An attention residual network with triple feature as input is proposed to predict the remaining useful life (RUL) of bearings. First, the channel attention and spatial attention are connected in series into the residual connection of the residual neural network to obtain a new attention residual module, so that the newly constructed deep learning network can better pay attention to the weak changes of the bearing state. Secondly, the “triple feature” is used as the input of the attention residual network, so that the deep learning network can better grasp the change trend of bearing running state, and better realize the prediction of the RUL of bearing. Finally, The method is verified by a set of experimental data. The results show the method is simple and effective, has high prediction accuracy, and reduces manual intervention in RUL prediction.

This study investigated the distribution of attention to frontal space in augmented reality (AR). We conducted two experiments to compare binocular and monocular observation when an AR image was presented. According to a previous study, when participants observed an AR image in monocular presentation, they perceived the AR image as more distant than in binocular vision. Therefore, we predicted that attention would need to be shifted between the AR image and the background in not the monocular observation but the binocular one. This would enable an observer to distribute his/her visual attention across a wider space in the monocular observation. In the experiments, participants performed two tasks concurrently to measure the size of the useful field of view (UFOV). One task was letter/number discrimination in which an AR image was presented in the central field of view (the central task). The other task was luminance change detection in which dots were presented in the peripheral field of view (the peripheral task). Depth difference existed between the AR image and the location of the peripheral task in Experiment 1 but not in Experiment 2. The results of Experiment 1 indicated that the UFOV became wider in the monocular observation than in the binocular observation. In Experiment 2, the size of the UFOV in the monocular observation was equivalent to that in the binocular observation. It becomes difficult for a participant to observe the stimuli on the background in the binocular observation when there is depth difference between the AR image and the background. These results indicate that the monocular presentation in AR is superior to binocular presentation, and even in the best condition for the binocular condition the monocular presentation is equivalent to the binocular presentation in terms of the UFOV.

The wireless sensor network (WSN) is a technology that senses environmental information and provides appropriate services to users. There are diverse application areas for WSNs such as disaster prevention, military, and facility management. Despite the many prospective applications, WSN s are vulnerable to various malicious attacks. In false report attacks, a malicious attacker steals a few sensor nodes and obtains security materials such as authentication keys from the nodes. The attacker then injects false event reports to the network through the captured nodes. The injected false reports confuse users or deplete the limited energy of the nodes in the network. Many filtering schemes have been proposed to detect and remove false reports. In the statistical en route filtering (SEF) scheme, each node shares authentication keys selected from a global key pool. Due to the limited memory, each node is able to store only a small portion of the global key pool. Therefore, the routing paths of the event reports significantly affect the filtering (i.e., detecting) probability of false reports. In this paper, we propose a method to determine the routing paths of event reports both hop by hop and on demand at each node. In this method, each node chooses the next node on the path from the event source to the sink node based on the key indexes of its neighbor nodes. Experiments show that the proposed method is far more energy efficient than the SEF when the false traffic ratio (FTR) is ≥ 50% in the network.

Inverse filters can be designed in order to enhance the accuracy with which signals recorded in a given space can be reproduced in a given listening space. The problem is considered here of the design of an inverse filter matrix which enables K recorded signals to be accurately reproduced at K points in the listening space when transmitted via M loudspeaker channels. The analysis is sufficiently general to incorporate the case when the best (least squares) approximation is sought to the reproduction of K signals at L points in the space when LK. An analysis is presented which demonstrates that the approach suggested by the Multiple-Input/Output Inverse Filtering theorem of Miyoshi and Kaneda can be realised adaptively by using the Multiple Error LMS algorithm of Elliott et al.