A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.     

A Review on the Separation of Niobium and Tantalum by Solvent Extraction

Niobium and tantalum are found together in natural minerals. Leaching with concentrated acid solution or alkaline roasting followed by water/dilute acid leaching results in a solution containing these two metals. In this work, we reviewed the extraction and separation of Nb(V) and Ta(V) from leach liquors in the absence and presence of hydrofluoric acid by acidic, neutral extractants and amines. Several solvent extraction systems were compared in the light of the requirement of hazardous chemicals, extraction/separation efficiency, and stability/solubility of extractants. Since the cationic species of Nb(V) and Ta(V) are unstable in the aqueous solution, the application of acidic extractants for extraction and separation of these metals is limited. Amines can extract Nb(V) as well as Ta(V) irrespective of the presence of fluoride ion but the separation of these metals from the loaded amines should be carried out in stripping process, resulting in process complexity. Another disadvantage encountered for amine is related to high viscosity in extraction process. Neutral extractants are considered as promising extractants which are effective to separate Ta(V) and Nb(V) from either non-fluoride or to moderate fluoride ion concentration in terms of extraction, separation and stripping efficiency. Compared to the traditional methyl iso-butyl ketone (MIBK) based system, methyl isoamyl ketone (MIAK) is found to be a promising extractant to separate Nb(V) and Ta(V).     

Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

Distant Supervised Relation Extraction with Cost-Sensitive Loss

Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods.     

飞秒激光光斑轮廓的傅里叶描述子重建及特征提取

在利用飞秒激光对单晶硅材料进行微尺度烧蚀时,衍生的等离子体发光光斑中蕴含着大量的加工信息。如何有效提取光斑图像的几何特征信息,对研究飞秒激光烧蚀加工工艺过程具有重要意义。轮廓特征是描述激光光斑最重要的特征之一,本文着重分析了等离子体光斑轮廓特征与光斑运动规律间的关系及光斑运动情况分类方法。首先,考虑到光斑微弱的特性,对光斑图像进行增强处理。其次,采用链码方法提取光斑轮廓特征,并利用傅里叶描述子低频特性,选取少量的描述子重建光斑图像轮廓特征,减少了复杂的计算量,有效提高了光斑图像分类的速度与质量。最后,运用Hu不变矩特征证明傅里叶描述子用于提取激光光斑对轮廓特征的有效性。     

一种提高油样中低含量石油磺酸盐分析效率的方法

为了准确分析油田化学驱采出液油样中石油磺酸盐的含量,介绍了一种在油样中加入不同极性溶剂组成的萃取体系的预处理技术,将石油磺酸盐萃取到水相,利用液相色谱仪对其进行检测的方法。取适量油样,依次加入正己烷、二氯甲烷、无水乙醇和蒸馏水,振荡均匀后静置分层,取下层水相溶液进行色谱分析。色谱柱为阴离子交换柱,流动相为甲醇/水和甲醇/盐水(含0.2 mol/L乙酸和0.2 mol/L乙酸铵),梯度洗脱。实验结果表明,该预处理技术联合液相色谱法检测技术分析效率较高,加标回收率高于90%,液相色谱最小定量限10 mg/L,线性范围10～2000 mg/L。该方法可对油样中低含量的石油磺酸盐进行快速定量分析。     

Et-Ph-BCTABipy的合成及其对Pd2+的萃取性能

合成了N，N′-二乙基-N，N′-二苯基-［2，2′-联吡啶］-6，6′-二硫代酰胺(Et-Ph-BCTABipy)萃取剂，并利用¹³C NMR和¹H NMR对其进行了表征；研究了相接触时间、萃取剂浓度、水相初始酸度和Pd²⁺浓度等因素对Et-Ph-BCTABipy萃取Pd²⁺性能的影响，利用摩尔比法确定了Et-Ph-BCTABipy与Pd²⁺所形成的配合物组成；同时，在Ln(Ⅲ)与Pd²⁺共存体系中研究了Et-Ph-BCTABipy对Pd²⁺的萃取选择性。结果表明：Et-Ph-BCTABipy在HNO₃体系中对Pd²⁺具有较强的萃取性能和较高的萃取选择性；萃取过程中Et-Ph-BCTABipy与Pd²⁺以1∶2的比例结合，其萃取平衡常数K_ex=3.42×10⁶。     

图像分类中基于分类矢量量化的视觉词袋模型

特征表示是图像识别和分类的基础，视觉词袋是一种图像的特征表示方法。分析现有视觉词典构建方法的不足，提出一种新的视觉词典构建方法。首先利用梯度方差把特征矢量分为光滑类和边缘类，然后分别针对不同类别的特征矢量进行视觉词典的构建，最后根据两类视觉词典生成视觉词袋。图像分类实验表明，提出的新方法能提高分类准确率。     

STL模型特征信息的自适应分层的研究

模型的分层是3D打印前处理的一个重要环节，针对目前分层算法效率低、不能有效保留模型细微特征的问题，提出了一种新的基于STL模型特征信息的思想。首先提取模型的特征边，对特征边内实体表面采用区域增长算法进行表面分割，然后对分割后的表面判断其类型，最后对于不同的表面类型采用不同的分层求交处理算法。利用VC++6.0平台和OpenGL显示技术，依据模型表面几何特征自适应地变动层厚，减小台阶效应，加快分层效率，同样能很好地保留模型局部特征，且该算法可以根据实际模型的成型方向，实现分层方向的改变，保证成型零件的精度。     

瓦斯抽采集成旁通阀二位放水器研制

针对井下瓦斯抽采中原放水器配气阀安装不规范、双截止阀使用繁琐、工人劳动强度大等现状,研制了具有集成调控装置的放水器,能够实现一个阀门控制所有气路和水路,自主放水和负压排水,从源头上杜绝了瓦斯喷孔和水流溢出等情况,大大降低了职工的劳动强度,实现了井下抽采系统安全可靠运行。