• Annual Conference of KIPS
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• 2005.05a
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• pp.263-266
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• 2005

다중센서데이터 융합시스템(MSDFS)은 여러 센서로부터 획득된 이질의 데이터를 정규화된 포맷으로 융합하고 단일 센서에서의 획득오차를 최소한으로 줄여 표적의 정확한 식별 및 판단을 지원하는 시스템이다. 이 시스템들은 고유의 기능을 수행하는 모듈들에 대한 고수준의 재사용성을 요구하므로, 현재의 소프트웨어공학 기법을 적용시 공통부분에 대한 효율적 설계가 어렵다. 따라서 본 논문에서는 시스템 개발에 이러한 비효율적인 요소를 제거하는 제품-계열 개발방법론을 MSDFS의 임베디드 소프트웨어 설계에 적용한다. 이를 위해 분석 대상에 대한 영역지정에서부터 재사용가능한 컴포넌트의 식별까지 설계 하며, 마지막으로 설계된 모델에 대한 검증을 위해 GQM 패러다임을 적용한다. 또한 산출물에 대한 성능평가 기준을 제시하여 시스템 개발을 효과적으로 향상시킬 수 있는 방안을 제시한다.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1351-1367
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• 2018

Image registration is an essential process for image fusion, change detection and time series analysis using multi-sensor images. For this purpose, we need to detect accurately the difference of scale and rotation between the multi-sensor images with difference spatial resolution. In this paper, we propose a new feature matching method using variable circular template for image registration between multi-resolution images. The proposed method creates a circular template at the center of a feature point in a coarse scale image and also a variable circular template in a fine scale image, respectively. After changing the scale of the variable circular template, we rotate the variable circular template by each predefined angle and compute the mutual information between the two circular templates and then find the scale, the angle of rotation and the center location of the variable circular template, respectively, in fine scale image when the mutual information between the two circular templates is maximum. The proposed method was tested using Kompsat-2, Kompsat-3 and Kompsat-3A images with different spatial resolution. The experimental results showed that the error of scale factor, the error of rotation angle and the localization error of the control point were less than 0.004, $0.3^{\circ}$ and one pixel, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.76-81
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• 2009

We propose an effective emotion recognition algorithm based on the minimum classification error (MCE) incorporating multi-modal system The emotion recognition is performed based on a Gaussian mixture model (GMM) based on MCE method employing on log-likelihood. In particular, the reposed technique is based on the fusion of feature vectors based on voice signal and galvanic skin response (GSR) from the body sensor. The experimental results indicate that performance of the proposal approach based on MCE incorporating the multi-modal system outperforms the conventional approach.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.166-172
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• 2007

In this paper, we address the target positioning performance of Multi-Static sonar with respect to target positioning method and measurement error. Based on the analysis on two candidate solution approaches, namely, Least Square (LS) using range and angular information simultaneously and Maximum Likelihood (ML) using only range information as the existing information fusion methods for possible application to Multi-Static sonar, we propose to employ ML using range and angular information. Assuming that each sensor can receive range and angular information, we conduct representative comparison experiments over the existing and proposed methods under various measurement noise scenarios. We also investigate the target positioning performance according to number of sensors, distance between transmitter and receiver. According to the experimental results, RMSE of the proposed ML with distance and direction information is found to be more superior to ML using distance alone and to LS in case distance between transmitter and receiver is longer and number of receiver is smaller.

• Smart Structures and Systems
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• v.16 no.2
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• pp.295-328
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• 2015

In recent years the monitoring of structural behavior through acquisition of vibrational data has become common practice. In addition, recent advances in sensor development have made the collection of diverse dynamic information feasible. Other than the commonly collected acceleration information, Global Position System (GPS) receivers and non-contact, optical techniques have also allowed for the synchronous collection of highly accurate displacement data. The fusion of this heterogeneous information is crucial for the successful monitoring and control of structural systems especially when aiming at real-time estimation. This task is not a straightforward one as measurements are inevitably corrupted with some percentage of noise, often leading to imprecise estimation. Quite commonly, the presence of noise in acceleration signals results in drifting estimates of displacement states, as a result of numerical integration. In this study, a new approach based on a time domain identification method, namely the Unscented Kalman Filter (UKF), is proposed for correcting the "drift effect" in displacement or rotation estimates in an online manner, i.e., on the fly as data is attained. The method relies on the introduction of artificial white noise (WN) observations into the filter equations, which is shown to achieve an online correction of the drift issue, thus yielding highly accurate motion data. The proposed approach is demonstrated for two cases; firstly, the illustrative example of a single degree of freedom linear oscillator is examined, where availability of acceleration measurements is exclusively assumed. Secondly, a field inspired implementation is presented for the torsional identification of a tall tower structure, where acceleration measurements are obtained at a high sampling rate and non-collocated GPS displacement measurements are assumed available at a lower sampling rate. A multi-rate Kalman Filter is incorporated into the analysis in order to successfully fuse data sampled at different rates.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.431-444
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• 2014

As applications of spaceborne SAR imagery are extended, there are increased demands for accurate registrations for better understanding and fusion of radar images. It becomes common to adopt multi-resolution SAR images to apply for wide area reconnaissance. Geometric correction of the SAR images can be performed by using satellite orbit and attitude information. However, the inherent errors of the SAR sensor's attitude and ground geographical data tend to cause geometric errors in the produced SAR image. These errors should be corrected when the SAR images are applied for multi-temporal analysis, change detection applications and image fusion with other sensor images. The undesirable ground registration errors can be corrected with respect to the true ground control points in order to produce complete SAR products. Speeded Up Robust Feature (SURF) technique is an efficient algorithm to extract ground control points from images but is considered to be inappropriate to apply to SAR images due to high speckle noises. In this paper, an attempt is made to apply SURF algorithm to SAR images for image registration and fusion. Matched points are extracted with respect to the varying parameters of Hessian and SURF matching thresholds, and the performance is analyzed by measuring the imaging matching accuracies. A number of performance measures concerning image registration are suggested to validate the use of SURF for spaceborne SAR images. Various simulations methodologies are suggested the validate the use of SURF for the geometric correction and image registrations and it is shown that a good choice of input parameters to the SURF algorithm should be made to apply for the spaceborne SAR images of moderate resolutions.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.315-328
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• 2024

Purpose: The purpose of this paper is to present a service construction plan using multiple complex sensor information to detect abnormal situations in urban life safety that are difficult to identify on CCTV. Method: This study selected service scenarios based on actual testbed data and analyzed service importance for local government control center operators, which are main users. Result: Service scenarios were selected as detection of day and night dynamic object, Detection of sudden temperature changes, and Detection of time-series temperature changes. As a result of AHP analysis, walking and mobility collision risk situation services and fire foreshadowing detection services leading to immediate major disasters were highly evaluated. Conclusion: This study is significant in proposing a plan to build an anomaly detection service that can be used in local governments based on real data. This study is significant in proposing a plan to build an anomaly detection service that can be used by local governments based on testbed data.

• Annual Conference of KIPS
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• 2017.04a
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• pp.24-25
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• 2017

최근 다양한 3차원 뎁스 센서의 등장은, 3차원 캐릭터가 사람의 움직임에 따라 실시간으로 애니메이션되도록 하였다. 하지만, 센서에 따라, 공간상의 뎁스를 얻는 방식이 다르며, 이 결과 캡처를 가능하는 센싱영역 또한 뎁스의 종류에 따라 많은 차이를 보여 왔다. 본 논문은 두 가지 방식의 멀티의 센서를 결합하여, 동시에 실시간으로 사용함으로서, 하나의 센서만을 사용했을 경우 얻을 수 없는 조인트에 대한 정보를 얻음으로서, 자세한 캐릭터에 대한 스켈레톤을 애니메이션 하는 방법을 제안한다.

• The Journal of Korea Robotics Society
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• v.6 no.2
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• pp.182-188
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• 2011

This paper describes a method of road tracking by using a vision and laser with extracting road boundary (road lane and curb) for navigation of intelligent transport robot in structured road environments. Road boundary information plays a major role in developing such intelligent robot. For global navigation, we use a global positioning system achieved by means of a global planner and local navigation accomplished with recognizing road lane and curb which is road boundary on the road and estimating the location of lane and curb from the current robot with EKF(Extended Kalman Filter) algorithm in the road assumed that it has prior information. The complete system has been tested on the electronic vehicles which is equipped with cameras, lasers, GPS. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision system by our approach for detecting the curb of road and lane boundary detection.

• Annual Conference of KIPS
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• 2023.05a
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• pp.298-300
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• 2023

자율주행 및 robot navigation 시스템에서 물체 인식 성능향상을 위해 대부분 MSF(Multi-Sensor Fusion) 기반 설계를 한다. 따라서 각 센서로부터 들어온 정보를 정합하는 것은 정확한 MSF 알고리즘을 위한 필요조건이다. 다양한 선행 연구에서 2D 데이터에 대한 공격을 진행했다. 자율주행에서는 3D 데이터를 다루어야 하므로 선행 연구에서 하지 않았던 3D 데이터 공격을 진행했다. 본 연구에서는 스케일링 공격 기반 카메라-라이다 센서 간 정합 모델의 정확도를 저하시키는 공격 방법을 제안한다. 제안 방법은 입력 라이다의 포인트 클라우드에 스케일링 공격을 적용하여 다운스케일링 단계에서 공격하고자 한다. 실험 결과, 입력 데이터에 공격하였을 때 공격 전보다 평균제곱 이동오류는 56% 이상, 평균 사원수 각도 오류는 98% 이상 증가했음을 보였다. 다운스케일링 크기 별, 알고리즘별 공격을 적용했을 때, 10×20 크기로 다운스케일링 하고 lanczos4 알고리즘을 적용했을 때 가장 효과적으로 공격할 수 있음을 확인했다.