Effects of Food Additives on Rehydration Properties of Frozen Dumpling Wrappers

With the vacuum freeze-drying technology, frozen dumpling wrappers were prepared, to investigate the effects of six kinds of food additives, including modified starch, compound phosphate, maltodextrin, guar gum, distilled monoglycerides and transglutaminase （TG enzyme）, on the drying rate, rehydration ratio and sense value of the frozen dumpling wrappers. The results showed that, with respective addition of 6% modified starch, O. 1% compound phosphate, 10% maltodextrin, 0.4% guar gum, 0.4% distilled monoglyceride and 0.3% transglutaminase, the drying rate, rehydration ratio and sense value of the frozen dumpling wrappers were the highest.

用TCP Wrappers加固Linux

要想提高Linux的安全，需要从不同的角度入手，比如说TCP Wrappers。

基于SVM的无刷同步发电机故障检测研究

旋转整流器是无刷同步电机中的重要部件,研究其故障检测方法对于提高发电机的整体运行水平十分必要。设计一种基于支持向量机(SVM)的无刷同步发电机旋转整流器故障检测方法,针对正常状态、整流器单个二极管开路状态以及整流器单个二极管短路状态,通过选取发电机三相端电压的多个特征频率,利用SVM进行特征分类,并借鉴Wrapper方法构造分类精度最高的频率特征子集,通过试验对所提方法进行验证。结果表明:由50 Hz(f_(2)特征)和150 Hz(f_(6)特征)构成的特征子集具有最高的分类精度,可用于旋转整流器的故障检测。

基于混合进化算法的特征选择方法研究

特征选择(Feature Selection,FS)是一种有效的数据预处理方法,它可以通过选择高维数据中一组具有高相关性和低冗余性的特征,从而解决数据冗余引起的维数灾难.目前许多计算方法已经被应用于求解FS问题,其中基于教与学优化(Teaching and Learning-based Optimization Algorithm,TLBO)的特征选择模型由于其高效的全局搜索能力受到越来越多学者的关注.然而,随着数据规模的不断扩大,这些算法所具有的模型不稳定、模型精确度低和局部搜索能力差等局限性,使算法的研究逐步陷入困境.为解决上述问题,本文提出了融合教与学优化算法与局部搜索方法(Local Search,LS)的混合进化Wrapper算法模型(Teaching and Learning-based Optimization-Local Search Algorithm,TLBOLS).首先,由于传统的教与学优化算法不能直接用于求解特征选择问题,算法在初始化阶段将实数型编码转为二进制编码,然后为保证种群的多样性,在教阶段引入最差个体重启机制,并针对进化班级过程中学习者与教学者两种身份采用不同值的TF值,提出二进制的教与学特征选择算法(Binary Teaching and Learning-based Optimization-Local Search Algorithm,BTLBOLS).随后,提出结合多操作的局部搜索方法和变邻域搜索逐渐增强扰动力度,提高整个种群的个体质量.为优化特征选择结果,BTLBOLS利用综合评价指标作为目标函数指导整体进化过程.实验选取45个高维癌症基因表达数据集进行测试并与十种特征选择算法相比,实验结果表明,相比其他算法,BTLBOLS在分类准确率和特征个数上都具有一定优势,算法分类性能有效提高.

A New Hybrid Feature Selection Sequence for Predicting Breast Cancer Survivability Using Clinical Datasets

This paper proposes a hybrid feature selection sequence comple-mented with filter and wrapper concepts to improve the accuracy of Machine Learning(ML)based supervised classifiers for classifying the survivability of breast cancer patients into classes,living and deceased using METABRIC and Surveillance,Epidemiology and End Results(SEER)datasets.The ML-based classifiers used in the analysis are:Multiple Logistic Regression,K-Nearest Neighbors,Decision Tree,Random Forest,Support Vector Machine and Multilayer Perceptron.The workflow of the proposed ML algorithm sequence comprises the following stages:data cleaning,data balancing,feature selection via a filter and wrapper sequence,cross validation-based training,testing and performance evaluation.The results obtained are compared in terms of the following classification metrics:Accuracy,Precision,F1 score,True Positive Rate,True Negative Rate,False Positive Rate,False Negative Rate,Area under the Receiver Operating Characteristics curve,Area under the Precision-Recall curve and Mathews Correlation Coefficient.The comparison shows that the proposed feature selection sequence produces better results from all supervised classifiers than all other feature selection sequences considered in the analysis.

Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.

Wrapper Based Linear Discriminant Analysis(LDA)for Intrusion Detection in IIoT

The internet has become a part of every human life.Also,various devices that are connected through the internet are increasing.Nowadays,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development.Moreover,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy utilities.It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks.Intrusion detection is defined as the detection in the network of security threats targeting privacy information and sensitive data.Intrusion Detection Systems(IDS)have taken an important role in providing security in the field of computer networks.Prevention of intrusion is completely based on the detection functions of the IDS.When an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network attacks.Many research works have been done for preventing network attacks.Every day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly defined.In this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

针对模板生成网页的一种数据自动抽取方法(英文)

当前,Web上的很多网页是动态生成的,网站根据请求从后台数据库中选取数据并嵌入到通用的模板中,例如电子商务网站的商品描述网页.研究如何从这类由模板生成的网页中检测出其背后的模板,并将嵌入的数据(例如商品名称、价格等等)自动地抽取出来.给出了模板检测问题的形式化描述,并深入分析模板产生网页的结构特征.提出了一种新颖的模板检测方法,并利用检测出的模板自动地从实例网页中抽取数据.与其他已有方法相比,该方法能够适用于"列表页面"和"详细页面"两种类型的网页.在两个第三方的测试集上进行了实验,结果表明,该方法具有很高的抽取准确率.

基于图像特征选择识别田间籽棉品级

依据中国籽棉品级文字标准,在HSI颜色空间提取了反映籽棉颜色和杂质含量的14个纹理特征以及反映棉瓣大小、结构的16个形状特征,该特征集存在维数灾难,需要进行降维。面向籽棉品级识别的特征选择问题属于非多项式(NP)难题,该文基于交叉验证、混合Filter-Wrapper和启发式搜索提出了一种求解算法。首先,以最优特征组合和浮动搜索为启发式搜索策略,基于10-折交叉验证在每一个训练集上用Filter启发式搜索最优l维特征子集(l=1,2,3,…,30),评价函数为类可分性准则;其次,在10个训练集上用Wrapper从最优l维特征子集中选择最优特征子集的容量(l=1,2,3,…,30),评价函数为Bayes分类器的误分率,10个验证集的平均误分率极小处产生最优特征子集的容量;最后,在最优特征子集容量处验证预测集的平均误分率。结果表明,所选择的10个最优特征子集在预测集上的平均识别率为88.39%,混合Filter-Wrapper和浮动搜索的特征选择算法效率高、效果好。

基于Internet的信息集成技术

综述了基于Ｉｎｔｅｒｎｅｔ上分布异构信息源的网上信息集成技术的研究进展，通过典型实例对其中信息提取与转换、数据模型和查询处理等关键技术的最新进展进行了归纳与探讨，最后对该领域今后的研究方向进行了展望．

一种基于分类算法的网页信息提取方法

在目前的Web信息提取技术中,很多都是基于HTML结构的,由于HTML结构的经常变化,使提取模板需要经常更新,而提取模板的更新需要很多领域知识。本文提出一种基于分类算法的Web信息提取方法,通过将网页文本按照其显示属性的不同进行分组,以显示属性值为基础对Web页面文本进行分类,获取所关注文本,从而完成对Web页面的信息提取。这种提取方法操作简单,易于实现,对网页结构的依赖性小。

基于统计相关性与K-means的区分基因子集选择算法

针对高维小样本癌症基因数据集的有效区分基因子集选择难题,提出基于统计相关性和K-means的新颖混合基因选择算法实现有效区分基因子集选择.算法首先采用Pearson相关系数和Wilcoxon秩和检验计算各基因与类标的相关性,根据统计相关性原则选取与类标相关性较大的若干基因构成预选择基因子集;然后,采用K-means算法将预选择基因子集中高度相关的基因聚集到同一类簇,训练SVM分类模型,计算每一个基因的权重,从每一类簇选择一个权重最大或者采用轮盘赌思想从每一类簇选择一个得票数最多的基因作为本类簇的代表基因,各类簇的代表基因构成有效区分基因子集.将该算法与采用随机策略选择各类簇代表基因的随机基因选择算法Random,Guyon的经典基因选择算法SVM-RFE、采用顺序前向搜索策略的基因选择算法SVM-SFS进行实验比较,几个经典基因数据集上的200次重复实验的平均实验结果表明:所提出的混合基因选择算法能够选择到区分性能非常好的基因子集,建立在该区分基因子集上的分类器具有非常好的分类性能.

基于Biogeography的SoC测试Wrapper扫描链设计算法

基于IP(intellectual property)核的系统级芯片的测试已成为SoC(system on chip)发展中的瓶颈,提出了一种采用BBO(biogeography based optimization)算法的Wrapper扫描链设计方法,使得Wrapper扫描链均衡化,从而达到IP核测试时间最小化的目的。本算法基于群体智能,通过实施迁徙操作和变异操作,实现Wrapper扫描链均衡化设计。本文以ITC'02 Test bench-marks中的典型IP核为实验对象,实验结果表明本算法相比BFD(best fit decrease)等算法,能够进一步缩短Wrapper扫描链,从而缩短IP核测试时间。

基于平均值余量的Wrapper扫描链平衡算法

测试问题已成为SoC发展过程中的瓶颈,提出一种新的Wrapper扫描链平衡算法以期缩短IP核测试时间。算法首先计算Wrapper扫描链长度平均值,再结合特定的余量值,计算得到一个取值区间,记该区间为平均值余量;然后将IP核的内部扫描链按其长度降序排列,每次均将最长的内部扫描链添加到某条Wrapper扫描链上,直到该Wrapper扫描链长度在平均值余量所指定的区间内为止。以ITC'02 SoC Test Benchmarks内的所有测试集为对象完成的实验证明本算法能极其有效的通过扫描链平衡设计缩短IP核测试时间。

基于XML的分布式异构地理数据集成与共享

地理信息系统的迅速发展和广泛应用积累了大量的地理数据资源 ,它们分别以不同的存储格式存放在不同地理位置的不同系统中 ,给数据综合利用和数据共享带来了很大的不便 ,特别是给数据在网络环境下的集成和共享构成了障碍。本文针对目前 GIS数据集成和共享的现状 ,提出了一个以 XML

基于特征选择和RRVPMCD的滚动轴承故障诊断方法

针对滚动轴承故障诊断时所提取的特征值中可能含有较小相关性和冗余性特征,采用基于Wrapper模式的距离评价技术(distance evaluation technique,简称DET)进行特征选择。在分类器的设计中,提出了基于稳健回归的多变量预测模型(Robust regression-Variable predictive model based class discriminate,简称RRVPMCD)分类方法,以减小"异常值"对参数估计的影响,从而有望建立更加准确的预测模型。即根据Wrapper模式的特点,首先通过DET方法计算出各特征值对类的敏感度,并结合RRVPMCD分类器,选择敏感度最大的若干特征值组成特征向量矩阵;然后用RRVPMCD方法进行训练,建立预测模型;最后用所建立的预测模型进行模式识别。实验分析结果表明,基于Wrapper模式的特征选择方法和RRVPMCD分类方法相结合可以有效地对滚动轴承的工作状态和故障类型进行识别。

基于XML/CORBA的XML Wrapper系统研究

XML技术是基于Web的电子商务应用研究的热点，CORBA技术能解决跨平台等兼容问题，实现分布式软件集成。文章探讨了XML应用于Web所面临的问题及基于CORBA 的解决方案，构建了一个基于XML/ CORBA新型Web框架的应用系统——XML Wrapper系统。论述了系统设计目标、系统结构、关键技术及实现，最后给出了XML Wrapper系统的特色。

Web信息的自主抽取方法

提出了基于表格结构及列表结构的W eb页面信息自主抽取的方法。可根据用户对信息的需求自主地从相关页面中抽取信息并将抽取信息按关系模型进行重组存放在数据库中,对表格结构信息源仅需标注一页网页,即可获取抽取知识,通过自学习能够较好地适应网页信息的动态变化,实现信息的自动抽取。对列表结构信息源信息,通过对DOM树结构的分析,动态获得信息块在DOM层次结构中的路径,根据信息对象基本的抽取知识,获得信息对象值。采用自学习的方法以适应网页信息的动态变化。