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The temperature field of stator strands with a transposed structure is challenging to analyze in large machines. In order to deal with this problem, this article presents a three-dimensional thermal network model of stator transposed strands for hydrogenerator. Take a 250-MW hydrogenerator as an example, and a novel thermal network model is established. In the proposed model, the solving domain is discretized according to the real physical structure, the transposition structure of stator strands is taken into account, and the change of heat sources caused by transposed strands is considered. Next, results of the proposed model are compared with those of the traditional thermal network without considering the transposition structure, and the proposed model can better model the real structure of the stator strands. Then, the effect of a different number of ventilation ducts on temperature distribution is discussed. Besides, the calculation results were compared with the finite-element method, and the proposed method took less time to calculate. Finally, the temperature of the stator was measured by the embedded temperature sensor, and the correctness of the calculation results of the proposed model is verified. The novel three-dimensional thermal network model can quickly and accurately calculate the temperature of stator transposed strands. [ABSTRACT FROM AUTHOR]

In this paper, we are devoted to solving the problem of crossing surveillance and mobile phone visual location recognition, especially for the case that the query and reference images are captured by mobile phone and surveillance camera, respectively. Besides, we also study the influence of the environmental condition variations on this problem. To explore that problem, we first build a cross-device location recognition dataset, which includes images of 22 locations taken by mobile phones and surveillance cameras under different time and weather conditions. Then based on careful analysis of the problems existing in the data, we specifically design a method which unifies an unsupervised subspace alignment method and the semi-supervised Laplacian support vector machine. Experiments are performed on our dataset. Compared with several related methods, our method shows to be more efficient on the problem of crossing surveillance and mobile phone visual location recognition. Furthermore, the influence of several factors such as feature, time, and weather is studied. [ABSTRACT FROM PUBLISHER]

Common action recognition methods describe an action sequence along with its time axis, i.e., first extracting features from the $x y$ plane, and then modeling the dynamic changes along with the time axis. Other than the ordinary $x y$ plane-based representation, other views, e.g., $x t$ slice-based representation, may be more efficient to distinguish different actions. In this paper, we investigate different slicing views of the spatiotemporal volume to organize action sequences and propose an efficient slice representation for human action recognition. First, a minimum average entropy principle is proposed to select the optimal slicing angle for each action sequence adaptively. This allows the foreground pixels to be distributed in the fewest slices so as to reduce more uncertainty caused by the information dispersed in different slices. Then, the obtained slice sequence is transformed into a pair of 1-D signals to describe the distribution of foreground pixels along the time axis. Finally, the mel frequency cepstrum coefficient features are calculated to describe the spectrum characteristics of the 1-D signals over time. Thus, a 3-D spatiotemporal action volume is efficiently transformed into a low-dimensional spectrum features. Extensive experiments on the 2-D human action data sets (the UIUC and the WEIZMANN) as well as the Microsoft Research (MSR) Action3-D depth data set demonstrate the effectiveness of the slice-based representation, where the recognition performance can reach to the state-of-the-art level with high efficiency. [ABSTRACT FROM PUBLISHER]