CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective

Remote sensing scene objective recognition (RSSOR) plays a serious application value in both military and civilian fields. Convolutional neural networks (CNNs) have greatly enhanced the improvement of intelligent objective recognition technology for remote sensing scenes, but most of the methods using CNN for high-resolution RSSOR either use only the feature map of the last layer or directly fuse the feature maps from various layers in the "summation" way, which not only ignores the favorable relationship information between adjacent layers but also leads to redundancy and loss of feature map, which hinders the improvement of recognition accuracy. In this study, a contextual, relational attention-based recognition network (CRABR-Net) was presented, which extracts different convolutional feature maps from CNN, focuses important feature content by using a simple, parameter-free attention module (SimAM), fuses the adjacent feature maps by using the complementary relationship feature map calculation, improves the feature learning ability by using the enhanced relationship feature map calculation, and finally uses the concatenated feature maps from different layers for RSSOR. Experimental results show that CRABR-Net exploits the relationship between the different CNN layers to improve recognition performance, achieves better results compared to several state-of-the-art algorithms, and the average accuracy on AID, UC-Merced, and RSSCN7 can be up to 96.46%, 99.20%, and 95.43% with generic training ratios.

A Real Time Method Based on Deep Learning for Reconstructing Holographic Acoustic Fields from Phased Transducer Arrays

Phased transducer arrays (PTA) can control ultrasonic waves to produce a holographic acoustic field. However, obtaining the phase of the corresponding PTA from a given holographic acoustic field is an inverse propagation problem, which is a mathematically unsolvable nonlinear system. Most of the existing methods use iterative methods, which are complex and time-consuming. To better solve this problem, this paper proposed a novel method based on deep learning to reconstruct the holographic sound field from PTA. For the imbalance and randomness of the focal point distribution in the holographic acoustic field, we constructed a novel neural network structure incorporating attention mechanisms to focus on useful focal point information in the holographic sound field. The results showed that the transducer phase distribution obtained from the neural network fully supports the PTA to generate the corresponding holographic sound field, and the simulated holographic sound field can be reconstructed with high efficiency and quality. The method proposed in this paper has the advantage of real-time performance that is difficult to achieve by traditional iterative methods and has the advantage of higher accuracy compared with the novel AcousNet methods.

Multiscale Convolutional Neural Network Based on Channel Space Attention for Gearbox Compound Fault Diagnosis

Gearboxes are one of the most widely used speed and power transfer elements in rotating machinery. Highly accurate compound fault diagnosis of gearboxes is of great significance for the safe and reliable operation of rotating machinery systems. However, traditional compound fault diagnosis methods treat compound faults as an independent fault mode in the diagnosis process and cannot decouple them into multiple single faults. To address this problem, this paper proposes a gearbox compound fault diagnosis method. First, a multiscale convolutional neural network (MSCNN) is used as a feature learning model, which can effectively mine the compound fault information from vibration signals. Then, an improved hybrid attention module, named the channel-space attention module (CSAM), is proposed. It is embedded into the MSCNN to assign weights to multiscale features for enhancing the feature differentiation processing ability of the MSCNN. The new neural network is named CSAM-MSCNN. Finally, a multilabel classifier is used to output single or multiple labels for recognizing single or compound faults. The effectiveness of the method was verified with two gearbox datasets. The results show that the method possesses higher accuracy and stability than other models for gearbox compound fault diagnosis.

Multi-Object Detection in Security Screening Scene Based on Convolutional Neural Network

The technique for target detection based on a convolutional neural network has been widely implemented in the industry. However, the detection accuracy of X-ray images in security screening scenarios still requires improvement. This paper proposes a coupled multi-scale feature extraction and multi-scale attention architecture. We integrate this architecture into the Single Shot MultiBox Detector (SSD) algorithm and find that it can significantly improve the effectiveness of target detection. Firstly, ResNet is used as the backbone network to replace the original VGG network to improve the feature extraction capability of the convolutional neural network for images. Secondly, a multi-scale feature extraction (MSE) structure is designed to enrich the information contained in the multi-stage prediction feature layer. Finally, the multi-scale attention architecture (MSA) is fused onto the prediction feature layer to eliminate the redundant features' interference and extract effective contextual information. In addition, a combination of Adaptive-NMS and Soft-NMS is used to output the final prediction anchor boxes when performing non-maximum suppression. The results of the experiments show that the improved method improves the mean average precision (mAP) value by 7.4% compared to the original approach. New modules make detection much more accurate while keeping the detection speed the same.

A Radio Environment Map Updating Mechanism Based on an Attention Mechanism and Siamese Neural Networks

A radio environment map (REM) is an effective spectrum management tool. With the increase in the number of mobile devices, the wireless environment changes more and more frequently, bringing new challenges to REM updates. Traditional update methods usually rely on the amount of data collected for updating without paying attention to whether the wireless environment has changed enough. In particular, a waste of computational resources results from the frequently updated REM when the wireless environment does not change much. When the wireless environment changes a lot, the REM is not updated promptly, resulting in a decrease in REM accuracy. To overcome the above problems, this work combines the Siamese neural network and an attention mechanism in computer vision and proposes an update mechanism based on the amount of wireless environmental change starting from image data. The method compares the newly collected crowdsourced data with the constructed REM in terms of similarity. It uses similarity to measure the necessity of the REM to be updated. The algorithm in this paper can achieve a controlled update by setting a similarity threshold with good controllability. In addition, the effectiveness of the algorithm in detecting changes of the wireless environment has been demonstrated by combing simulation data.

Personality coherence as a personality dynamics-related concept

Extant theoretical models of personality coherence/incoherence do not sufficiently address the challenge of explaining personality coherence dynamics and the role of psychological mechanisms, including temperament and attention. To overcome these limitations, the Complex-System Approach to Personality (C-SAP) postulates that personality coherence is a within-person structure that arises from the functional consistency/inconsistency between personality traits/types, underlain by specific attentional and temperament mechanisms that have integrative and regulatory potential. The dominant (reactive, regulative) function of stimulation processing in temperament types is the foundation for assessing personality coherence. This paper presents a revised, fine-grained model of personality coherence - originally arising from the C-SAP - that is enriched by a focus on personality coherence dynamics in relation to behavioral consistency. The methodological principles necessary for studying personality coherence dynamics are outlined in detail. This paper also addresses: (i) research methods for relating personality coherence/incoherence to behavioral consistency/inconsistency, and (ii) situational contexts that are important to these personality dynamics. In addition, personality coherence dynamics in relation to the self and character and the impact of the C-SAP assumption that behaviors are more stable than traits/types on the relation between personality coherence and behavioral consistency are discussed.

Progressive Two-Stage Network for Low-Light Image Enhancement

At night, visual quality is reduced due to insufficient illumination so that it is difficult to conduct high-level visual tasks effectively. Existing image enhancement methods only focus on brightness improvement, however, improving image quality in low-light environments still remains a challenging task. In order to overcome the limitations of existing enhancement algorithms with insufficient enhancement, a progressive two-stage image enhancement network is proposed in this paper. The low-light image enhancement problem is innovatively divided into two stages. The first stage of the network extracts the multi-scale features of the image through an encoder and decoder structure. The second stage of the network refines the results after enhancement to further improve output brightness. Experimental results and data analysis show that our method can achieve state-of-the-art performance on synthetic and real data sets, with both subjective and objective capability superior to other approaches.

Video-game play and non-symbolic numerical comparison

Visual display was used by Stäb and Ilg to examine the abilities of video-game players and non-players to determine simple mathematical abilities.

Pupil tracks statistical regularities: behavioral and neural implications

Pupillary light reflex adjusts the amount of light reaching the retina. Recent work suggests that the brainstem pupillary light reflex pathway is controlled by the environment's internal models derived from higher-order temporal statistics. This finding has implications at the behavioral and neural levels. Pupillary changes in response to statistical regularities could be a metric constituting the precision with which the internal models are represented. These pupillary changes may aid in information processing through attentional mechanisms. One possible region that mediates descending cognitive inputs to pupil cycling is locus coeruleus. Here we propose a unified framework of locus coeruleus' role in modulating pupillary change, which successfully explains current and previous findings. The locus coeruleus could have multiple subsystems selectively (but not exclusively) driven by behavioral relevance and statistical learning to regulate pupillary change.