• The Journal of the Acoustical Society of Korea
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• v.25 no.4
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• pp.184-190
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• 2006

Recently, Lee et al. have proposed an algorithm utilizing the signals from different paths by using bottom mounted simple linear array to estimate 3-D location of oceanic target. But this algorithm assumes that sound velocity is constant along depth of sea. Consequently, serious performance loss is appeared in real oceanic environment that sound speed is changed variously. In this paper, we present a 3-D near field localization algorithm for inhomogeneous sound speed. The proposed algorithm adopt localization function that utilize ray propagation model for multipath environment with linear sound speed profile(SSP), after that, the proposed algorithm searches for the instantaneous azimuth angle, range and depth from the localization cost function. Several simulations using linear SSP and non linear SSP similar to that of real oceans are used to demonstrate the performance of the proposed algorithm. The estimation error in range and depth is decreased by 100m and 50m respectively.

• Electronics and Telecommunications Trends
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• v.29 no.1
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• pp.11-20
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• 2014

동작인식(gesture recognition) 기술은 입력 영상으로부터 영상에 포함된 사람들의 동작을 인식하는 기술로써 영상감시(visual surveillance), 사람-컴퓨터 상호작용(human-computer interaction), 지능로봇(intelligence robot) 등 다양한 적용분야를 가진다. 특히 최근에는 저비용의 거리 센서(range sensor) 및 효율적인 3차원 자세 추정(3D pose estimation)기술의 등장으로 동작인식은 기존의 어려움들을 극복하고 다양한 산업분야에 적용이 가능할 정도로 발전을 거듭하고 있다. 본고에서는 그러한 거리영상(range image) 기반의 동작인식 기술에 대한 최신 연구동향을 살펴본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.899-900
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• 2017

본 논문에서는 도로주행 영상 내에서 차선을 검출하는 알고리즘을 제안한다. 제안하는 알고리즘은 차량 내부 카메라로 촬영된 영상에 대하여 바타차야 거리를 이용해 차선 후보 영역을 검출한다. 검출된 영역에 대해 도로와 차선의 레퍼런스 RGB 값과의 바타차야 거리를 이용해 분류한 뒤, 차선이 갖는 특징을 모델링하여, 분류된 영역에서 차선으로 추정되는 영역만을 남긴다. 차선 영역 세그먼트의 흰 차선과 노란 차선의 클래스와의 유사도를 계산하여 검출된 차선정보를 제공한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.575-578
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• 2003

본 논문은 제한된 영역 안의 다수 물고기를 추적하는 방법을 제안한다. 고정된 카메라로 물고기가 있는 수조의 영상을 얻은 다음 실시간으로 얻는 매경영상을 통해 물고기의 이미지만을 얻는다. 이렇게 얻어진 이미지를 ART2 알고리즘을 통해 clustering을 하고 각각의 물고기라 추정되는 cluster와 이전까지 측정되어진 물고기 좌표와의 거리 계산을 통해 각각의 물고기의 개체 인식을 하게 된다. 본 논문에서는 기존의 물고기 이미지를 얻는 방법을 개선하여 다 개체 추적을 위한 깨끗한 개체 이미지를 얻는 방법과, 각 cluster들과 이진 물고기 위치와의 거리계산을 통한 개체 인식 방법에 대해 초점을 맞추었다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.

• Information and Communications Magazine
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• v.34 no.4
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• pp.33-38
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• 2017

본고에서는 최근 새로이 각광받고 있는 UWB 기술을 소개한다. IR-UWB 측위 관련 국내 및 해외 동향을 살펴보고, IR-UWB 표준에 의거하여 IR-UWB 신호 및 프레임 구조, 계층 구조 등에 대하여 서술하였다. 그리고 IR-UWB를 이용하여 고정밀 거리추정 및 위치추정을 하는 연구 동향에 대하여 살펴보았으며, 마지막으로 기술 동향을 정리하여 IR-UWB 관련 회사의 제품을 소개하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1883-1890
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• 2013

Location detection technology (LDT) is one of the core techniques for location based service (LBS) in wireless communication for improving resource management and quality of services. The location of a mobile station (MS) is estimated using the time of arrival (TOA) technique based on three circles with centers corresponding to coordinates of three base stations (BSs) and radius corresponding to distances between MS and BSs. For accurately estimating the location of MS, three circles should meet at a point for the trilateration method, but they generally do not meet a point because the radius is increased depending on the number of time delay for estimating the distance between MS and BS and the carrier frequency. The increased three circles intersect at six points and the three intersection points among them should be generally placed close to coordinate of the location for the specific MS. In this paper, we propose the shortest distance algorithm for TOA trilateration method, to select three interior intersection points from entire six points. The proposed approach selects three intersection points with the shortest distances between coordinates of MS and intersection points and determines the averaged coordinate of the selected three points, as the location of the specific MS. We demonstrate the performance of the proposed algorithm using a typical computer simulation example.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.374-380
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• 2012

The k-Nearest Neighbor (kNN) technique is popularly applied to assess forest resources at the county level and to provide its spatial information by combining large area forest inventory data and remote sensing data. In this study, two approaches such as distance-weighting and stratification of training dataset, were compared to improve kNN-based forest growing stock estimates. When compared with five distance weights (0 to 2 by 0.5), the accuracy of kNN-based estimates was very similar ranged ${\pm}0.6m^3/ha$ in mean deviation. The training dataset were stratified by horizontal reference area (HRA) and forest cover type, which were applied by separately and combined. Even though the accuracy of estimates by combining forest cover type and HRA- 100 km was slightly improved, that by forest cover type was more efficient with sufficient number of training data. The mean of forest growing stock based kNN with HRA-100 and stratification by forest cover type when k=7 were somewhat underestimated ($5m^3/ha$) compared to statistical yearbook of forestry at 2011.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.7
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• pp.449-458
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• 2014

In the context-aware services, localization is an important technical element. Due to the easy to use and low cost, it was widely enabled with RF properties such as RSSI. However, RSSI is known to be not appropriated for indoor localization, because it tends to show big variance in time and is greatly effected with the multipath. This paper proposes a distance estimation process and its constituted methods for indoor localization, by making use of the other WLAN's RF property, CSI(Channel State Information). Firstly we define a comprehensive localization process, and suggest a calibration algorithm of environment factors in the path loss propagation model. Then, by implementing them with a commercial WLAN module, an the proposed process and methods are evaluated in terms of usefulness.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.114-122
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• 2011

We propose an automatic localization technique based on Uniform Arc Length Sampling (UALS) of 360 degree range sensor data. The proposed method samples 3D points from dense a point-cloud which is acquired by the sensor, registers the sampled points to a digital surface model(DSM) in real-time, and determines the location of an Unmanned Ground Vehicle(UGV). To reduce the sampling and registration time of a sequence of dense range data, 3D range points are sampled uniformly in terms of ground sample distance. Using the proposed method, we can reduce the number of 3D points while maintaining their uniformity over range data. We compare the registration speed and accuracy of the proposed method with a conventional sample method. Through several experiments by changing the number of sampling points, we analyze the speed and accuracy of the proposed method.