Vertex-Edge Degree Based Indices of Honey Comb Derived Network

Chemical graph theory is a branch of mathematics which combines graph theory and chemistry. Chemical reaction network theory is a territory of applied mathematics that endeavors to display the conduct of genuine compound frameworks. It pulled the research community due to its applications in theoretical and organic chemistry since 1960. Additionally, it also increases the interest the mathematicians due to the interesting mathematical structures and problems are involved. The structure of an interconnection network can be represented by a graph. In the network, vertices represent the processor nodes and edges represent the links between the processor nodes. Graph invariants play a vital feature in graph theory and distinguish the structural properties of graphs and networks. In this paper, we determined the newly introduced topological indices namely, first -degree Zagreb index, first -degree Zagreb index, second -degree Zagreb index, -degree Randic index, -degree atom-bond connectivity index, -degree geometric-arithmetic index, -degree harmonic index and -degree sum-connectivity index for honey comb derived network. In the analysis of the quantitative structure property relationships (QSPRs) and the quantitative structureactivity relationships (QSARs), graph invariants are important tools to approximate and predicate the properties of the biological and chemical compounds. Also, we give the numerical and graphical representation of our outcomes.     

基于音频特征的工程车辆工况识别研究

高效率地使用工程车辆是工程项目管理中节约成本的有效方法，无人监管环境下工程车辆的工况识别，是实现工程车辆高效率使用的有效手段。目前以GPS等技术为核心的车辆智能管理系统未对工程车辆进行工况识别，提出一种基于GRU循环神经网络的工程车辆工况识别方法，通过对工程车辆在不同工况下产生的音频信号进行分析，从中提取Mel倒谱系数作为主要特征，构建GRU循环神经网络模型进行训练和识别。实验结果表明，该方法可以实现对工程车辆工况的有效识别。     

Neural Emotion Detection via Personal Attributes

There has been a recent line of work to automatically detect the emotions of posts in social media. In literature, studies treat posts independently and detect their emotions separately. Different from previous studies, we explore the dependence among relevant posts via authors' backgrounds, since the authors with similar backgrounds, e.g., "gender", "location", tend to express similar emotions. However, personal attributes are not easy to obtain in most social media websites. Accordingly, we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection: the Joint Model with Personal Attention Mechanism (JPA) model is used to detect emotion and personal attributes jointly, and capture the attributes-aware words to connect similar people; the Neural Personal Discrimination (NPD) model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection. Experimental results show the usefulness of personal attributes in emotion detection, and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.     

Electrochemical performance of in situ LiFePO4 modified by N-doped graphene for Li-ion batteries

LiFePO₄ modified by N-doped graphene (NG) with a three-dimensional conductive network structure was synthesized via a one-step in situ hydrothermal method. The effects of N amount of NG on the phase structure, morphology, and electrochemical properties of LiFePO₄ are investigated in this study. X-ray diffraction (XRD) results show that doping suitable N amounts in NG do not alter the crystal structure of LiFePO_4, and scanning electron microscopy (SEM) images show that NG can slightly reduce the particle size of LiFePO₄. The high-resolution transmission electron microscopy (HRTEM) results show that the LiFePO₄ particles are well covered and connected by NG. The electrochemical performance confirms that LiFePO₄ modified by 20% N-doped graphene (named LFP/NG-4) displays a perfect specific capacity of 166.6 mAh·g^?1 at a rate of 0.2C and can reach 125 mAh·g^?1 at a rate of 5 C. Electrochemical impedance spectroscopy (EIS) results illustrate that the charge transfer resistance value of the LFP/NG-4 composite is only 58.6 Ω, which is very low compared with LiFePO₄. Cyclic voltammetry (CV) tests indicate that the addition of 20% N-doped graphene can effectively reduce electrode polarization and improve reversibility. The LFP/NG-4 composite with a three-dimensional conductive network structure can be regarded as a promising cathode material for Li-ion batteries.     

基于GIS单线图的配网停电单模拟操作应用研究

配电网停电会造成电力系统供配电可靠性以及服务质量下降,研究基于地理信息系统(GIS)单线图的配网停电单模拟操作应用。利用网格长度作为基本单位建立坐标系,以选取起始点与终止点为基础,通过四参数法将GIS坐标映射至图纸网格内,实现配网内设备初步布局,将杆塔、站房和整体均匀分布作为优化目标,设置多目标优化目标函数实现GIS单线图最终优化。选取某电力公司配网作为单模拟操作应用对象,模拟结果表明,单模拟操作配网停电后,该配网各负荷点年故障率、次平均停电时间以及年停电时间均有所减少,可有效提升配网的供配电可靠性。     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

Quality assessment for color correction-based stitched images via bi-directional matching

With the deepening of social information, the panoramic image has drawn a significant interest of viewers and researchers as it can provide a very wide field of view (FoV). Since panoramic images are usually obtained by capturing images with the overlapping regions and then stitching them together, image stitching plays an important role in generating panoramic images. In order to effectively evaluate the quality of stitched images, a novel quality assessment method based on bi-directional matching is proposed for stitched images. Specifically, dense correspondences between the testing and benchmark stitched images are first established by bi-directional SIFT-flow matching. Then, color-aware, geometric-aware and structure-aware features are respectively extracted and fused via support vector regression (SVR) to obtain the final quality score. Experiments on our newly constructed database and ISIQA database demonstrate that the proposed method can achieve comparable performance compared with the conventional blind quality metrics and the quality metrics specially designed for stitched images.     

认知双向中继网络在κ-μ衰落信道下的中断性能分析

针对认知双向中继网络在进行数据传输时面临的复杂无线信道场景问题,采用广义κ-μ分布构建认知双向中继网络中的视距和非视距无线传输信道,推导次网络在κ-μ衰落信道下的统一中断概率,并分析次网络在多种单一和混合衰落信道情况下的中断性能。仿真结果表明,无线信道的衰落程度对次网络的中断概率影响显著,依据衰落信道类型合理设置网络参数将有助于提升次网络中断性能。     

Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.     

2种侧围加强内框总成检具结构对比分析

概述了侧围加强内框总成与周边零件的匹配关系,从检具操作的人机工程出发,描述了侧围加强内框总成检具的2种定位方式和结构设计,对比了2种定位方式对于检测结果的影响,总结了其优点和弊端,结果表明侧围加强内框总成检具采用车内方向朝下的定位方式效果更好。