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At present, the specific capacity and cyclic stability of single cobalt manganese oxide are still insufficient to meet the application requirements. In this work, (Co,Mn)(Co,Mn) 2 O 4 /g-C 3 N 4 electrode materials with different mass ratios were synthesized by adjusting the composite ratio of g-C 3 N 4 and (Co,Mn)(Co,Mn) 2 O 4. The results of the three-electrode test system show that the electrochemical performance of the composite sample is improved compared with that of pure (Co,Mn)(Co,Mn) 2 O 4. When the mass ratio of g-C 3 N 4 to (Co,Mn)(Co,Mn) 2 O 4 is 1:9, the electrochemical performance is the best. This is because layered g-C 3 N 4 can provide more active sites for electrochemical reactions and increase the wettability of the material, thus improving the electrochemical reaction process. The power density of the assembled asymmetric supercapacitor CMO/GCN-2//AC can reach 16800 ​W ​kg−1 at an energy density of 14 ​Wh kg−1. Schematic representation of the synthesized (Co,Mn)(Co,Mn) 2 O 4 /g-C 3 N 4 structure. The granular form represents (Co,Mn)(Co,Mn) 2 O 4 , and the coloured layered structure is g-C 3 N 4. This structure improves the electrochemical reaction kinetics of the material. [Display omitted] • The specific capacity and cyclic stability of single cobalt-manganese oxides are relatively poor. The presence of lone pair electrons in g-C 3 N 4 nitrogen atoms induces greater polarity in the molecule, enhancing the wettability and carrier mobility of the material. In this paper, (Co,Mn)(Co,Mn) 2 O 4 /g-C 3 N 4 electrode material with different composite ratio was prepared. • The results of the three-electrode test system show that the electrochemical performance of the composite sample is improved compared with that of pure (Co,Mn)(Co,Mn) 2 O 4. When the mass ratio of g-C 3 N 4 to (Co,Mn)(Co,Mn) 2 O 4 is 1:9, the composite sample has the best electrochemical performance. • The power density of the assembled asymmetric supercapacitor CMO/GCN-2//AC can reach 16800 ​W ​kg−1 at energy density of 14 ​Wh kg−1. [ABSTRACT FROM AUTHOR]

In this paper, we establish a regularity criterion for the Navier–Stokes system with axisymmetric initial data. It is proved that if the local axisymmetric smooth solution u satisfies ‖ u θ 1 r ≤ ς ‖ L α ( ( 0 , T ⁎ ) ; L β ) < ∞ for any given ς > 0 , with 2 α + 3 β = 1 , 4 ≤ β ≤ 6 , then the strong solution keeps smoothness up to time T ⁎ . [ABSTRACT FROM AUTHOR]

Abstract: Moving object detection is one of the key technologies for intelligent video monitoring systems. For real-time detection of moving object in the surveillance scene, the general and simple method is based on background image difference. However, it requires the accurate current background image and the approach for automatic background updating along with the illumination variance is difficult to design and implement. This limits its applications. To solve the above problem, a new self-adaptive background approximating and updating algorithm based on optical flow theory is presented for the traffic surveillance scene in this paper. To detect the moving regions of interest in the scene, the difference image between the current frame and the updating background is first obtained by using a color image difference model, and then a self-adaptive thresholding segmentation method for moving object detection based on the Gaussian model is developed and implemented. Moreover, an effective shadow-eliminating algorithm based on contour information and color features is developed. Experimental results demonstrate that the proposed background updating method can update the background exactly and rapidly along with the variance of illumination, the self-adaptive thresholding segmentation method based on the Gaussian model can extract the moving object regions accurately and completely, and the shadow can be eliminated accurately. This is the foundation for further objects recognition and understanding. [Copyright &y& Elsevier]

Human skin detection in images is desirable in many practical applications, e.g., human–computer interaction and adult-content filtering. However, existing methods are mainly suffer from confusing backgrounds in real-world images. In this paper, we try to address this issue by exploring and combining several human skin properties, i.e. color property, texture property and region property. First, images are divided into superpixels, and robust skin seeds and background seeds are acquired through color property and texture property of skin. Then we combining color, region and texture properties of skin by proposing a novel skin color and texture based graph cuts (SCTGC) to acquire the final skin detection results. Comprehensive and comparative experiments show that the proposed method achieves promising performance and outperforms many state-of-the-art methods over publicly available challenging datasets with a great part of hard images. [ABSTRACT FROM AUTHOR]

Clothing style analysis is a critical step for understanding images of people. To automatically identify the style of clothing that people wear is a challenging task due to various poses of person and large variations for even the same clothing category. Suit as one of the clothing style is a key element in many important activities. In this paper, we propose a novel suits detection method for images of people in unconstrained environments. In order to cope with various human poses, human pose estimation is incorporated. By analyzing the style of clothing, we propose the color features, shape features and statistical features for suits detection. Experiments with four popular classifiers have been conducted to demonstrate that the proposed features are effective and robust. Comparative experiments with Bag of Words (BoW) method demonstrate that the proposed features are superior to BoW which is a popular method for object detection. The proposed method has achieved promising performance over our dataset, which is a challenging web image set with various human poses and diverse styles of clothing. [ABSTRACT FROM AUTHOR]

Multi-task learning aims to tackle various tasks with branched feature sharing architectures. Considering its diversity and complexity, discriminative feature representations need to be extracted for each individual task. Fixed geometric structures as a limitation of convolutional neural networks (CNNs) in building models, is also exists and poses a severe challenge in multi-task learning since the geometric variations will augment when we deal with multiple tasks. In this paper, we go beyond these limitations and propose a novel multi-task network by introducing the deformable convolution. Our design, the Deformable Multi-Task Network (DMTN), starts with a single shared network for constructing a shared feature pool. Then, we present task-specific deformable modules to extract discriminative features to be tailored for each task from the shared feature pool. The task-specific deformable modules utilize two new parts, deformable part and alignment part, to extract more discriminative task-specific features while greatly enhancing the transformation modeling capability. Experiments conducted on various multi-task learning types demonstrate the effectiveness of the proposed method. On multiple classification tasks, semantic segmentation and depth estimation tasks, our DMTN exceeds state-of-the-art approaches against strong baselines. [ABSTRACT FROM AUTHOR]

When introducing physics-constrained deep learning solutions to the volumetric super-resolution of scientific data, the training is challenging to converge and always time-consuming. We propose a new hierarchical sampling method based on octree to solve these difficulties. In our approach, scientific data is preprocessed before training, and a hierarchical octree-based data structure is built to guide sampling on the latent context grid. Each leaf node in the octree corresponds to an indivisible subblock of the volumetric data. The dimensions of the subblocks are different, making the number of sample points in each randomly cropped training data block to be adaptive. We reconstruct the octree at intervals according to loss distribution to perform the multi-stage training. With the Rayleigh-Bénard convection problem, we deploy our method to state-of-the-art models. We constructed adequate experiments to evaluate the training performance and model accuracy of our method. Experiments indicate that our sampling optimization improves the convergence performance of physics-constrained deep learning super-resolution solutions. Furthermore, the sample points and training time are significantly reduced with no drop in model accuracy. We also test our method in training tasks of other deep neural networks, and the results show our sampling optimization has extensive effectiveness and applicability. The code is publicly available at https://github.com/xinjiewang/octree-based_sampling. • We propose a novel octree-based approach for modeling volumetric scientific data by leveraging its importance distribution. • We present a hierarchical sampling method to optimize the distribution of sample points in each training data block. • We construct a multi-stage training strategy to avoid failure of the proposed sampling optimization. [ABSTRACT FROM AUTHOR]