Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics

In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.

Selective sampling with Gromov–Hausdorff metric:Efficient dense-shape correspondence via Confidence-based sample consensus

Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.

Anti-symmetric sampled grating quantum cascade laser for mode selection

For mode selection in a quantum cascade laser(QCL),we demonstrate an anti-symmetric sampled grating(ASG).The wavelength of the-1-th mode of this laser has been blue-shifted more than 75 nm(~10 cm^(-1))compared with that of an ordinary sampled grating laser with an emission wavelength of approximately 8.6μm,when the periodicities within both the base grating and the sample grating are kept constant.Under this condition,an improvement in the continuous tuning capability of the QCL array is ensured.The ASG structure is fabricated in holographic exposure and optical photolithography,thereby enhancing its flexibility,repeatability,and cost-effectiveness.The wavelength modulation capability of the two channels of the grating is insensitive to the variations in channel size,assuming that the overall waveguide width remains constant.The output wavelength can be tailored freely within a certain range by adjusting the width of the ridge and the material of the cladding layer.

A New Sample-Selection and Modeling Method Based on Near-Infrared Spectroscopy and Its Industrial Application

Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.

Estimation of Multivariate Sample Selection Models via a Parameter-Expanded Monte Carlo EM Algorithm

This paper develops a parameter-expanded Monte Carlo EM (PX-MCEM) algorithm to perform maximum likelihood estimation in a multivariate sample selection model. In contrast to the current methods of estimation, the proposed algorithm does not directly depend on the observed-data likelihood, the evaluation of which requires intractable multivariate integrations over normal densities. Moreover, the algorithm is simple to implement and involves only quantities that are easy to simulate or have closed form expressions.

A Failure Sample Selection Method Based on Failure Pervasion Intensity

The complex and uncertain relationship among failures was always ignored in failure sample selection based on traditional testability demonstration experimental method. A failure pervasion model is founded based on fuzzy probability Petri net (FPPN) which can depict the propagation and pervasion relation among failures,then failure pervasion intensity is defined,the process of failure pervasion was depicted based on k-step fault pervasion algorithm and the pervasion intensity was expressed by a value. The method of sample selection based on failure pervasion intensity and failure rate is introduced into the process of sample selection. The practical application shows that the sample set selected based on failure pervasion intensity and failure rate can represent the failure set adequately.

Feature Selection for Multi-label Classification Using Neighborhood Preservation

Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.

Selection of Spectral Data for Classification of Steels Using Laser-Induced Breakdown Spectroscopy

Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.

Do cooperatives participation and technology adoption improve farmers’welfare in China?A joint analysis accounting for selection bias

This study examines the impact of farmers’cooperatives participation and technology adoption on their economic welfare in China.A double selectivity model(DSM)is applied to correct for sample selection bias stemming from both observed and unobserved factors,and a propensity score matching(PSM)method is applied to calculate the agricultural income difference with counter factual analysis using survey data from 396 farmers in 15 provinces in China.The findings indicate that farmers who join farmer cooperatives and adopt agricultural technology can increase agricultural income by 2.77 and 2.35%,respectively,compared with those non-participants and non-adopters.Interestingly,the effect on agricultural income is found to be more significant for the low-income farmers than the high-income ones,with income increasing 5.45 and 4.51%when participating in farmer cooperatives and adopting agricultural technology,respectively.Our findings highlight the positive role of farmer cooperatives and agricultural technology in promoting farmers’economic welfare.Based on the findings,government policy implications are also discussed.

Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved interval partial least squares

As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring system.In order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs spectra.This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling.The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum.Different wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory.When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum wavelengths.While the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and 0.9999.The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.

Application of fuzzy optimal selection of similar slopes to the evaluation of slope stability

The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimization theory is thus introduced to the evaluation of slope stability by this paper and a method of fuzzy optimal selection of similar slopes is put forward to analyze slope stability.By comparing the relative membership degrees that the evaluated object sample of slope is similar to the source samples of which the stabilities are detected clearly,the source sample with the maximal relative membership degree will be chosen as the best similar one to the object sample,and the stability of the object sample can be evaluated by that of the best similar source sample.In the process many uncertain influential factors are considered and characteristics and knowledge of the source samples are obtained.The practical calculation indicates that it can achieve good results to evaluate slope stability by using this method.

All-Solid-State Screen-Printed Sensors for Potentiometric Calcium(II) Determinations in Environmental Samples

This paper describes preparation, characterization and electrochemical performance of novel planar miniaturized all-solid-state (ASS) screen-printed potentiometric sensors for the detection of Ca2+ ions in environmental samples. Screen-printed graphite-based ion-selective electrodes (ISEs) and screen-printed reference electrodes based on silver-containing pastes have been applied in a space saving manner on common ceramic substrates with small dimensions. Applications to environmental samples are shown by direct potentiometry and potentiometric titrations in real water samples. Conducting polymers (CPs) have been used as solid-contact materials and as intermediate layer between the polyvinyl chloride (PVC)-containing ion-selective membrane and the graphite-containing substrate. Different diamides have been incorporated into the PVC membrane. In the range from 10-4 mol/L to 10-1 mol/L, the ISEs show linear slopes of 27 mV/decade, which is close to the Nernstian response. Moreover, the ISEs have response times of 6 months. The novel potentiometric ASS sensors enable simple and exact Ca2+ determinations in real samples.

基于不同数据源的毕业要求达成情况评价实证研究

毕业要求的达成情况评价是“学生为中心、面向产出、持续改进”工程教育基本理念实施的关键环节,其通过跟踪学生的学习轨迹对毕业要求进行达成情况评价,检验认证期内的某一届获学士学位毕业生能力是否达成,对于评价学生培养质量及持续改进教学薄弱环节具有重要参考依据。不同的评价方法和评价数据源会直接影响评价结果的有效性和持续改进的合理性。该文在分析毕业要求达成情况评价常用方法的基础上,讨论课程定量评价不同样本的选取方式对毕业要求达成结果的影响,最后以毕业要求达成的直接评价和间接评价结果探讨可行的持续改进措施,以提升专业的产出质量。

经济林种植户有机肥施用行为及影响因素分析——基于陕西省589户种植户调查数据

在积极倡导环境保护的时代背景下,探讨经济林种植户有机肥施用行为的影响因素,对林业绿色可持续发展具有重要影响。以陕西省589户经济林种植户的调查数据为基础,运用Heckman样本选择模型,着重分析电商参与和社会网络对经济林种植户有机肥施用行为的影响。结果表明,74%的种植户选择施用有机肥,但有机肥投入资金占肥料总投入较低。种植户电商参与、社会网络正向影响其有机肥施用决策和施用程度,并且社会网络在电商参与影响有机肥施用程度的过程中起正向调节作用。此外,家中农用机械数量、经济林种植面积以及是否被评选为科技示范户种植户也会对种植户有机肥施用行为产生影响。

融合选择性稀疏采样的细粒度图像分类

常用的细粒度分类方法通过提取局部信息学习细粒度特征,容易忽视周围环境因素影响问题,造成分类精度下降.针对这一问题提出了一个简单有效的框架,称为选择性稀疏采样.通过类峰值响应产生稀疏注意定位有信息的对象部分,根据图像内容选择动态数量的稀疏注意,生成判别性和补充性两个分支进行视觉表示,使得特征部分和全局信息相辅相成.对于容易产生混淆的部分,引入了一个“梯度增强”损失,只关注每个样本的混淆类,为补充性分支提供更多的细节特征.通过实验结果表明,该方法在常用数据集的基准测试中分别达到了88.6%,92.8%和94.8%的精确度,验证了该方法的有效性.

不均衡小样本下多特征优化选择的生命体触电故障识别方法

针对现有的剩余电流保护装置无法有效识别触电事故的问题,该文提出了一种不均衡小样本下多特征优化选择的生命体触电故障识别方法。首先通过变分自编码器(VAE)对实验收集到的生命体触电小样本数据进行增殖以实现正负样本均衡;然后在时域上提取能够反映波形动态变化特性的23个特征量,并利用高斯核Fisher判别分析(GKFDA)与最大信息系数(MIC)法从中选择最优表达特征组;最后,提出基于遗忘因子的在线顺序极限学习机(FOS-ELM)算法实现生命体触电行为的鉴别。实验结果表明,所提方法利用不均衡小样本触电数据集就可以训练出一个优秀的分类模型,诊断准确率可达98.75%,诊断时间仅为1.33 ms。其优良的性能结合在线增量式学习分类器设计,使得模型具备新知识学习能力,具有极好的工程应用前景。

纤维肌痛综合征生物标记物的筛选及免疫细胞浸润分析

背景:纤维肌痛综合征作为常见风湿病,其发病与中枢敏化及免疫异常有关,但具体过程尚未阐明,缺乏特异性诊断标志物,不断探索该病的发病机制具有重要的临床意义。目的:基于加权基因共表达网络分析(WGCNA)等生物信息学方法和机器学习算法筛选纤维肌痛综合征潜在的诊断相关标志基因,并分析其免疫细胞浸润特征。方法:对来自基因表达综合数据库(GEO)的纤维肌痛综合征数据集转录谱进行差异分析和WGCNA分析,整合筛选出差异共表达基因,进一步采用机器学习套索回归(LASSO)算法、支持向量机递归特征消除(SVM-RFE)机器学习算法来识别核心生物标志物,并绘制受试者工作特征(ROC)曲线以评估诊断价值。最后,采用单样本基因集富集分析(ssGSEA)和基因集富集分析(GSEA)评估纤维肌痛综合征的免疫细胞浸润情况及通路富集。结果与结论:①对GSE67311数据集按照log2|(FC)|>0,P<0.05的条件进行差异分析后获得8个下调的差异表达基因;进行WGCNA分析后获得正相关性最高(r=0.22,P=0.04)的模块(MEdarkviolet)内含基因497个,负相关性最高(r=-0.41,P=6×10-5)的模块(MEsalmon2)内含基因19个;将差异表达基因与WGCNA的2个高相关性模块基因取交集,获得7个基因。②对上述7个基因进行LASSO回归算法筛选出4个基因,进行SVM-RFE机器学习算法筛选出5个基因,两者取交集后确定了3个核心基因,分别为重组1号染色体开放阅读框150蛋白(germinal center associated signaling and motility like,GCSAML)、整合素β8(Integrin beta-8,ITGB8)和羧肽酶A3(carboxypeptidase A3,CPA3);绘制3个核心基因的ROC曲线下面积分别为0.744,0.739,0.734,提示均具有很好的诊断价值,可作为纤维肌痛综合征的生物标志物。③免疫浸润分析结果显示,与对照组相比纤维肌痛综合征患者记忆B细胞、CD56 bright NK细胞和肥大细胞显著下调(P<0.05),且与上述3个生物标志物显著正相关(P<0.05)。④富集分析结果提示,纤维肌痛综合征的富集途径包括9条,主要与嗅觉传导、神经活性配体-受体相互作用及感染等通路密切相关。⑤上述结果显示,纤维肌痛综合征的发生发展与多基因参与、免疫调节异常及多个通路失调有关,但这些基因与免疫细胞之间的相互作用,以及它们与各通路之间的关系尚需进一步研究。

滑坡易发性预测建模的不确定性:不同“非滑坡样本”选择方式的影响

滑坡易发性预测建模中如何选择非滑坡是影响建模结果的重要不确定因素。为研究不同非滑坡选择方式的影响规律,拟用5种方式,即全区随机、坡度低于5°区域、滑坡缓冲300 m外区域、信息量(IV)法、半监督法来选择出与滑坡等比例的非滑坡样本;进一步将各选择方式与随机森林(RF)耦合构建随机RF、低坡度RF、缓冲区RF、IV–RF及半监督RF等模型。以江西南康区为例,获取高程、岩性、公路密度等19种环境因子和233个滑坡编录,将滑坡编录划分为2598个滑坡栅格单元构建上述耦合模型的输入–输出数据集。再采用预测精度和易发性指数分布等指标分析其建模不确定性。进一步针对耦合模型预测的滑坡易发性指数分布不合理等问题,在半监督RF建模时采用滑坡与非滑坡比例为1∶2的样本集开展建模并与1∶1等比例样本集工况作对比。结果表明:1)低坡度RF、缓冲区RF、IV–RF和半监督RF等模型的预测精度均大幅优于随机RF模型,可见准确选择非滑坡样本对易发性建模至关重要;2)半监督RF模型选择非滑坡样本的建模性能最优,且半监督RF在滑坡∶非滑坡=1∶2比其在1∶1时预测的易发性指数分布规律更准确可信。后续研究中有必要更深入探索滑坡与非滑坡样本的比例问题。

基于IF-AD-ELM的特高压直流输电系统故障辨识

针对现有的特高压直流(ultra high voltage direct current,UHVDC)输电系统故障检测方法灵敏度低、难以识别高阻接地故障的问题,提出了一种基于整数因子(integer factor,IF)-近似导数(approximate derivative,AD)和极限学习机(extreme learning machine,ELM)的特高压直流输电系统故障辨识方法。其中整数因子用于分析不同采样频率下的信号,近似导数法用于获得信号不同程度的细节系数。首先,基于不同的整数因子对信号进行下采样,并利用近似导数法对所得信号求一阶、二阶和三阶近似导数。其次,分别计算各个子信号的熵特征。然后,用基于交叉验证的递归特征消除(recursive feature elimination with cross validation,RFECV)算法对得到的一系列特征进行特征筛选,并结合ELM对特高压直流输电系统进行故障辨识。最后,在Matlab/Simulink环境中搭建了±800 kV的UHVDC系统模型,模拟不同故障类型。实验结果表明,所提方法在识别特高压直流输电系统不同类型故障时有更高的准确率,且耐受过渡电阻能力强。