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Convolutional neural networks (CNNs) and transformers have boosted the rapid growth of object detection in synthetic aperture radar (SAR) images. However, it is still a challenging task because SAR images usually have the characteristics of unclear contour, sidelobe interference, speckle noise, multiple scales, complex inshore background, etc. More effective feature extraction by the backbone and augmentation in the neck will bring a promising performance increment. In response, we make full use of the advantage of CNNs in extracting local features and the advantage of transformers in capturing long-range dependencies to propose a Swin Transformer-based detector for arbitrary-oriented SAR ship detection. Firstly, we incorporate a convolution-based local perception unit (CLPU) into the transformer structure to establish a powerful backbone. The local-perception-enhanced Swin Transformer (LP-Swin) backbone combines the local information perception ability of CNNs and the global feature extraction ability of transformers to enhance representation learning, which can extract object features more effectively and boost the detection performance. Then, we devise a cross-scale bidirectional feature pyramid network (CS-BiFPN) by strengthening the propagation and integration of both location and semantic information. It allows for more effective utilization of the feature extracted by the backbone and mitigates the problem of multi-scale ships. Moreover, we design a one-stage framework integrated with LP-Swin, CS-BiFPN, and the detection head of R3Det for arbitrary-oriented object detection, which can provide more precise locations for inclined objects and introduce less background information. On the SAR Ship Detection Dataset (SSDD), ablation studies are implemented to verify the effectiveness of each component, and competing experiments illustrate that our detector attains 93.31% in mean average precision (mAP), which is a comparable detection performance with other advanced detectors. [ABSTRACT FROM AUTHOR]

High-resolution remote-sensing images cover more feature information, including texture, structure, shape, and other geometric details, while the relationships among target features are more complex. These factors make it more complicated for classical convolutional neural networks to obtain ideal results when performing a feature classification on remote-sensing images. To address this issue, we proposed an attention-based multiscale max-pooling dense network (DMAU-Net), which is based on U-Net for ground object classification. The network is designed with an integrated max-pooling module that incorporates dense connections in the encoder part to enhance the quality of the feature map, and thus improve the feature-extraction capability of the network. Equally, in the decoding, we introduce the Efficient Channel Attention (ECA) module, which can strengthen the effective features and suppress the irrelevant information. To validate the ground object classification performance of the multi-pooling integration network proposed in this paper, we conducted experiments on the Vaihingen and Potsdam datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). We compared DMAU-Net with other mainstream semantic segmentation models. The experimental results show that the DMAU-Net proposed in this paper effectively improves the accuracy of the feature classification of high-resolution remote-sensing images. The feature boundaries obtained by DMAU-Net are clear and regionally complete, enhancing the ability to optimize the edges of features. [ABSTRACT FROM AUTHOR]

We propose a novel reduced-order model and examine its applicability to the complex three-dimensional turbulent wake of a generic square-backed bluff body called the Ahmed body at the Reynolds number ReH = U∞H/ν = 9.2 × 104 (where U∞ is free-stream velocity, H the height of the body, and ν viscosity). Training datasets are obtained by large eddy simulation. The model reduction method consists of two components—a Visual Geometry Group (VGG)-based hierarchical autoencoder (H-VGG-AE) and a temporal convolutional neural network (TCN). The first step is to map the high-dimensional flow attributes into low-dimensional features, namely latent modes, which are employed as the input for the second step. The TCN is then trained to predict the low-dimensional features in a time series. We compare this method with a TCN based on proper orthogonal decomposition (POD), which utilizes time coefficients as the input in the second part. It turns out that the H-VGG-AE has a lower reconstruction error than POD when the number of latent modes is relatively small in the first part. As the number of latent modes increases, POD exceeds in the performance of model reduction. However, the H-VGG-AE-based TCN is still more effective in terms of spatiotemporal predictions because it has a lower prediction error and costs much less time. [ABSTRACT FROM AUTHOR]

The existence of aberrations has always been an important limiting factor in the imaging field. Especially in optical microscopy imaging, the accumulated aberration of the optical system and the biological samples distorts the wavefront on the focal plane, thereby reducing the imaging resolution. Here, we propose an adaptive optical aberration correction method based on convolutional neural network. By establishing the relationship between the Zernike polynomial and the distorted wavefront, with the help of the fast calculation advantage of an artificial intelligence neural network, the distorted wavefront information can be output in a short time for the reconstruction of the wavefront to achieve the purpose of improving imaging resolution. Experimental results show that this method can effectively compensate the aberrations introduced by the system, agarose and HeLa cells. After correcting, the point spread function restored the doughnut-shape, and the resolution of the HeLa cell image increased about 20%. [ABSTRACT FROM AUTHOR]

Buildings contribute significantly to global energy consumption. Optimizing internal building space layout is an essential approach for reducing energy consumption. However, proactively improving energy efficiency by building space design is still challenging requiring comprehensive consideration of complex interactions between indoor environment and occupant behavior, which is less studied previously. Considering behavior-environment integration, this study proposes a data-driven approach based on multi-sensor fusion for energy-efficiency oriented occupancy space optimization in buildings. Firstly, time series data including indoor environment and occupant behavior were collected based on multi-sensor fusion. Then, a data-integrated Convolutional Neural Network (CNN) model was developed for occupancy state classification. Based on obtained occupant schedules, space occupancy patterns of users were extracted using hierarchical clustering, and space optimization was further conducted for energy efficiency improvement. Finally, energy consumption was predicted with random forest regression after space optimization, and the impact of occupancy space optimization on energy efficiency can be evaluated. The proposed method was successfully applied in an academic office building on a campus in Wuhan, China, which helped achieve energy consumption reduction by 23.5 %. This study presents a promising path towards sustainable energy goals in building design, which serves as advanced guidance in the management of building energy performance. • Capture environment and behavior information based on multi-sensor fusion. • Develop a data-integrated CNN model for occupancy state classification. • Optimize occupant distribution in building space for energy efficiency based on occupancy patterns. • Occupancy space optimization can help achieve 23.5 % reduction in building energy consumption. • Spatially and temporally granular occupancy states and space utilization were investigated. [ABSTRACT FROM AUTHOR]