Thin films of transparent conducting gallium-doped zinc oxide (GZO) were deposited by magnetron sputtering technique onto glass substrates. The films were characterized by various methods to understand their microstructural, optical and electrical characteristics. The effects of substrate temperature on the physical properties of the films were investigated. The results show that the GZO films are polycrystalline in nature having a hexagonal wurtzite type crystal structure with a preferred grain orientation in the (002) direction. The substrate temperature significantly affects the crystal structure and optoelectrical properties of the films. The GZO film grown at the substrate temperature of 670K has the largest crystal grain, the lowest resistivity and the highest figure of merit. Meanwhile, the optical constants, dielectric function and dissipation factor of the films were determined using the methods of Manifacier and Swanepoel. The dispersion behavior of the refractive index was studied in terms of the single-oscillator Wemple–DiDomenico (W–D) model, and the oscillator parameters of the films were achieved. Furthermore, the optical energy gaps were calculated by W–D model and Tauc's relation, respectively, and the values obtained from W–D model are in agreement with those determined from the Tauc's relation.
由于专业运动知识的匮乏、动作评估准确度低等问题,导致人们对运动训练的积极性不高,运动水平提升慢以及身体出现不同程度的损伤等,针对以上问题,提出一种基于注意力机制的轻量级采样模块的HRnet(Lightweight Sampling Attention block HRnet,LSA-HRnet)人体姿态估计模型对运动过程中的动作进行分析和评估,提出的算法采用轻量级采样块网络和融合注意力机制实现模型的轻量化以及模型性能的提升.相比原始HRnet模型及其他的优秀人体姿态估计SCnet模型、轻量级Lite-HRnet,在自制太极拳的实验结果表明提出的模型能够有效的提高预测精度和降低参数量.基于提出方法能丰富混合现实技术在运动领域的发展的技术理论,改进现存运动问题、激发练习兴趣和提升体质健康.
In order to solve the problem that existing models for predicting and assessing the risk of readmission in heart failure patients lack interpretability and cannot meet the requirements of clinical application, a WGBDT（Weighted Gradient Boosting Decision Trees）-based model for predicting the risk of readmission in heart failure patients within six months is proposed. This model consists of a risk prediction based on the WGBDT algorithm and an interpretation framework based on the SHAP（SHapley Additive exPlanation） model. On the one hand, the WGBDT risk model completes the training of the base classifier by updating the sample weights. The weighted accumulation of the base classifier that iteratively trains the residual samples updated by classification error rates of the base classifier can obtain a model with better generalization and accuracy. On the other hand, the SHAP interpretability framework uses a combination of Kernel SHAP and clinical medicine prior knowledge to interpret the WGBDT black box model and complete the interpretability of the proposed model. By using a clinical dataset with 2008 heart failure patients from a hospital in Sichuan Province as training set and test set, the results show that the conclusions obtained by the proposed model outperform those obtained by mainstream machine learning algorithms such as GBDT, XGBoost, SVM, DT, Adaboost,et al. At the same time, the interpretability of the proposed model is enhanced by using the SHAP framework and the order of the importance of factors affecting heart failure is identified according to the importance of the features. These provide a scientific reference for doctors to formulate more reasonable treatment plans.
针对现有被动孤岛检测法检测盲区大、准确率不高的问题,提出了一种小波包(Wavelet Packet Transform,WPT)、变分模态分解(Variational Mode Decomposition,VMD)和BP神经网络相结合的孤岛检测法.采集光伏电站公共耦合点(PCC)处工况数据,并利用WPT对电压波形中的特定频率成分进行滤波;采用VMD将滤波信号分解为具有不同中心频率的模态分量,并将其合成所需的电压特征向量;利用BP神经网络对工况数据进行学习分类,判断是否发出并网断路器跳闸信号.通过PSCAD/MATLAB联合仿真,验证了所提孤岛检测法的有效性,并探究了不同干扰工况下该检测法的抗干扰性能.
政策文本为社会的发展提供了权威性和原则性的方向和指导,但由于政策文本间关系错综复杂、政策实体间潜在连接关系隐性等,导致政策信息准确获取困难、理解不全面等.针对上述特点以及现有研究大多集中于单一类型政策实体及关系分析,难以全面准确地刻画政策间多类实体潜在关系的不足,提出了一种融合节点相似和属性偏好的多实体政策网络链路预测算法LP-NA.LP-NA综合考虑了节点之间的相似性、节点属性偏好对预测概率的贡献和预测偏差对链接预测的影响,采用反映局部信息的链接聚类系数和全局信息的接近中心性计算节点相似度,构建原始权重矩阵,并将政策网络的节点属性偏好和预测偏离值加入矩阵分解框架中,对网络中已知连边信息进行综合计算,提升了实体间链路预测精度.在7个真实多实体政策网络数据集上的实验结果表明:与现有算法相比,提出的LP-NA算法将AUC最大分别提升了4.76%、4.20%、2.81%、3.08%、3.25%、4.59%、3.03%.
