Morphological symmetry is a fundamental characteristic of legged animals and robots. Most existing Deep Reinforcement Learning approaches for legged locomotion neglect to exploit this inherent symmetry, often producing unnatural and suboptimal behaviors such as dominant legs or non-periodic gaits. To address this limitation, we propose a novel learning-based framework to systematically optimize symmetry by state distribution symmetrization. First, we introduce the degree of asymmetry (DoA), a quantitative metric that measures the discrepancy between original and mirrored state distributions. Second, we develop an efficient computation method for DoA using gradient ascent with a trained discriminator network. This metric is then incorporated into a reinforcement learning framework by introducing it to the reward function, explicitly encouraging symmetry during policy training. We validate our framework with extensive experiments on quadrupedal and humanoid robots in simulated and real-world environments. Results demonstrate the efficacy of our approach for improving policy symmetry and overall locomotion performance.

ABSTRACT The Density Peaks Clustering (DPC) algorithm is well‐known for its simplicity and efficiency in clustering data of arbitrary shapes. However, it faces challenges such as inconsistent local density definitions and sample assignment errors. This paper introduces the Shared Neighbors and Natural Neighbors Density Peaks Clustering (SN‐DPC) algorithm to address these issues. SN‐DPC redefines local density by incorporating weighted shared neighbors, which enhances the density contribution from distant samples and provides a better representation of the data distribution. It also establishes a new similarity measure between samples using shared and natural neighbors, which increases intra‐cluster similarity and reduces assignment errors, thereby improving clustering performance. Compared with DPC‐CE, IDPC‐FA, DPCSA, FNDPC, and traditional DPC, SN‐DPC demonstrated superior effectiveness on both synthetic and real datasets. When applied to the analysis of electricity consumption patterns, it more accurately identified load consumption patterns and usage habits.

Object detection algorithms are pivotal components of UAV imaging systems, extensively employed in complex fields. However, images captured by high-mobility UAVs often suffer from motion blur cases, which significantly impedes the performance of advanced object detection algorithms. To address these challenges, we propose an innovative object detection algorithm specifically designed for blurry images, named dual-stream restoration embedding blur-feature fusion network (DREB-Net). First, DREB-Net addresses the particularities of blurry image object detection problem by incorporating a blurry image restoration auxiliary branch (BRAB) during the training phase. Second, it fuses the extracted shallow features via multilevel attention-guided feature fusion (MAGFF) module, to extract richer features. Here, the MAGFF module comprises local attention modules and global attention modules, which assign different weights to the branches. Then, during the inference phase, the deep feature extraction of the BRAB can be removed to reduce computational complexity and improve detection speed. In loss function, a combined loss of mean squared error (MSE) and SSIM is added to the BRAB to restore blurry images. Finally, DREB-Net introduces fast Fourier transform in the early stages of feature extraction, via a learnable frequency domain amplitude modulation module (LFAMM), to adjust feature amplitude and enhance feature processing capability. Compared to the baseline, DREB-Net achieved an approximate 7% increase in both mAP50 and mAR50 across two experimental datasets. Experimental results indicate that DREB-Net can still effectively perform object detection tasks under motion blur in captured images, showcasing excellent performance and broad application prospects. Our source code will be available athttps://github.com/EEIC-Lab/DREB-Net.git.

Summary User behavior analysis is the research foundation of power load forecasting and power abnormal detection, and it is the theoretical support for smart grid planning and the construction of energy internet. Aiming at the complex characteristics of high‐dimensional, noisy, and multi‐redundant of power load data, this article used the principal component analysis (PCA) to reduce the dimensionality of power data. The density peaks clustering algorithm with shared neighbor and attractiveness (DPC‐SNA) was then used to cluster the data with reduced dimensionality to extract the user's electricity consumption characteristics. The DPC‐SNA algorithm first constructs a sample similarity measurement criterion that shares the similarity of neighbors, on which the local density is defined accordingly. The new local density can effectively distinguish the contributions of local samples and global samples. Incorporating the idea of universal gravitation, a new calculation method of sample attractiveness was defined, and the remaining samples were allocated by the attractiveness matrix. Experiment was performed using the actual load data of special transformer users in a certain area. The results show that there were five typical electricity consumption behavior characteristics in this area, namely, the evening‐peak production type, all‐day production type, multi‐peak production type, daytime production type, and night production type. The corresponding load peak time periods were also obtained.

SUMMARY Analysis of electricity consumption behaviors lays the foundation for power grid planning, demand‐side response, electricity pricing, and energy efficiency improvement. In this study, we used the principal component analysis (PCA) to reduce the dimensionality of electricity load data and used the density peak clustering algorithm based on K‐nearest neighbors and shared nearest neighbor similarity (DPC‐KS) for cluster analysis of load profiles so as to obtain the electricity consumption behaviors of customers. In DPC‐KS, the local density was defined by integrating the idea of K‐nearest neighbors to find the density peaks, which promoted the accuracy of the cluster centers found. Also, a sample similarity measure criterion of shared nearest neighbor similarity was defined, a sample similarity matrix was built, and the samples were allocated accordingly, which enables a more accurate allocation of the remaining samples. Additionally, PCA and DPC‐KS were used to conduct cluster analysis for the electricity load data of 315 dedicated substation customers in a region, and four types of electricity consumption behaviors were obtained and analyzed. The experiments validated the effectiveness of DPC‐KS, and DPC‐KS provided technical support for intelligent decision‐making in the power grid.

In recent years, deep learning methods have achieved great success for vehicle detection tasks in aerial imagery. However, most existing methods focus only on extracting latent vehicle target features, and rarely consider the scene context as vital prior knowledge. In this letter, we propose a scene context attention-based fusion network (SCAF-Net), to fuse the scene context of vehicles into an end-to-end vehicle detection network. First, we propose a novel strategy, patch cover, to keep the original target and scene context information in raw aerial images of a large scale as much as possible. Next, we use an improved YOLO-v3 network as one branch of SCAF-Net, to generate vehicle candidates on each patch. Here, a novel branch for the scene context is utilized to extract the latent scene context of vehicles on each patch without any extra annotations. Then, these two branches above are concatenated together as a fusion network, and we apply an attention-based model to further extract vehicle candidates of each local scene. Finally, all vehicle candidates of different patches, are merged by global nonmax suppress (g-NMS) to output the detection result of the whole original image. Experimental results demonstrate that our proposed method outperforms the comparison methods with both high detection accuracy and speed. Our code is released athttps://github.com/minghuicode/SCAF-Net.

The detection of objects such as vehicle, airplane and ship is a fundamental problem in optical remote-sensing(ORS) image process. Despite a great success has achieved by migrating nature image detection methods to the remote sensing field, some challenges in hardware limit environments still remain to be solved, e.g., space-borne hardware and UAV-borne hardware. We proposed a low-computational network by digging several prior knowledge in the remote sensing field. By focusing on certain ground sample distance(gsd) and single target class, the proposed method gains high performance with only less than 1% parameters and less than 1% computation used comparing with the state-of-the-art detection method. Detection result on public available vehicle dataset demonstrates the effectiveness of the proposed method. Meanwhile, the ship and airplane detection results of two private datasets are also shown. Our vehicle detection code on limited hardware is now available at https://github.com/minghuicode/scsf-detector.

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called a rotatable region-based residual network (R3-Net), to detect multioriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In R-DN, a novel rotatable position-sensitive pooling is designed to keep the position and orientation information simultaneously while downsampling the feature maps of R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image data sets, namely, DLR 3K Munich Data set and VEDAI Data set, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available athttps://youtu.be/xCYD-tYudN0.

Detecting dense buildings without elevation information is an important and challenging task in remote sensing applications. In this paper, we present a novel cascaded deep neural network architecture, incorporating multi -stage region proposal detection and Hough transform to obtain better mid-level semantic information for man-made objects. This proposed network can be trained end-to-end by multi-loss jointly. We train and test it on a large building dataset collected from Google Earth, including buildings from urban, suburban and rural areas. Experiments demonstrate great robustness and superiority of our method to various buildings over other convolutional neural network (CNN) based detection methods.

In this paper, we propose a novel Faster R-CNN based method to detect multiscale objects in very high resolution optical remote sensing images. Firstly, a pre-trained CNN is used to extract features from an input image; and then a set of object candidates are generated. To efficiently detect objects with various scales, we design a hierarchical selective filtering (HSF) layer to map features in different scales to the same scale space. The HSF layer can be applied on both region proposal and the subsequent detection network. More importantly, it can be plugged into Faster R-CNN network without modifying its architecture, meanwhile boosting the performance on detecting objects with varying scales. The proposed model can be trained in an end-to-end manner. We test our network on three datasets containing different multiscale objects, including airplanes, ships and buildings, which are collected from Google Earth images and GaoFen-2 images. Experiments demonstrate high precision and robustness of our method.

Ship detection is an important and challenging task in remote sensing applications. Most methods utilize specially designed hand-crafted features to detect ships, and they usually work well only on one scale, which lack generalization and impractical to identify ships with various scales from multiresolution images. In this paper, we propose a novel deep feature-based method to detect ships in very high-resolution optical remote sensing images. In our method, a regional proposal network is used to generate ship candidates from feature maps produced by a deep convolutional neural network. To efficiently detect ships with various scales, a hierarchical selective filtering layer is proposed to map features in different scales to the same scale space. The proposed method is an end-to-end network that can detect both inshore and offshore ships ranging from dozens of pixels to thousands. We test our network on a large ship data set which will be released in the future, consisting of Google Earth images, GaoFen-2 images, and unmanned aerial vehicle data. Experiments demonstrate high precision and robustness of our method. Further experiments on aerial images show its good generalization to unseen scenes.