动名兼类和动词名用的区别

本文力求说明汉语动名兼类词和动词名用的区别，并根据双音节复合动词与数量词组(几)“个”的组合能力来划分两者的界限。     

小波分析中频带能量泄漏及对策

为了寻找信号特征提取优化的方法，排除误诊断，分析了Daubcchies小波系列的频带频率特性，指出了小波分析存在着频带能量泄漏的问题。频带能量泄漏不利于信号的特征提取和分析。通过仿真研究，得到了频带能量泄漏的基本规律，提出了频带能量增量比较法。该方法通过比较小波分析的相邻三个频带的能量增量，确定信号的特征频率分量所在的频带，进而以该频带的能量增量作为信号变化的特征。实验证明，运用该方法可以优化信号特征的提取。     

基于SIFT的车标识别算法

针对车标识别过程中匹配阈值难、识别速度慢的问题,提出一种基于尺度不变特征变换(SIFT)的特征匹配车标识别算法.利用SIFT算子对图像的视角、平移、放射、亮度、旋转等不变特性进行提取,并采用BP神经网络算法自主选取车标图像特征进行分类、匹配和识别.仿真实验结果表明,简单车标和复杂车标的识别率平均值均达90%以上,该算法识别速度较快、识别率较高,能满足实际应用的需要.     

基于稀疏转稠密机制的仿射不变特征匹配算法

提出一种基于稀疏、稠密特征转换的仿射不变特征匹配算法,其中稀疏特征包括坐标,尺度,仿射模拟参数等,稠密特征指基于图像局部区域内光学属性的局部描述符.本文算法在Affine-SIFT算法基础之上,针对在特征提取阶段仅使用稀疏特征提取的缺陷做出了改进.由于稠密信息只有在稀疏参数满一定足检测条件时才能提取到特征,导致本可以匹配到的特征(包括稀疏、稠密参数)无法提取,将通过使用稀疏特征构造新的模拟图像,通过将稀疏特征重新稠密化,并在模拟图像基础上进一步提取稀疏特征,同时可检测到原始图像中检测不到的可匹配特征,最终达到增大特征建立匹配的概率,提升正确匹配数量的目标.经实验验证,本文提出的稀密特征转换算法相比于ASIFT算法能大量增加特征匹配的数量.除针对ASIFT方法提供扩展外,该方法也可用于扩展具有充分稀疏特征参数的其它特征提取和匹配方法,并适用于目标识别、目标分类和三维重建等问题.     

辽宁新宾满族自治县乡村景观的视觉感知评价

通过实地踏勘,对民族地区乡村景观视觉感知进行研究。按照视觉特性总结出7类典型乡村景观类型。从拍摄的151张照片中选取21张各类典型景观照片,对当地及周边100名村民进行喜好的风景问卷调查。结果显示:对与自己生活相关的景观和自然风貌景观喜好较高;对有规则、管理良好的景观喜好度较高;对该地区的历史文化景观(如古树、古庙、古建筑等)的保护与开发充满期待。乡村景观的良好维护和合理规划对当地居民均有良好的积极效果。     

基于ReliefF-NOSCA-AdakNN的肌肉疲劳识别技术研究

针对竞技体育训练中的肌肉疲劳监测问题,提出了一种基于ReliefF-NOSCA-AdakNN（RNA）的表面肌电信号（sEMG）特征提取和分类算法. 该算法结合了特征和类别之间的相关性分析和启发式搜索算法,对高维特征进行了有效的筛选和分类. 将RNA算法应用于经过滤波处理的肱二头肌肌电信号数据,对不同疲劳状态进行了识别和分类. 实验结果表明,提出的RNA算法在平均分类准确率和标准差方面分别达到了83.88%和0.012 7,均显著优于传统单一算法,体现了较好的分类性能.     

Consensus-Related Performance of Triplex MASs Based on Partial Complete Graph Structure

This article mainly studies first-order coherence related to the robustness of the triplex MASs consensus models with partial complete graph structures; the performance index is studied through algebraic graph theory. The topologies of the novel triplex networks are generated by graph operations and the approach of graph spectra is applied to calculate the first-order network coherence. The coherence asymptotic behaviours of the three cases of the partial complete structures are analysed and compared. We find that under the condition that the number of nodes in partial complete substructures n tends to infinity, the coherence asymptotic behaviour of the two sorts of non-isomorphic three-layered networks will be increased by r−12(r+1), which is irrelevant to the peripheral vertices number p; when p tends to infinity, adding star copies to the original triplex topologies will reverse the original size relationship of the coherence under consideration of the triplex networks. Finally, the coherence of the three-layered networks with the same sorts of parameters, but non-isomorphic graphs, are simulated to verify the results.     

Scale Enhancement Pyramid Network for Small Object Detection from UAV Images

Object detection is challenging in large-scale images captured by unmanned aerial vehicles (UAVs), especially when detecting small objects with significant scale variation. Most solutions employ the fusion of different scale features by building multi-scale feature pyramids to ensure that the detail and semantic information are abundant. Although feature fusion benefits object detection, it still requires the long-range dependencies information necessary for small objects with significant scale variation detection. We propose a simple yet effective scale enhancement pyramid network (SEPNet) to address these problems. A SEPNet consists of a context enhancement module (CEM) and feature alignment module (FAM). Technically, the CEM combines multi-scale atrous convolution and multi-branch grouped convolution to model global relationships. Additionally, it enhances object feature representation, preventing features with lost spatial information from flowing into the feature pyramid network (FPN). The FAM adaptively learns offsets of pixels to preserve feature consistency. The FAM aims to adjust the location of sampling points in the convolutional kernel, effectively alleviating information conflict caused by the fusion of adjacent features. Results indicate that the SEPNet achieves an AP score of 18.9% on VisDrone, which is 7.1% higher than the AP score of state-of-the-art detectors RetinaNet achieves an AP score of 81.5% on PASCAL VOC.     

An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System

This paper proposes an improved human-body-segmentation algorithm with attention-based feature fusion and a refined corner-based feature-point design with sub-pixel stereo matching for the anthropometric system. In the human-body-segmentation algorithm, four CBAMs are embedded in the four middle convolution layers of the backbone network (ResNet101) of PSPNet to achieve better feature fusion in space and channels, so as to improve accuracy. The common convolution in the residual blocks of ResNet101 is substituted by group convolution to reduce model parameters and computational cost, thereby optimizing efficiency. For the stereo-matching scheme, a corner-based feature point is designed to obtain the feature-point coordinates at sub-pixel level, so that precision is refined. A regional constraint is applied according to the characteristic of the checkerboard corner points, thereby reducing complexity. Experimental results demonstrated that the anthropometric system with the proposed CBAM-based human-body-segmentation algorithm and corner-based stereo-matching scheme can significantly outperform the state-of-the-art system in accuracy. It can also meet the national standards GB/T 2664-2017, GA 258-2009 and GB/T 2665-2017; and the textile industry standards FZ/T 73029-2019, FZ/T 73017-2014, FZ/T 73059-2017 and FZ/T 73022-2019.     

未成年人网络阅读的心理特征及其影响

简要介绍了未成年人网络阅读的现状,分析和研究了未成年人网络阅读的心理特征.提出了加强未成年人网络阅读指导的有效措施.