基于加权模糊C均值算法改进的高光谱图像分类方案设计

为了有效改善高光谱图像数据分类的精确度,减少对大数目数据集的依赖,在原型空间特征提取方法的基础上提出一种基于加权模糊C均值算法改进型原型空间特征提取方案。该方案通过加权模糊 C 均值算法对每个特征施加不同的权重,从而保证提取后的特征含有较高的信息量。实验结果表明,与业内公认的原型空间提取算法相比 该方案在相对较小的数据集下,其性能仍具有较为理想的稳定性,且具有相对较高的分类精度,这样子就大大降低了对数据集样本数量的依赖性,同时改善了原型空间特征方法的效率。     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.     

基于图像处理的闸机口跳闸事件检测算法

文中提出了一种基于图像处理的闸机口跳闸事件检测方法。首先,利用模板匹配的方法进行预处理,得到闸门图像匹配率;其次,根据跳闸事件的连贯性特点,提取一段连续时间序列的匹配率作为分类特征;最后,采取基于最小错误率的贝叶斯分类方法对这段时间序列特征向量进行分类。实验表明,该算法可以有效的检测出闸机口的跳闸事件,具有实时性好、不需要额外的传感器、成本低的优点,具有较好的工程应用前景。     

农林院校校外实验教学基地分类考评机制及考评指标改革研究——大学化学校外实践基地实证分析*

针对大学化学校外实践教学基地建设和运行过程中的基地与高校的协同管理机制,实践教学基地软硬条件建设和资源配置及实践教学与理论学习的连贯性等问题,在OBE理论指导下,探究分类考核机制,在实证中构建分层次考评模型及考评指标,并建立适合于农林高校的化学校外实践教学基地的分类考评指标体系。     

Identification of key classification features of early cervical squamous cell carcinoma

Despite the tremendous progress in molecular analysis of pan-cancer, little is known regarding molecular classification of cervical squamous cell carcinoma. In this study, we adopted a multi-omics approach to identify potential key classification features of cervical squamous cell carcinoma. Specifically, we analyzed mRNA, and microRNA (miRNA) expression data, as well as DNA methylation and copy number variation in cervical squamous cell carcinoma cases, using datasets obtained from The Cancer Genome Atlas (TCGA). Moreover, we identified molecules in each dimension, as well as integrated and clustered filtered classification features, and used them to distinguish different subtypes. The resulting key classification features were used to establish a classification model for cervical squamous cell carcinoma. Our results revealed two cervical squamous cell carcinoma subtypes, with significant differences across clinical survival levels, as well as 8 key classification features of cervical squamous cell carcinomas. These findings are expected to provide important references for early classification of cervical squamous cell carcinoma and identification of classification markers.     

聚焦形貌恢复方法模型设计

采用基于拉普拉斯算符聚焦形貌恢复方法,提出了模拟目标深度测量的数值模型。数值模拟的核心是基于通过几何光学预测的理想图像的卷积与透镜广义孔径函数的多色点扩散函数,即用聚焦误差替代抛物线圆柱形貌或高斯函数。该模型可以使用基于聚焦形貌恢复方法的传感器真实组件参数、光源光谱、光学系统离差、相机的光谱灵敏度。提出了光学系统离差（消球差、消色差、色差）对确定目标表面形貌的精确度和可靠性的影响。结果表明,该模型可以有效提高实验效率,缩短时滞,降低成本。     

基于偏振特性对石英玻璃和绿漆涂层的反演

物质的偏振特性与其复折射率、表面粗糙度以及观测几何条件有关,为了应用偏振探测技术实现对目标的定量反演,本文首先对两种典型目标(绿漆涂层和石英玻璃)的偏振特性进行了实验测定,并对偏振度与探测天顶角的关系进行了分析。利用实验数据并基于描述目标偏振特性的PG模型首次在考虑了粗糙度的影响下,对目标的折射率、消光系数进行了定量反演,最后将反演结果与参考结果进行比较。结果表明,石英玻璃的折射率相对误差为4.944 9%,绿漆涂层的折射率与消光系数的相对误差分别为11%和21.558 9%。该方法在考虑表面粗糙度的条件下能够更精确地测定物质的复折射率,同时也为偏振技术应用于目标定量反演提供了依据。     

一种高准确率的交通监控视频车辆检测算法

针对高速视频监控的车辆识别易受几何变换影响且准确度不高的问题,提出了高效的阴影去除和车辆分类两种视频处理算法来提高车辆识别性能。利用阴影区域前景和背景的对比度参数进行有效的阴影去除;利用Hu不变矩表征车辆,从而克服了几何变换的影响。对比试验结果证明,本算法具有较高的阴影检测性能和极佳的车辆分类性能,明显优于其他同类型算法。     

Biembedding Steiner Triple Systems in Surfaces Using the Bose Construction

A uniform framework is presented for biembedding Steiner triple systems obtained from the Bose construction using a cyclic group of odd order, in both orientable and nonorientable surfaces. Within this framework, in the nonorientable case, a formula is given for the number of isomorphism classes and the particular biembedding of Ducrocq and Sterboul (preprint 18pp., 1978) is identified. In the orientable case, it is shown that the biembedding of Grannell et al. (J Combin Des 6 ( 7 ), 325–336) is, up to isomorphism, the unique biembedding of its type. Automorphism groups of the biembeddings are also given.     

Classification of Graeco‐Latin Cubes

A q‐ary code of length n, size M, and minimum distance d is called an code. An code with is said to be maximum distance separable (MDS). Here one‐error‐correcting () MDS codes are classified for small alphabets. In particular, it is shown that there are unique (5, 5³, 3)₅ and (5, 7³, 3)₇ codes and equivalence classes of (5, 8³, 3)₈ codes. The codes are equivalent to certain pairs of mutually orthogonal Latin cubes of order q, called Graeco‐Latin cubes.