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Vincent Havyarimana,Zhu Xiao,Dong Wang 한국전자통신연구원 2016 ETRI Journal Vol.38 No.3
To improve the ability to determine a vehicle’s movement information even in a challenging environment, a hybrid approach called non-Gaussian square root-unscented particle filtering (nGSR-UPF) is presented. This approach combines a square root-unscented Kalman filter (SR-UKF) and a particle filter (PF) to determinate the vehicle state where measurement noises are taken as a finite Gaussian kernel mixture and are approximated using a sparse Gaussian kernel density estimation method. During an outage-free GPS period, the updated mean and covariance, computed using SR-UKF, are estimated based on a GPS observation update. During a complete GPS outage, nGSR-UPF operates in prediction mode. Indeed, because the inertial sensors used suffer from a large drift in this case, SR-UKF-based importance density is then responsible for shifting the weighted particles toward the high-likelihood regions to improve the accuracy of the vehicle state. The proposed method is compared with some existing estimation methods and the experiment results prove that nGSR-UPF is the most accurate during both outage-free and complete-outage GPS periods.
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Quantitative Evaluation of Sensor Reconfigurability Based on Data-driven Method
Dongnian Jiang,Wei Li 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.9
Sensor reconfigurability is the basis of the fault tolerance of a system. In order to improve the fault tolerance and reliability of sensors in the system, a method of quantitative evaluation of sensor reconfigurability based on a data-driven method is proposed. In this method, an analytic redundancy analysis of sensors in the system is carried out, and the analytic redundancy model is established. Based on this, the quantitative evaluation index of sensor reconfigurability is given by using the similarity evaluation method. Because the possible inaccuracy of the evaluation index results in uncertainties of the system, an adaptive threshold is designed to improve the evaluation accuracy. This method can quantitatively evaluate the reconfigurability of the sensor without depending on the system model, which provides a theoretical basis for improving the fault tolerance of the system at the design stage.
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더미 클래스를 가지는 열린 집합 얼굴 인식 방법의 유효성 검증에 대한 연구
안정호(Jung-Ho Ahn),최권택(KwonTaeg Choi) 한국디지털콘텐츠학회 2017 한국디지털콘텐츠학회논문지 Vol.18 No.3
The open set recognition method should be used for the cases that the classes of test data are not known completely in the training phase. So it is required to include two processes of classification and the validation test. This kind of research is very necessary for commercialization of face recognition modules, but few domestic researches results about it have been published. In this paper, we propose an open set face recognition method that includes two sequential validation phases. In the first phase, with dummy classes we perform classification based on sparse representation. Here, when the test data is classified into a dummy class, we conclude that the data is invalid. If the data is classified into one of the regular training classes, for second validation test we extract four features and apply them for the proposed decision function. In experiments, we proposed a simulation method for open set recognition and showed that the proposed validation test outperform SCI of the well-known validation method.
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