In this paper, the long-term reliability of swash plate type hydraulic pump is studied by prognostics method. For the purpose, the pumping power of hydraulic pump is measured for 00 cycles and the performance after 00 cycles is estimated using the particle filter method. To verify the predicted 00 cycle's performance, the actual test results are compared with the estimated result and the trend of estimation is well matched with actual test results. The long-term reliability evaluation using the prognostics method performed in this study shows the feasibility that can be utilized in development phase of tracked vehicle to improve the quality of initial products.

For several years, keyboard and mouse have been used as the main interacting devices between users and computer games, but they are becoming outdated. Gesture-based human-computer interaction systems are becoming more popular owing to the emergence of virtual reality and augmented reality technologies. Therefore research on these systems has attracted a significant attention. The researches focus on designing the interactive interfaces between users and computers. Human-computer interaction is an important factor in computer games because it affects not only the experience of the users, but also the design of the entire game. In this research, we develop an particle filter-based face tracking method using color distributions as features, for the purpose of applying to gesture-based human-computer interaction systems for computer games. The experimental results proved the efficiency of particle filter and color features in face tracking, showing its potential in designing human-computer interactive games.

PURPOSES: The nonlinear model of fatigue cracking is typically used for determining the maintenance period. However, this requires that the model parameters be known. In this study, the particle filter (PF) method was used to determine various statistical parameters such as the mean and standard deviation values for the nonlinear model of fatigue cracking. METHODS: The PF method was used to determine various statistical parameters for the nonlinear model of fatigue cracking, such as the mean and standard deviation. RESULTS : On comparing the values obtained using the PF method and the least square (LS) method, it was found that PF method was suitable for determining the statistical parameters to be used in the nonlinear model of fatigue cracking. CONCLUSIONS : The values obtained using the PF method were as accurate as those obtained using the LS method. Furthermore, reliability design can be applied because the statistical parameters of mean and standard deviation can be obtained through the PF method.

최근 공간정보 및 컴퓨터기술의 발달과 함께 시공간적인 토양침식의 프로세스를 구현할 수 있는 다양한 물리적 기반의 모델이 개발되고 있다. 비록 물리적 기반의 토양침식모델이 다양한 지점에서 다양한 형태로 발생하고 있는 침식, 이송 및 퇴적에 관한 일련의 정보를 제공하지만, 파라메타, 모델의 구조 및 관측 자료의 불확실성 등으로 인하여 모델을 예측 혹은 특정 목적을 위하여 활용하는 경우에는 많은 어려움이 있다. 따라서 본 연구에서는 유역기반의 토양침식모델(CSEM)의 최적 파라메타의 추정 및 그 불확실성을 평가하기 위하여 자료동화기법 중의 하나인 파티클 필터를 적용하였다. 파티클 필터를 CSEM과 연계한 모형(CSEM-PF)은 비선형 시스템의 특성을 갖는 물리적 기반 모형인 CSEM의 파라메타를 추정하기 위하여 매 시간의 관측 유량 및 관측 유사량을 활용하여 각각의 가중치를 계산하고, 이를 바탕으로 필터링을 수행하여 유출량 및 유사량과 관련된 다양한 파라메타를 추정하였다. 또한 이를 통하여 각 파라메타에 대한 불확실성 뿐만 아니라, 시변성을 갖는 파라메타에 대한 특성을 고려할 수 있음을 확인하였다. CSEM-PF를 용담댐의 소유역을 대상으로 과거의 기록적인 3개의 태풍에 의하여 발생한 사상에 적용하여, 각 사상에 대한 최적의 파라메타를 추정하고, 그에 대한 불확실성 분석을 수행하였다.

This paper presents a probabilistic head tracking method, mainly applicable to face recognition and human robot interaction, which can robustly track human head against various variations such as pose/scale change, illumination change, and background clutters. Compared to conventional particle filter based approaches, the proposed method can effectively track a human head by regularizing the sample space and sequentially weighting multiple visual cues, in the prediction and observation stages, respectively. Experimental results show the robustness of the proposed method, and it is worthy to be mentioned that some proposed probabilistic framework could be easily applied to other object tracking problems.

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, due to the usage of the accurate sensors, distinct particles which compensate one another are attenuated as the RBPF-SLAM continues. To avoid this particle depletion, we propose the strategic games to assign the particle’s payoff which replaces the importance weight in the current RBPF-SLAM framework. From simulation works, we show that RBPF-SLAM with the strategic games is inconsistent in the pessimistic way, which is different from the existing optimistic RBPF-SLAM. In addition, first, the estimation errors with applying the strategic games are much less than those of the standard RBPF-SLAM, and second, the particle depletion is alleviated.

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze the result representation of SLAM using RBPF (RBPF-SLAM) when particle diversity is preserved. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. Thus, we propose several result representations and provide the analysis of the representations. For the analysis, estimation errors and their variances, and consistency of RBPF-SLAM are dealt in this study. According to the simulation results, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-object tracking. Also, the method is applied to the intelligent environment and its performance is verified by the experiments.