并流共沉淀法合成Dy_(2)O_(3)-ZrO_(2)纳米粉体EI北大核心CSCD

采用并流化学共沉淀法合成了Dy_(2)O_(3)掺杂ZrO_(2)(DySZ)纳米粉体材料,系统研究稳定剂掺杂量、阳离子浓度、反应系统pH值和煅烧温度对粉体材料物相组成、晶体结构和微观形貌的影响。结果表明:不同合成工艺条件下,DySZ粉体材料均具有纳米尺度特征,球形颗粒尺寸为10~30 nm,Dy_(2)O_(3)的掺杂可以起到稳定晶型的作用;稳定剂掺杂量对DySZ粉体的物相组成具有明显影响,掺杂量为10%(质量分数)时可合成单一四方相结构的DySZ粉体;DySZ粉体材料的四方度和微观形貌对稳定剂掺杂量、阳离子浓度、反应体系pH值和煅烧温度均不敏感,但其平均晶粒尺寸随稳定剂掺杂量、阳离子浓度和反应体系pH值的升高略有降低,随煅烧温度的提高而显著增加。     

Stone–Wales Defect in Graphene

2D graphene the most investigated structures from nanocarbon family studied in the last three decades. It is projected as an excellent material useful for quantum computing, artificial intelligence, and next generation advanced technologies. Graphene exists in several forms and its extraordinary thermal, mechanical, and electronic properties, principally depend on the kind of perfection of the hexagonal atomic lattice. Defects are always considered as undesired components but certain defects in graphene could be an asset for electrochemistry and quantum electronics due to the engineered electronclouds and quantum tunnelling. The authors carefully discuss the Stone-Wales imperfections in graphene and its derivatives comprehensively. A specific emphasis is focused on the experimental and theoretical aspects of the Stone-Wales defects in graphene with respect to structure-property relationships. The corroboration of extrinsic defects like external atomic doping, functionalization, edge distortion in the graphene consisting of Stone-Wales imperfections, which are very significant in designing graphene-based electronic devices, are summarized.     

Charge storage coating based triboelectric nanogenerator and its applications in self-powered anticorrosion and antifouling

As a novel energy-harvesting device, a triboelectric nanogenerator (TENG) can harvest almost all mechanical energy and transform it into electrical energy, but its output is low. Although the micro-nano structures of triboelectrode surfaces can improve their output efficiency, they lead to high costs and are not suitable for large-scale applications. To address this problem, we developed a novel TENG coating with charge-storage properties. In this study, we modified an acrylic resin, a friction material, with nano-BaTiO₃ particles and gas phase fluorination. The charge-trapping ability of nanoparticles was used to improve the output of TENG. The short-circuit current and the output voltage of coating-based TENGs featuring charge storage and electrification reached 15 μA and 800 V, respectively, without decay for longtime working. On this basis, self-powered anticorrosion and antifouling systems are designed to reduce the open circuit potential of A3 steel by 510 mV and reduce the adhesion rate of algae on the surface of metal materials. This study presents a high-output, stable, coating-based TENG with potential in practical applications for anticorrosion and antifouling.     

基于启发式社团发现模型的创新态势研判算法

专利网络是复杂网络领域重要的组成部分,研究专利网络对理解和把握技术创新方向具有重要指导作用。利用专利文本数据构建无向加权专利网络图,并基于启发式社团发现模型提出一种创新态势研判算法。为了缓解专利标题和摘要短文本引发的文本向量稀疏问题,采用非监督的稀疏向量稠密化方法;为了解决专利网络构建过程中的相似度阈值自动化选择问题,以实验驱动的方法对比相似度阈值与专利网络常用统计指标的变化关系,最终选用平均聚类系数这一指标实现了最优相似度阈值的自动化判定。以我国数字中国及大数据领域真实发明专利数据为驱动,验证了方法的有效性并分析了数字中国及大数据领域创新态势。     

Stability analysis for the Whipple bicycle dynamics

It has been known that bicycle stability is closely linked to a pair of ordinary differential equations (ODEs). The linearization technique used to derive these ODEs, nevertheless, has yet to be thoroughly examined. For this purpose, we conduct an analysis of the dynamics of the Whipple bicycle, starting with the contact kinematics, using the Gibbs–Appell method. The effort results in a complete nonlinear model with minimal dimensions, from which equilibrium points during the bicycle’s straight and circular motions can be determined. The model can be linearized around these points via a perturbation analysis under no additional assumptions. Given the non-hyperbolic nature of the equilibria, we apply the center manifold theorem to analyze their stability, offering a rigorous derivation of the (well-know) exponential stability of the bicycle in its leaning and steering motions. Finally, a dimensionless index is defined to characterize the influence of physical parameters on the bicycle stability.

     

基于机器学习的恐怖分子预测算法

当今世界恐怖袭击事件频繁发生,通过对嫌疑人进行预测分析,有利于尽早发现新生或者隐藏的恐怖分子并对其进行针对性打击,以减少人员和财产损失。为此,使用机器学习方法,提取恐怖袭击事件的多方面特征,对一个或多个嫌疑人进行预测。采用贝叶斯优化对Bagging、决策树、随机森林和全连接神经网络4种算法进行寻优,将预处理后的数据输入优化后的算法模型对恐怖袭击事件嫌疑人进行预测,以准确率、召回率、精度和F 1值作为指标评价算法性能。实验结果表明,当预测结果仅输出一个嫌疑人时,基于树的算法预测结果普遍较好,其中Bagging算法的预测精度最高为0.911,而全连接神经网络可以得到多个嫌疑人的预测结果,其预测精度为0.8778。     

3D model retrieval via single image based on feature mapping

With the development of manufacture, more and more 3D models are generated by users and many differnet factories. 3D model retrieval has been receiving more and more attention in computer vision and the field of data analysis. In this paper, we propose a novel 3D model retrieval algorithm by cross-modal feature mapping (CMFM), which utilize one single image as query information to address 3D model retrieval problem. Specifically, in this paper, we first proposed to leverage 2D image to handle 3d model retrieval problem, which is one new problem in this field. The proposed feature learning method can benefit: 1) avoiding the interference of query image recorded by different visual sensor; 2) handling cross-modal data retrieval by simple computer vision technologies, which can guarantee the performance of retrieval and also control that the retrieval time hold a low level; 3) the low complexity of this method can guarantee that this method can be applied in many fields. Finally, we validate the retrieval method on three popular datasets. Extensive comparison experiments show the superiority of the proposed mehtod. To the best of our knowledge, it is the first method to handle 3D model retreival based on one single 2D image.     

基于粒子群算法寻最优属性关联下的零样本语义自编码器

针对零样本图像分类构建共享属性层时造成的信息缺失问题,该文提出一种嵌入属性关联性的补偿方法。通过语义自编码器构建特征到属性的映射,然后以最大后验概率估计在类高斯模型构建的基础上实现零样本图像分类。为弥补SAE对属性关系学习的不足,引入加性因子与乘性因子对属性相关性进行嵌入,并利用粒子群算法搜寻最优的因子参数,实现属性相关性信息的补偿。实验结果表明采取相同映射方法的情况下,基于属性相关性嵌入的零样本图像分类在Pubfig数据集和OSR数据集上的分类效果较之其他方法得到了显著提升。     

平衡态分子动力学 Green-Kubo 方法计算氮化硼 单层结构热导率的模型尺寸效应研究

分子动力学模拟可以直接表征体系原子的行为,因此成为研究氮化硼相关材料微观导热机理的重要工具,但目前尚没有关于氮化硼材料模型尺寸对其热传导相关性质影响规律的研究。该文采用平衡态分子动力学并结合 Green-Kubo 方法,研究了纯净氮化硼单层结构热导率、声子色散关系以及态密度随模拟尺寸的变化规律,并解释了其内部机理。实验发现,氮化硼单层材料热导率随着模拟尺寸的增大而减小,并在单层面积约 4.1 nm×4.1 nm 时收敛于(349±22)W/(m?K),此收敛值远小于平衡态分子动力学计算中石墨烯热导率的收敛尺寸(10 nm×10 nm),这说明氮化硼单层中声子之间的散射大于石墨烯。此外,不同于热导率,氮化硼单层结构的声子色散曲线、态密度几乎不受模拟尺寸的影响。该研究结果可为采用平衡态分子动力学研究氮化硼相关材料的微观导热机理提供重要参考。