提高综合滤波器鉴别率的一种有效方法

提出一种提高综合滤波器鉴别率的有效方法，其做法是：对样本集在等相关峰条件下求解综合滤波器的同时，使样本集的互补样本满足零件相关峰条件，此方法把训练模式类同所有其它模式类在映射空间的距离拉大，有效地提高了综合滤波器的鉴别率，以取自同一手指纹为样本集，求解综合鉴别函数后，建立了指纹的实时模式识别系统，实验结果表明本文提出的改进方法对提高综合滤波器的鉴别率是非常有效的。     

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.     

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.     

基于支持向量机的肤色滤波器

为了探测图像中的肤色像素,提出了一种新的方法-支持向量机（SVM：Support Vector Machine）方法.它是一种基于肤色的非特定人的面部定位方法,是非接触人机交互技术和机器视觉中的一个重要内容.实验结果表明,采用支持向量机方法较传统人工神经网络方法不仅有更高的探测准确性,而且具有更好的推广性能.由于SVM采用结构风险最小化（SRM：Structural Risk Minimization）准则,在最小化训练误差（经验风险）的同时,尽量缩小模型预测误差的上界,从而使模型有更好的泛化能力.     

二维光学神经网络自由空间互连结构设计

本文分析了以透镜阵列元件建立空间互连通道的光学神经网络中神经元数的理论极限.为了提高网络中的神经元数,我们提出了一种并行结构设计,该结构克服了小透镜成象中的离轴象散的影响,提高了系统的光能利用效率,同时降低了系统中的对位精度误差,提高了互连运算精度.     

大规模光电混合神经网络中的可编程拓扑重构

提出了一种在大规模光电混合神经网络系统实现可编程拓扑重构的有效方法，文中介绍了这种方法的原理，运用技术以及采用这种方法在已有的ＮＰ１０２４光学／数字神经网络处理器中运行单层反馈型和多层前馈型等不同网络拓扑结构的实验结果。     

用于光学神经网络硬件系统误差纠正的虚拟神经网络

提出了一种用于修正光学神经网络硬件系统误差的虚拟神经网络模型，光学实验结果表明该网络能够有效地消除硬件系统误差对实验结果的影响。     

用特征编码模板实现多模式分类识别的方法

以编码理论和神经网络优化算法为基础,提出了一种构造编码模板用于识别某一类模式集的方法,并以26个大写英文字母的识别作为实验进行了研究。     

广东省海域经济鱼类的重金属污染及其评价

样品中各元素原位、微区、高分辨率的空间分布特征具有现实而重要的研究意义. 在生物样品中,元素原位空间分布特征可以作为生理过程的指示指标,揭示代谢途径,并可反映周围环境特征对生物体的影响方式. 在地质样品中,元素原位空间分布特征可以作为矿产评估依据,也可作为研究环境地球化学变化过程的重要指标. 目前常用的几种微区元素原位分布特征测试技术包括：微区XRF技术（μXRF）、同步辐射技术（SR）、能谱电子显微镜联用技术（EM-EDS）、电子探针技术（EP）、激光剥蚀联用电感耦合等离子体质谱技术（LA-ICP-MS）、激光诱导击穿等离子发射光谱技术（LIBS）等. 根据其测试原理,这些技术在是否无损、是否定量检测、适合样品类型和大小、前处理方式复杂程度、检出限高低、分辨率大小等方面存在差异. 介绍和总结了当前主流的微区原位元素分布检测技术,分析比较了各种技术的优缺点及其适用的样品类型和应用范围,对标准样品的选择、数据校准处理及可视化成图等方案进行初步探讨,为相关的科研分析工作提供技术参考.     

Recent advances in the application of nanomaterials in enzymatic glucose sensors

Due to the critical role of glucose level in the diagnosis and treatment of diabetes, as well as the increasing number of diabetics, there is an overwhelming demand for developing glucose sensors. It is well acknowledged that these sensors, especially those based on glucose oxidase, have played an important role in blood glucose detection. Inspired by the attractive properties, nanomaterials, especially nanostructured carbon and metal/metal oxides, have been extensively explored to develop enzymatic glucose sensors with high sensitivity, fast response time, and satisfied stability. In this review, a brief history of glucose biosensors is firstly presented. Furthermore, we discuss the currently available fabrication possesses in the field of enzymatic glucose biosensors based on nanomaterials, focusing on the carbon-based, metal-based, and metal oxides-based nanocomposites. What is more, we discuss the challenges and attempt to give an outlook on the possible further developments.