
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• 2011

In multi-user wireless communication systems, adaptive modulation and scheduling are promising techniques for increasing the system throughput. However, a mass of wireless recourse will be occupied and spectrum efficiency will be decreased to feedback channel quality indication (CQI) of all users in every subcarrier or chunk for adaptive orthogonal frequency division multiplexing (OFDM) systems. Thus numerous limited feedback schemes are proposed to reduce the system overhead. The recently proposed compressive sensing (CS) theory provides a new framework to jointly measure and compress signals that allows less sampling and storage resources than traditional approaches based on Nyquist sampling. In this paper, we proposed two novel CQI feedback schemes based on general CS and subspace CS, respectively, both of which could be used in a wireless OFDM system. The feedback rate with subspace CS is greatly decreased by exploiting the subspace information of the underlying signal. Simulation results show the effectiveness of the proposed methods, with the same feedback rate, the throughputs with subspace CS outperform the discrete cosine transform (DCT) based method which is usually employed, and the throughputs with general CS outperform DCT when the feedback rate is larger than 0.13 bits/subcarrier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.3887-3907
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• 2015

This paper introduces a full duplex single secondary user multiple-input multiple-output (FD-SSU-MIMO) cognitive radio network, where secondary user (SU) opportunistically accesses the authorized spectrum unoccupied by primary user (PU) and transmits data based on FD-MIMO mode. Then we study the network achievable average sum-rate maximization problem under sum transmit power budget constraint at SU communication nodes. In order to solve the trade-off problem between SU's sensing time and data transmission time based on opportunistic spectrum access (OSA) and the power allocation problem based on FD-MIMO transmit mode, we propose a simple trisection algorithm to obtain the optimal sensing time and apply an alternating optimization (AO) algorithm to tackle the FD-MIMO based network achievable sum-rate maximization problem. Simulation results show that our proposed sensing time optimization and AO-based optimal power allocation strategies obtain a higher achievable average sum-rate than sequential convex approximations for matrix-variable programming (SCAMP)-based power allocation for the FD transmission mode, as well as equal power allocation for the half duplex (HD) transmission mode.

• 대한원격탐사학회지
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• 제40권2호
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• pp.219-228
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• 2024

Vessels can operate with their Automatic Identification System (AIS) turned off, prompting the development of strategies to identify them. Among these, utilizing satellites to collect radio frequency (RF) data in the absence of AIS has emerged as the most effective and practical approach. The purpose of this study is to develop a matching algorithm for RF with AIS data and find the RF's applicability to classify a suspected ship. Thus, a matching procedure utilizing three RF datasets and AIS data was employed to identify ships in the Yellow Sea and the Korea Strait. The matching procedure was conducted based on the proximity to AIS points, ensuring accuracy through various distance-based sections, including 2 km, 3 km, and 6 km from the AIS-based estimated points. Within the RF coverage, the matching results from the first RF dataset and AIS data identified a total of 798 ships, with an overall matching rate of 78%. In the cases of the second and third RF datasets, 803 and 825 ships were matched, resulting in an overall matching rate of 84.3% and 74.5%, respectively. The observed results were partially influenced by differences in RF and AIS coverage. Within the overlapped region of RF and AIS data, the matching rate ranged from 80.2% to 98.7%, with an average of 89.3%, with no duplicate matches to the same ship.