This study aims to address the issue of low signal-to-noise ratio and inaccuracy in feedwater flow measurement values of steam generators in nuclear power plants under low-load operating conditions. It proposes the use of the Extended Kalman Filter algorithm to estimate the feedwater flow and the differential pressure across the valve. The system states are defined as the feedwater flow and valve differential pressure. Based on the valve flow characteristic curve and pressure difference calculation formula, a nonlinear system model with a recursive characteristic is constructed. This model incorporates the coupling relationship between flow and pressure difference to facilitate the application of the Extended Kalman Filter algorithm in valve state estimation. Simulation results validate that the algorithm based on this model exhibits high robustness and effectiveness under various noise conditions, making it suitable for real-time estimation of valve states.

An automatic and accurate state recognition of switches on electrical cabinet control panels plays an increasingly important role in the routine inspection of power equipment. This paper presents a novel method for real-time cabinet panel switch state recognition using hybrid visual features. Compared to traditional methods, the proposed approach can ensure the rectangular object regions from images captured at arbitrary angles by applying the perspective transformation model. Besides, the switch regions are segmented and the corresponding visual features are extracted on HSV space, instead of raw RGB space, which overcomes the illumination variability issues. The morphological operations and the inherent geometrical constraints, are employed to group the switch regions. Eventually, the switch recognition is implemented on high-dimensional vector space using feature similarity discriminants. The proposed method has been evaluated on the image dataset collected from power station cabinets. The experimental results verify the effectiveness of the method.

In recent years, consumer-level depth cameras have been adopted for various applications. However, they often produce depth maps at only a moderately high frame rate (approximately 30 frames per second), preventing them from being used for applications such as digitizing human performance involving fast motion. On the other hand, low-cost, high-frame-rate video cameras are available. This motivates us to develop a hybrid camera that consists of a high-frame-rate video camera and a low-frame-rate depth camera and to allow temporal interpolation of depth maps with the help of auxiliary color images. To achieve this, we develop a novel algorithm that reconstructs intermediate depth maps and estimates scene flow simultaneously. We test our algorithm on various examples involving fast, non-rigid motions of single or multiple objects. Our experiments show that our scene flow estimation method is more precise than a tracking-based method and the state-of-the-art techniques.

Tobacco loosening and resurgence process has strong nonlinearity and large delay-time ratio, and it is difficult to describe the process via accurate mathematical models. Thus, it is difficult to control the outlet tobacco moisture percentage (OTMP) for loosening and resurgence machine by operators. To solve these problems, combining the expert knowledge and operation experience, an intelligent control method based on case-based reasoning (CBR) is presented for loosening and resurgence machine. The intelligent control method is applied in certain cigarette plant which shows that the OTMP is controlled in the scope of target value and water consumption is reduced.