Multi-view information fusion can provide more accurate, complete and reliable data descriptions for monitoring objects, effectively improve the limitations and unreliability of single-view data. Existing multi-view information fusion based on deep learning mostly focuses on the feature level and decision level, with large information loss, and does not distinguish the view weight in the fusion process. To this end, a multi-view data level information fusion model CAM_MCFCNN with view weight was proposed based on a channel attention mechanism and convolutional neural network. The model used the channel characteristics to implement multi-view information fusion at the data level stage, which made the fusion position and mode more natural and reduced the loss of information. A multi-channel fusion convolutional neural network was used for feature learning. In addition, the channel attention mechanism was used to learn the view weight, so that the algorithm could pay more attention to the views that contribute more to the fault identification task during the training process, and more reasonably integrate the information of different views. The proposed method was verified by the data of the planetary gearbox experimental platform. The multi-view data and single-view data were used as the input of the CAM_MCFCNN model and single-channel CNN model respectively for comparison. The average accuracy of CAM_MCFCNN on three constant-speed datasets reached 99.95%, 99.87% and 99.92%, which was an improvement of 0.95%, 2.25%, and 0.04%, compared with the single view with the highest diagnostic accuracy, respectively. When facing limited samples, CAM_MCFCNN had similar performance. Finally, compared with different multi-view information fusion algorithms, CAM_MCFCNN showed better stability and higher accuracy. The experimental results showed that the proposed method had better performance, higher diagnostic accuracy and was more reliable, compared with other methods.
Although self-perceived language proficiency has recently been found to influence foreign language enjoyment (FLE), rigorous assessment of the causal relationship between actual second language (L2) achievement and FLE has received relatively little attention. Based on control-value theory, this longitudinal study examined the causal antecedents of the relationship between the L2 achievement of 206 FL learners and their FLE from the perspective of dynamic systems theory and conducted a cross-lagged panel (CLP) analysis using Mplus 8.3 software. Both variables were measured two times over one academic year (10 months) in an English as a foreign language (EFL) course. The Wilcoxon signed-rank test showed significant changes in both variables over time. According to the CLP path model, L2 achievement at Time 1 (T1) appeared to affect subsequent FLE, while FLE at T1 failed to predict L2 achievement at Time 2 (T2). This study provides empirical evidence of the directional effect of L2 achievement on FLE regarding the hypothesized reciprocal effect of the two. Implications for stakeholders in the field of education are discussed.
Objective image quality assessment (IQA) aims to develop computational models to predict the perceptual image quality consistent with subjective evaluations. As image information is presented by the change in intensity values in the spatial domain, the gradient, as a basic tool for measuring the change, is widely used in IQA models. However, does the change measured by the gradient actually correspond to the change perceived by the human visual system (HVS)? To explore this issue, in this paper, we analyze how the ability of the HVS to perceive changes is affected by the upper threshold, and we propose an IQA index based on an adaptively truncating gradient. Specifically, the upper threshold at each pixel in an image is adaptively determined according to the image content, and the adaptively truncating gradient is obtained by retaining the part of the gradient magnitude that is less than the upper threshold and truncating the part that is greater than the upper threshold. Then, the distorted image quality is calculated by comparing the similarity of the adaptively truncating gradient between a reference image and the distorted image. Experimental results on six benchmark databases demonstrate that the proposed index correlates well with human evaluations.
Dissemination of Chinese culinary culture is important for Chinese culture to go global. Shannxi, a western province of China, features abundant cuisine which is representative of Shannxi local’s lifestyle. However, there are many spelling mistakes and other translation mistakes in some time-honoured Chinese restaurants’ English menu. Appropriate translation strategies, including domestication, foreignization and Pinyin, a phonetic transcription system for Chinese characters,should be adopted to explore practical and feasible translation of the culinary culture.
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