• 제어로봇시스템학회논문지
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• 제13권8호
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• pp.754-759
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• 2007

In this paper, we proposed the Bi-Modal Sensor Fusion Algorithm which is the emotional recognition method that be able to classify 4 emotions (Happy, Sad, Angry, Surprise) by using facial image and speech signal together. We extract the feature vectors from speech signal using acoustic feature without language feature and classify emotional pattern using Neural-Network. We also make the feature selection of mouth, eyes and eyebrows from facial image. and extracted feature vectors that apply to Principal Component Analysis(PCA) remakes low dimension feature vector. So we proposed method to fused into result value of emotion recognition by using facial image and speech.

• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• 제어로봇시스템학회논문지
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• 제16권7호
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• pp.639-645
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• 2010

This paper presents the sensor fusion algorithm that recognizes a primary vehicle by fusing radar and monocular vision data. In general, most of commercial radars may lose tracking of the primary vehicle, i.e., the closest preceding vehicle in the same lane, when it stops or goes with other preceding vehicles in the adjacent lane with similar velocity and range. In order to improve the performance degradation of radar, vehicle detection information from vision sensor and path prediction predicted by ego vehicle sensors will be combined for target classification. Then, the target classification will work with probabilistic association filters to track a primary vehicle. Finally the performance of the proposed sensor fusion algorithm is validated using field test data on highway.

• 한국정보통신학회논문지
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• 제12권7호
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• pp.1202-1208
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• 2008

본 논문에서는 유삭식 심해무인잠수정인 해미래와 진수장치인 해누비로 이루어지는 심해무인잠수정 시스템의 항법 알고리즘에 사용되는 다중 센서 융합 기법에 대하여 소개하고 있다. 수중 위치 추적 시스템의 성능은 초단기선, 장기선, 고도계와 같은 수중 음향 센서의 성능에 의해 결정되는데 수중음향 신호는 다양한 형태의 노이즈를 가지고 있어 특별한 주의가 요망된다. 본 논문에서는 이동 관측창 개념을 이용한 실용적인 다중 센서 융합 알고리즘을 제안하였다. 해미래의 동해 실해역 시험을 통해 획득된 계측치에 본 알고리즘을 적용하여 그 성능을 고찰한 결과 우수한 성능을 보임을 알 수 있었다.

• 전자공학회논문지
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• 제52권5호
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• pp.190-196
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• 2015

본 논문은 항법장치 위치보정을 위해 칼만필터를 적용한 GPS/INS융합의 다중보정방법을 제안한다. 연구에서는 관성항법장치를 구현하기 위해 9축 항법장치로 보정알고리즘을 적용하여 위치오차를 감소시킨 방법을 적용했다. 일반적으로 GPS/INS는 위치정보를 얻어낼 수 있지만 위치정보를 구하는 과정에서 오차 또한 더불어 커지게 되기에 이를 보정하기 위한 강인한 오차 보정 알고리즘이 필요하다. 본 논문에서는 9축 관성센서(mpu-9150)의 외란에 대한 강인성 향상을 위해 가속도계 보정 알고리즘을 사용하여 tilt보정을 수행했으며, 제어 대상체의 정확한 방위를 파악할 수 있도록 Yaw각 재정의 알고리즘을 적용하였다. 최종적으로 GPS/INS와 칼만 필터를 함께 결합한 통합시스템을 구현하였다.

• Journal of Information Processing Systems
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• 제12권1호
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• pp.35-45
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• 2016

To resolve the problems of Poisson/impulse noise, blurriness, and sharpness in degraded X-ray images, a novel and efficient enhancement algorithm based on X-ray image fusion using a discrete wavelet transform is proposed in this paper. The proposed algorithm consists of two basics. First, it applies the techniques of boundary division to detect Poisson and impulse noise corrupted pixels and then uses the Wiener filter approach to restore those corrupted pixels. Second, it applies the sharpening technique to the same degraded X-ray image. Thus, it has two source X-ray images, which individually preserve the enhancement effects. The details and approximations of these sources X-ray images are fused via different fusion rules in the wavelet domain. The results of the experiment show that the proposed algorithm successfully combines the merits of the Wiener filter and sharpening and achieves a significant proficiency in the enhancement of degraded X-ray images exhibiting Poisson noise, blurriness, and edge details.

• 한국항행학회논문지
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• 제15권5호
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• pp.696-704
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• 2011

본 논문에서는 단일 카메라와 초음파센서 데이터를 융합하는 이동 로봇 주행제어 알고리즘을 제안하였다. 이진화 영상처리를 위한 임계값을 영상 정보와 초음파센서 정보를 이용하는 퍼지추론기법으로 설정하였다. 임계값을 상황에 따라 가변하면 조도가 낮은 환경에서도 장애물 인식이 향상된다. 카메라 영상 정보와 초음파 센서 정보를 융합하여 장애물에 대한 격자지도를 생성하고 원궤적 경로기법으로 장애물을 회피하도록 한다. 제안된 알고리즘의 성능을 입증하기 위하여 조도가 낮은 실내와 좁은 복도에서 Pioneer 2-DX 이동로봇의 주행제어에 적용하였다.

• 한국항공우주학회지
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• 제48권7호
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• pp.513-522
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• 2020

본 논문은 PSD와 이종 센서 융합을 이용한 상대 항법 알고리즘에 대해 기술한다. 추종 시스템(Chaser)과 목표 시스템(Target) 간의 상대 항법을 수행하기 위해 하드웨어 시스템을 구축하고 알고리즘을 설계하여 시뮬레이션을 수행하였다. 이를 통해 상대 거리에 따른 오차 발생 경향을 확인하여 이종 센서 융합에 대한 분석을 수행하였다. 이후 구축한 하드웨어 시스템으로 지상 시험 환경을 구성하여 측정값을 획득하고 이를 후처리하여 상대 항법 알고리즘의 성능을 최종적으로 확인하였다.

• 제어로봇시스템학회논문지
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• 제17권10호
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• pp.995-1005
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• 2011

An efficient outdoor local localization method is suggested using multi-sensor fusion with MU-EKF (Multi-Update Extended Kalman Filter) for car-like mobile robots. In outdoor environments, where mobile robots are used for explorations or military services, accurate localization with multiple sensors is indispensable. In this paper, multi-sensor fusion outdoor local localization algorithm is proposed, which fuses sensor data from LRF (Laser Range Finder), Encoder, and GPS. First, encoder data is used for the prediction stage of MU-EKF. Then the LRF data obtained by scanning the environment is used to extract objects, and estimates the robot position and orientation by mapping with map objects, as the first update stage of MU-EKF. This estimation is finally fused with GPS as the second update stage of MU-EKF. This MU-EKF algorithm can also fuse more than three sensor data efficiently even with different sensor data sampling periods, and ensures high accuracy in localization. The validity of the proposed algorithm is revealed via experiments.

• 제어로봇시스템학회논문지
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• 제17권3호
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• pp.229-235
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• 2011

To make a robust object tracking and identifying system for an intelligent robot and/or home system, heterogeneous sensor fusion between visible ray system and infrared ray system is proposed. The proposed system separates the object by combining the ROI (Region of Interest) estimated from two different images based on a heterogeneous sensor that consolidates the ordinary CCD camera and the IR (Infrared) camera. Human's body and face are detected in both images by using different algorithms, such as histogram, optical-flow, skin-color model and Haar model. Also the pose of human body is estimated from the result of body detection in IR image by using PCA algorithm along with AdaBoost algorithm. Then, the results from each detection algorithm are fused to extract the best detection result. To verify the heterogeneous sensor fusion system, few experiments were done in various environments. From the experimental results, the system seems to have good tracking and identification performance regardless of the environmental changes. The application area of the proposed system is not limited to robot or home system but the surveillance system and military system.