Local singularity and S–A methods for analyzing ore-producing anomalies in the Jianbiannongchang area of Heilongjiang,China

The Heilongjiang Jianbiannongchang area is located at the confluence of the Great and Lesser Xing’an Ranges.This area has a complex magmatic and tectonic evolutionary history that has resulted in a complex and diverse geological background for mineralization.In this study,isometric logarithmic ratio(ILR)transformations of Au,Cu,Pb,Zn,and Sb contents were performed in the1:50,000 soil geochemical data of the Jianbiannongchang area.Robust principal component analysis(RPCA)was conducted based on ILR transformation.The local singularity and spectrum-area(S-A)methods were used to extract information on mineralogic anomalies.The results showed that:(1)the transformed data eliminated the influence of the original data closure effect,and the PC1and PC2 information obtained by applying RPCA reflected ore-producing element anomalies dominated by Au and Cu.(2)The local singularity method can enhance the information of the local strong and weak slow anomalies.After performing local singularity analysis on PC1 and PC2,the obtained local anomalies reflected the local singularity spatial anomaly patterns related to Cu and Au mineralization in this area,which is an effective method for trapping ore-producing anomalies.(3)Furthermore,the composite anomaly decomposition of PC1 and PC2 was performed using the S-A method,and the screened anomalous and background fields reflect the ore-producing anomalies related to Cu and Au mineralization.This information is in agreement with known Cu and Au mineralization.(4)The geochemical anomalies with mineralization potential were obtained outside the known mineralization sites by integrating the information of oreproducing anomalies extracted by the local singularity and S-A methods,providing the theoretical basis and exploration direction for future exploration in the study area.

Capability of discrete element method to investigate the macro-micro mechanical behaviours of granular soils considering different stress conditions and morphological gene mutation

Discrete element method(DEM)has been widely utilised to model the mechanical behaviours of granular materials.However,with simplified particle morphology or rheology-based rolling resistance models,DEM failed to describe some responses,such as the particle kinematics at the grain-scale and the principal stress ratio against axial strain at the macro-scale.This paper adopts a computed tomography(CT)-based DEM technique,including particle morphology data acquisition from micro-CT(mCT),spherical harmonic-based principal component analysis(SH-PCA)-based particle morphology reconstruction and DEM simulations,to investigate the capability of DEM with realistic particle morphology for modelling granular soils’micro-macro mechanical responses with a consideration of the initial packing state,the morphological gene mutation degree,and the confining stress condition.It is found that DEM with realistic particle morphology can reasonably reproduce granular materials’micro-macro mechanical behaviours,including the deviatoric stressevolumetric straineaxial strain response,critical state behaviour,particle kinematics,and shear band evolution.Meanwhile,the role of multiscale particle morphology in granular soils depends on the initial packing state and the confining stress condition.For the same granular soils,rougher particle surfaces with a denser initial packing state and a higher confining stress condition result in a higher degree of shear strain localisation.

Identification and analysis of serum samples by surface-enhanced Raman spectroscopy combined with characteristic ratio method and PCA for gastric cancer detection

This study aimed to explore the application of surface-enhanced Raman scattering(SERS)in the rapid diagnosis of gastric cancer.The SERS spectra of 68 serum samples from gastric cancer patients and healthy volunteers were acquired.The characteristic ratio method(CRM)and principal component analysis(PCA)were used to differentiate gastric cancer serum from normal serum.Compared with healthy volunteers,the serum SERS intensity of gastric cancer patients was relatively high at 722 cm^(-1),while it was relatively low at 588,644,861,1008,1235,1397,1445 and 1586 cm^(-1).These results indicated that the relative content of nucleic acids in the serum of gastric cancer patients rises while the relative content of amino acids and carbohydrates decreases.In PCA,the sensitivity and specificity of discriminating gastric cancer were 94.1%and 94.1%,respectively,with the accuracy of 94.1%.Based on the intensity ratios of four characteristic peaks at 722,861,1008 and 1397 cm^(-1),CRM presented the diagnostic sensitivity and specificity of 100%and 97.4%,respectively,and the accuracy of 98.5%.Therefore,the three peak intensity ratios of I_(722)/I_(861),I_(722)/I_(1008)and I_(722)/I_(1397)can be considered as biologicalfingerprint information for gastric cancer diagnosis and can rapidly and directly reflect the physiological and pathological changes associated with gastric cancer development.This study provides an important basis and standards for the early diagnosis of gastric cancer.

Higher-order principal component pursuit via tensor approximation and convex optimization

Recovering the low-rank structure of data matrix from sparse errors arises in the principal component pursuit （PCP）. This paper exploits the higher-order generalization of matrix recovery, named higher-order principal component pursuit （HOPCP）, since it is critical in multi-way data analysis. Unlike the convexification （nuclear norm） for matrix rank function, the tensorial nuclear norm is stil an open problem. While existing preliminary works on the tensor completion field provide a viable way to indicate the low complexity estimate of tensor, therefore, the paper focuses on the low multi-linear rank tensor and adopt its convex relaxation to formulate the convex optimization model of HOPCP. The paper further propose two algorithms for HOPCP based on alternative minimization scheme： the augmented Lagrangian alternating direction method （ALADM） and its truncated higher-order singular value decomposition （ALADM-THOSVD） version. The former can obtain a high accuracy solution while the latter is more efficient to handle the computationally intractable problems. Experimental results on both synthetic data and real magnetic resonance imaging data show the applicability of our algorithms in high-dimensional tensor data processing.

Source analysis and risk evaluation of heavy metal in the river sediment of polymetallic mining area:Taking the Tonglüshan skarn type Cu-Fe-Au deposit as an example,Hubei section of the Yangtze River Basin,China

In this paper,25 sampling points of overlying deposits in Tonglushan mining area,Daye City,Hubei Province,China were tested for heavy metal content to explore pollution characteristics,pollution sources and ecological risks of heavy metals in sediments.A geo-accumulation index method was used to evaluate the degree of heavy metal pollution in the sediment.The mean sediment quality guideline quotient was used for evaluating the ecological risk level of heavy metal in the sediment.And a method of correlation analysis,clustering analysis,and principal component analysis was used for preliminary analysis on the source of heavy metal in the sediment.It was indicated that there was extremely heavy metal pollution in the sediment,among which Cd was extremely polluted,Cu strongly contaminated,Zn,As,and Hg moderately contaminated,and Pb,Cr,and Ni were slightly contaminated.It was also indicated by the mean sediment quality guideline-quotient result that there was a high ecological risk of heavy metals in the sediment,and 64%of the sample sites had extremely high hidden biotoxic effects.For distribution,the contamination of branches was worse than that of the main channel of Daye Dagang,and the deposition of each heavy metal was mainly influenced by the distance from this sample site to the sewage draining exit of a tailings pond.The source analysis showed that the heavy metals in the sediment come from pollution discharging of mining and beneficiation companies,tailings ponds,smelting companies,and transport vehicles.In the study area,due to the influence of heavy metal discharging from these sources,the ecotoxicity of heavy metals in the sediment was extremely high,and Cd was the most toxic pollutant.The research figured out the key restoration area and elements for ecological restoration in the sediment of the Tonglüshan mining area,which could be referenced by monitoring and governance of heavy metal pollution in the sediment of the polymetallic mining area.

FUZZY PRINCIPAL COMPONENT ANALYSIS AND ITS KERNEL-BASED MODEL

Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input data may not be fully assigned to one class and it may partially belong to other classes.Based on the theory of fuzzy sets,this paper presents Fuzzy Principal Component Analysis(FPCA)and its nonlinear extension model,i.e.,Kernel-based Fuzzy Principal Component Analysis(KFPCA).The experimental results indicate that the proposed algorithms have good performances.

Accelerated Matrix Recovery via Random Projection Based on Inexact Augmented Lagrange Multiplier Method

In this paper, a unified matrix recovery model was proposed for diverse corrupted matrices. Resulting from the separable structure of the proposed model, the convex optimization problem can be solved efficiently by adopting an inexact augmented Lagrange multiplier (IALM) method. Additionally, a random projection accelerated technique (IALM+RP) was adopted to improve the success rate. From the preliminary numerical comparisons, it was indicated that for the standard robust principal component analysis (PCA) problem, IALM+RP was at least two to six times faster than IALM with an insignificant reduction in accuracy; and for the outlier pursuit (OP) problem, IALM+RP was at least 6.9 times faster, even up to 8.3 times faster when the size of matrix was 2 000×2 000.

Combining Principal Component Regression and Artificial Neural Network to Predict Chlorophyll-a Concentration of Yuqiao Reservoir’s Outflow

In order to investigate the eutrophication degree of Yuqiao Reservoir, a hybrid method, combining principal component regression (PCR) and artificial neural network (ANN), was adopted to predict chlorophyll-a concentration of Yuqiao Reservoir’s outflow. The data were obtained from two sampling sites, site 1 in the reservoir, and site 2 near the dam. Seven water variables, namely chlorophyll-a concentration of site 2 at time t and that of both sites 10 days before t, total phosphorus(TP), total nitrogen(TN), dissolved oxygen(DO), and temperature from January 2000 to September 2002, were utilized to develop models. To remove the collinearity between the variables, principal components extracted by principal component analysis were employed as predictors for models. The performance of models was assessed by the square of correlation coefficient, mean absolute error (MAE), root mean square error (RMSE) and average absolute relative error (AARE). Results show that the hybrid method has achieved more accurate prediction than PCR or ANN model. Finally, the three models were applied to predicting the chlorophyll-a concentration in 2003. The predictions of the hybrid method were found to be consistent with the observed values all year round, while the results of PCR and ANN models did not fit quite well from July to October.

A deep kernel method for lithofacies identification using conventional well logs

How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.

Fast Tensor Principal Component Analysis via Proximal Alternating Direction Method with Vectorized Technique

This paper studies the problem of tensor principal component analysis (PCA). Usually the tensor PCA is viewed as a low-rank matrix completion problem via matrix factorization technique, and nuclear norm is used as a convex approximation of the rank operator under mild condition. However, most nuclear norm minimization approaches are based on SVD operations. Given a matrix , the time complexity of SVD operation is O(mn2), which brings prohibitive computational complexity in large-scale problems. In this paper, an efficient and scalable algorithm for tensor principal component analysis is proposed which is called Linearized Alternating Direction Method with Vectorized technique for Tensor Principal Component Analysis (LADMVTPCA). Different from traditional matrix factorization methods, LADMVTPCA utilizes the vectorized technique to formulate the tensor as an outer product of vectors, which greatly improves the computational efficacy compared to matrix factorization method. In the experiment part, synthetic tensor data with different orders are used to empirically evaluate the proposed algorithm LADMVTPCA. Results have shown that LADMVTPCA outperforms matrix factorization based method.

Water Quality Analysis of Fuping Section of the Shichuan River Based on Different Evaluation Methods

In order to study the water quality of the Shichuan River basin in Fuping,Shaanxi Province,based on improved Nemerow index method,comprehensive pollution index method and principal component analysis method,eight water quality indexes such as pH,dissolved oxygen(DO),total dissolved solids(TDS),COD,total hardness,total phosphorus,total nitrogen and Zn in three monitoring sections of Fuping section of the Shichuan River in Shaanxi Province were detected and analyzed.The results show that the water quality of the surface water in the Shichuan River basin is gradeⅢorⅣwater,that is,the water is slightly polluted and moderately polluted.It is necessary to monitor the water quality after regulation and clarify the main factors causing the water pollution.

Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.

Principal component analysis and cluster analysis based orbit optimization for earth observation satellites

This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.

星载微波部件微放电阈值的改进多粒子蒙特卡罗计算方法

星载微波部件的微放电效应是导致航天器谐振类设备失谐、噪声电平抬高、输出功率下降,甚至影响通信信道乃至整个微波传输系统彻底失效的瓶颈问题之一。在设计阶段对星载微波部件微放电效应进行精准的评估是减少地面反复试验,避免延误研制周期的重要手段。为了进一步改善现有蒙特卡洛方法的计算准确度问题,文中提出了一种精度更高的计算星载微波部件微放电阈值的蒙特卡罗方法,该方法对参与微放电过程的初始电子进行了动态调整,采用四阶龙格-库塔法推进微波部件中电子的运动轨迹,基于Furman模型描述电子与微波表面相互作用的二次电子发射过程,按照碰撞电子产生的实际二次电子个数及对应能量参与碰撞时刻后的微放电过程,该多粒子-多碰撞过程更加客观、准确地表征了微放电效应发生的物理过程。以平板传输线和同轴传输线为例,文中所提出的方法相对于已有的蒙特卡罗方法计算精度显著提升,同时计算效率优于商用CST的粒子模拟结果。

大凉山地区不同马铃薯品种产量和营养品质的综合评价

为筛选出适宜四川大凉山地区种植的马铃薯品种,对14个马铃薯品种的12个产量与营养品质指标进行测定,并结合主成分分析、隶属函数法、系统聚类分析对马铃薯产量和品质表现进行综合评价。结果表明,单株结薯数、单薯鲜质量、单株块茎鲜质量、单产、还原糖含量的变异系数均超过30%;单株块茎鲜质量与单薯鲜质量、单产和蛋白质含量呈极显著正相关,干物质含量与淀粉含量、蛋白质含量呈极显著正相关,单薯鲜质量与VC含量、锌含量呈显著负相关,蛋白质含量与钾含量呈极显著负相关;主成分分析结果表明,12个指标可用4个主成分来表示,方差累积贡献率达到86.040%。进一步采用隶属函数法和系统聚类分析将14个品种分为3类,筛选出6个综合表现较优的品种,分别为川凉薯10号、青薯9号、川芋50、川凉芋13、云薯108、川芋22号。

外源钙对甘草生长及生理特性的影响

【目的】为揭示宁夏中部干旱带甘草优质高产栽培过程中甘草对不同钙浓度的适应机制,探究了外源钙对甘草生长及生理生化特性的影响。【方法】以乌拉尔甘草为试验材料,设置0(CK)、5(G_(1))、10(G_(2))、15(G_(3))、20 mmol/L(G_(4))5个钙浓度,分析比较了不同浓度外源钙处理下甘草生长、光合特性及抗氧化酶活性的变化,同时采用主成分分析和隶属函数法综合评价出适宜甘草生长的外源钙施用浓度。【结果】随着钙浓度的增加,甘草主根长、主根直径、单根鲜质量呈先增加后下降的趋势,且不同处理之间差异不显著;甘草叶片叶绿素含量、净光合速率(Pn)、蒸腾速率(Tr)、气孔导度(Gs)、胞间CO_(2)浓度(Ci)、超氧化物歧化酶(SOD)活性、过氧化氢酶(CAT)活性、过氧化物酶(POD)活性整体呈现先升后降的趋势,钙浓度为10 mmol/L时,Tr、Pn、Gs、SOD活性、CAT活性均达到峰值,且Pn、Gs显著高于对照(P<0.05)。MDA含量随着钙浓度的增加呈先下降后上升的趋势,各浓度处理间差异显著(P<0.05),钙浓度为10mmol/L时的MDA含量最低。综合评价结果表明,钙浓度为10mmol/L时,隶属函数值的均值最大,为0.739。【结论】施用外源钙有利于改善甘草的生理生化特性,促进生长发育;10mmol/L钙浓度是甘草生长的最佳施用量,可为干旱地区甘草的栽培模式提供理论依据。

基于德尔菲法构建医疗机构突发公共卫生事件应急能力评价体系及应用

目的 探索构建适用于北京市通州区医疗机构突发公共卫生事件应急能力评价体系,了解应急能力现状,为促进应急能力提升提供参考。方法 采用德尔菲专家咨询法建立应急能力评价体系,采用主成分分析法确定指标权重,采用Excel软件建立数据库,SPSS 21.0对资料进行描述性统计分析。结果 专家积极系数为100%,专家权威程度系数为(0.89±0.05)。两轮次专家评分均值为4.42~4.79,变异系数<11%,专家意见较为一致。主成分分析指标权重显示,社区卫生服务中心有5个一级指标和18个二级评价指标(KMO=0.453,Bartlett χ2=27.764,P<0.01)。二级及以上医疗机构有6个一级指标和20个二级评价指标(KMO=0.698,Bartlett χ2=86.324,P<0.01),各维度相关性较强。二级及以上医疗机构应急能力评定为强、中、弱各有2、3和2家,社区卫生服务中心评定为强、中、弱各有6、10和6家。结论 本研究建立的应急能力评价指标体系,经调查应用符合辖区医疗机构应急能力现状,为医疗机构提升卫生应急能力提供参考,在今后工作中可进一步验证和优化。

基于空间投影和聚类划分的SVR加速算法

数据不仅能产生价值,还对统计学的科学发展提供了动力。随着科技的飞速发展,海量数据得以涌现,但大规模的数据会导致很多传统处理方法很难满足各领域对数据分析的需求。面对海量数据时代学习算法的低效性,分治法通常被认为是解决这一问题最直接、最广泛使用的策略。SVR是一种强大的回归算法,在模式识别和数据挖掘等领域有广泛应用。然而在处理大规模数据时,SVR训练效率低。为此,该文利用分治思想提出一种基于空间投影和聚类划分的SVR加速算法(PKM-SVR)。利用投影向量将数据投影到二维空间;利用聚类方法将数据空间划分为k个互不相交的区域;在每个区域上训练SVR模型;利用每个区域的SVR模型预测落入同一区域的待识别样本。在标准数据集上与传统的数据划分方法进行对比实验,实验结果表明该算法训练速度较快,并表现出更好的预测性能。

基于层次分解、主成分分析和高斯混合模型的火成岩岩性识别——以惠州26洼古潜山为例

火成岩油气成藏规律复杂,受到火山运动、强构造运动以及风化剥蚀等叠加影响,火成岩的化学成分和结构构造复杂多样,非均质性极强,采用常规岩性识别方法难以一次性将所有岩性准确识别。借鉴层次分解思路,以惠州26洼古潜山为例,提出了一种火成岩岩性测井识别分类方法:综合考虑火成岩地质分类原则和测井响应特征来确定岩性识别层级,基于这种层次性的分类原则,在每一层次定量优选岩性识别敏感参数,建立研究区岩性识别优选层级;在明确岩性识别优选层级的基础上,逐级逐次使用主成分分析(PCA)和高斯混合模型(GMM)对岩性进行判别并确定其计算函数,建立分级优选岩性识别模型,最终达到整体岩性区分的目的。研究结果表明,研究区辉绿岩和闪长岩识别正确率分别为87.31%和84.32%,未分级未优选辉绿岩和闪长岩识别正确率为60.45%和54.88%,分级未优选其岩性识别正确率为69.61%和67.04%,有效提高了研究区的复杂岩性识别精度。该方法的提出对提高火成岩岩性识别精度提供了一种思路,也为研究区古潜山火成岩岩性精确识别提供了参考依据。

基于PCA-LM的空战目标威胁评估

空战过程中态势瞬息万变,获取敌目标的威胁是我方取得攻击占位优势和采取战术规避的前提条件。提出主成分分析法和阻尼最小二乘法相结合的回归模型对目标的威胁进行评估。利用主成分分析法,分析指标之间的相关性,转化成相互独立的分量,确定主成分分量,重构目标威胁评估体系;对目标威胁与主成分分量进行回归分析,利用阻尼最小二乘法对回归模型参数进行估计,得到主成分分量与目标威胁之间的统计关系;利用目标威胁估计值与实际值之间的误差大小,验证了回归模型的有效性。消除了指标之间的相关性对评估结果的影响,提高了评估结果的客观性,解决了传统评估方法忽略指标之间耦合性的问题。