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In order to solve the problem of fault diagnosis of switching power supply in medical equipment due to the lack of drawings, a fault diagnosis method based on feature fusion is proposed. In this paper, long short-term memory network and one-dimensional convolution neural network in deep learning are used to extract features of different dimensions and to adaptively fuse them into feature vectors. A fault intelligent diagnosis model based on feature fusion is established. At the same time, Adam algorithm is used to adaptively optimize the model to realize automatic recognition of fault state of switching power supply. Finally, taking the switch power supply of Mindray T8 monitor as an example, it is verified that the proposed method can effectively and accurately identify the common faults of the switch power supply without drawings, and the accuracy and accuracy of the model are better than the fault diagnosis method based on a single feature.

Objective: This study aimed to perform experiments to investigate the change trend in brain magnetic induction phase shift (MIPS) during hemorrhagic shock of different degrees of severity and to find the correlation between brain MIPS value and commonly used physiological indicators for detecting shock so as to explore a noninvasive method suitable for prehospital real-time detection of cerebral blood perfusion in hemorrhagic shock. Approach: The self-developed MIPS detection system was used to monitor the brain MIPS value in the whole process of hemorrhagic shock models of rabbits with different degrees of severity (control, mild, moderate, and severe) of shock in real time. Meanwhile, common physiological parameters, including arterial blood lactate (ABL), mean arterial pressure (MAP), heart rate (HR),core body temperature (CBT), regional cerebral blood flow (rCBF), and electroencephalogram (EEG), were also evaluated. Main results: The findings suggested that the brain MIPS value showed a downward trend in the shock process, and the decline degree of the MIPS value positively correlated with the severity of shock. Moreover, it showed a good detection and resolution ability in time/process and severity (P < 0.05). The MIPS values significantly correlated with ABL (P < 0.01), CBT (P < 0.01), and EEG (P < 0.05) at all four shock levels; with MAP (P < 0.05) and rCBF (P < 0.05) in the control, moderate, and severe groups; and with HR (P < 0.01) only in the severe group. Significance: The results demonstrated that the brain MIPS value has the capability of detecting hemorrhagic shock. The MIPS technique is a noninvasive method suitable for prehospital real-time detection of cerebral blood perfusion in hemorrhagic shock.

How to avoid the self-occlusion of a moving object is a challenging problem. An approach for dynamically avoiding self-occlusion is proposed based on the depth image sequence of moving visual object. Firstly, two adjacent depth images of a moving object are acquired and each pixel’s 3D coordinates in two adjacent depth images are calculated by utilizing antiprojection transformation. On this basis, the best view model is constructed according to the self-occlusion information in the second depth image. Secondly, the Gaussian curvature feature matrix corresponding to each depth image is calculated by using the pixels’ 3D coordinates. Thirdly, based on the characteristic that the Gaussian curvature is the intrinsic invariant of a surface, the object motion estimation is implemented by matching two Gaussian curvature feature matrices and using the coordinates’ changes of the matched 3D points. Finally, combining the best view model and the motion estimation result, the optimization theory is adopted for planning the camera behavior to accomplish dynamic self-occlusion avoidance process. Experimental results demonstrate the proposed approach is feasible and effective.

This paper presents a novel strategy for developing wind power in large-scale (multi-GW) wind farms in China. It involves combining oversized wind farms, large-scale electrical storage and long-distance transmission lines to deliver ‘baseload wind power’ to distant electricity demand centers. Baseload wind power is typically more valuable to the electric utility than intermittent wind power, so that storage can be economically attractive even in instances where the cost per kWh is somewhat higher than without storage. The prospective costs for this approach to developing wind power are illustrated by modifying an oversized wind farm at Huitengxile, Inner Mongolia. The site has an average power density of 580 W/m2 at 50 m hub heights and is located 500 km north of Beijing. Using locally mass-produced wind turbines there are good prospects that wind power would be cost-competitive with coal power, on a lifecycle cost basis, while providing substantial net environmental benefits. Finally, the institutional challenges related to the prospect of large-scale wind energy development are addressed. Especially important are policies aimed at developing the capacity for mass production of as much of this technology in China as is feasible. Promising instruments for speeding up the introduction of this technology include: (i) international joint ventures between foreign vendors and developers and Chinese manufacturers; and (ii) wind resource development concessions.