Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

INTEGRATED VEGETATION CLASSIFICATION AND MAPPINGUSING REMOTE SENSING AND GIS TECHNIQUES

NOAA-AVHRR data have been more and more used by scientists because of its short temporal resolution,large scope, inexpensive cost and broad wave bands. On macro and middle scale of vegetation remote sensing, NOAAAVHRR possesses an advantage when compared with other satellites. However, because NOAA-AVHRR also problem of low resolution, data distortion and geometrical distortion, in the area of application of NOAA-AVHRR in largescale vegetation - mapping, the accuracy of vegetation classification should be improved. This paper discuss the feasibilityof integrating the geographic data in GIS(Geographical Information System) and remotely sensed data in GIS. Under theenvironment of GIS, temperature, precipitation and elevation, which serve as main factors affecting vegetation growth,were processed by a mathematical model and qualified into geographic image under a certain grid system. The geographicimage were overlaid to the NOAA-AVHRR data which had been compressed and processed. In order to evaluate the usefulness of geographic data for vegetation classification, the area under study was digitally classified by two groups of interpreter: the proposed methodology using maximum likelihood classification assisted by the geographic database and a conventional maximum likelihood classification only. Both result were compared using Kappa statistics. The indices to both theproposed and the conventional digital classification methodology were 0. 668(yew good) and 0. 563(good), respetively.The geographic database rendered an improvement over the conventional digital classification. Furthermore, in this study,some problems related to multi-sources data integration are also discussed.

Clastic compaction unit classification based on clay content and integrated compaction recovery using well and seismic data

Compaction correction is a key part of paleogeomorphic recovery methods. Yet, the influence of lithology on the porosity evolution is not usually taken into account. Present methods merely classify the lithologies as sandstone and mudstone to undertake separate porositydepth compaction modeling. However, using just two lithologies is an oversimplification that cannot represent the compaction history. In such schemes, the precision of the compaction recovery is inadequate. To improve the precision of compaction recovery, a depth compaction model has been proposed that involves both porosity and clay content. A clastic lithological compaction unit classification method, based on clay content, has been designed to identify lithological boundaries and establish sets of compaction units. Also, on the basis of the clastic compaction unit classification, two methods of compaction recovery that integrate well and seismic data are employed to extrapolate well-based compaction information outward along seismic lines and recover the paleo-topography of the clastic strata in the region. The examples presented here show that a better understanding of paleo-geomorphology can be gained by applying the proposed compaction recovery technology.

Integrability classification and exact solutions to generalized variable-coefficient nonlinear evolution equation

This paper is concerned with the generalized variable-coefficient nonlinear evolution equation（vc-NLEE）.The complete integrability classification is presented,and the integrable conditions for the generalized variable-coefficient equations are obtained by the Painlevé analysis.Then,the exact explicit solutions to these vc-NLEEs are investigated by the truncated expansion method,and the Lax pairs（LP） of the vc-NLEEs are constructed in terms of the integrable conditions.

Improvement the Accuracy of Six Applied Classification Algorithms through Integrated Supervised and Unsupervised Learning Approach

We have presented an integrated approach based on supervised and unsupervised learning tech- nique to improve the accuracy of six predictive models. They are developed to predict outcome of tuberculosis treatment course and their accuracy needs to be improved as they are not precise as much as necessary. The integrated supervised and unsupervised learning method (ISULM) has been proposed as a new way to improve model accuracy. The dataset of 6450 Iranian TB patients under DOTS therapy was applied to initially select the significant predictors and then develop six predictive models using decision tree, Bayesian network, logistic regression, multilayer perceptron, radial basis function, and support vector machine algorithms. Developed models have integrated with k-mean clustering analysis to calculate more accurate predicted outcome of tuberculosis treatment course. Obtained results, then, have been evaluated to compare prediction accuracy before and after ISULM application. Recall, Precision, F-measure, and ROC area are other criteria used to assess the models validity as well as change percentage to show how different are models before and after ISULM. ISULM led to improve the prediction accuracy for all applied classifiers ranging between 4% and 10%. The most and least improvement for prediction accuracy were shown by logistic regression and support vector machine respectively. Pre-learning by k- mean clustering to relocate the objects and put similar cases in the same group can improve the classification accuracy in the process of integrating supervised and unsupervised learning.

Historical-Dynamic Integrative Classification of Basinogenesis and Ore-Forming Basins

Interesting classifications of basinogenesis and basins were proposed by many scientists. They classified basinogenesis and basins mainly from a single angle, either from a historical angle or from a dynamic angle . In order to more comprehensively understand them for more effectively guiding prospecting and exploration, the author integrates the two methods of analysis with each other and proposes an integrative classification .According to the historical - dynamic integrative classification,basinogenesis and basins can be.di-vided into three types :oceanic crust type ,embryo-continental (transitional )crust type and continental crust type .Oceanic crust type can be subdivided into mobile region type (mainly tenskmal )and stable region type . Embryo-continental type includes pre-geosynclinal type (divisible into several mobile region types and stable region types with tensional type predominating among mobile region types ) and ear ly-geosynclinal type (mainly tenskmal ) .Continental crust type includes late- geosynclinal (fold belt)type (compressional or tenskmal ),platform type (mainly sinking and rarely tenskmal subsidence-aulacogen)and geodepression (diwa )type (compressional , tenskmal or compresskmal-tenskmal ).

Least Squares One-Class Support Tensor Machine

One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods.

HISTORICAL-DYNAMIC INTEGRATIVE CLASSIFICATION OF BASINOGENESIS AND ORE-FORMING BASINS

Interesting classifications of basinogenesis and basins were proposed by many seientists. They classified basinogenesis and basins mainly from a single angle, either from a historical angle or from a dynamie angle. In order to more comprehensively understand them for moore effectively guidlilg prospeeting and exploration, the author integrates the two methods of analysis wilh cach other and proposes an integrative classification. According to the historieal-dynamic integrative classification, basinogenesis and basins can be divided into three types: occanic erust type. embryo-continental (transitional ) erust iype and continental crust type. Oceanie erust type call be subdivided into mobile region type (mainly tensional) and stable region type. Embryo-continental type includes pre-geosynclinal type (divisible into several mobile region types and stable region types with tensional type predoiminating among mobile region trpes) and early-geosynelinal type (mainly tensional). Continental erust type ineludes late-gcosynelinal (fold belt) type (compressional or tensional), platform type (mainly sinking and rarely tensional subsidence-aulacogen) and gcodepression (diwa) type (compressional, tensional or compressional-tensional ).

An applied research on remote sensing classification in the Loess Plateau

Dae to complex terrain of the Loess Plateau, the classification accuracy is unsatisfactory when a single supervised classification is used in die remote sensing investigation of the sloping field. Taking the loess hill and gully area of northern Shaanxi Province as a test area, a research was conducted to extract sloping field and other land use categories by applying an integrated classification. Based on an integration of supervised classification aad unsupervised classification, sampling method is remarkably unproved. The results show that the classification accuracy is satisfactory by the method and is of critical significance in obtaining up-to-date information of the sloping field, which should be helpful in the state key project of converting farmland to forest and grassland on slope land in this area. This research sought to improve the application accuracy of image classification in complex terrain areas.

Fruit Image Classification Using Deep Learning

Fruit classification is found to be one of the rising fields in computer and machine vision.Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues.The performance of the classification scheme depends on the range of captured images,the volume of features,types of characters,choice of features from extracted features,and type of classifiers used.This paper aims to propose a novel deep learning approach consisting of Convolution Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-TermMemory(LSTM)application to classify the fruit images.Classification accuracy depends on the extracted and selected optimal features.Deep learning applications CNN,RNN,and LSTM were collectively involved to classify the fruits.CNN is used to extract the image features.RNN is used to select the extracted optimal features and LSTM is used to classify the fruits based on extracted and selected images features by CNN and RNN.Empirical study shows the supremacy of proposed over existing Support Vector Machine(SVM),Feed-forwardNeural Network(FFNN),and Adaptive Neuro-Fuzzy Inference System(ANFIS)competitive techniques for fruit images classification.The accuracy rate of the proposed approach is quite better than the SVM,FFNN,and ANFIS schemes.It has been concluded that the proposed technique outperforms existing schemes.

Deep Domain-Adversarial Anomaly Detection With One-Class Transfer Learning

Despite the big success of transfer learning techniques in anomaly detection,it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification,especially for the data with a large distribution difference.To address this challenge,a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper.First,by integrating a hypersphere adaptation constraint into domainadversarial neural network,a new hypersphere adversarial training mechanism is designed.Second,an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible.Through transferring oneclass detection rule in the adaptive extraction of domain-invariant feature representation,the end-to-end anomaly detection with one-class classification is then enhanced.Furthermore,a theoretical analysis about the model reliability,as well as the strategy of avoiding invalid and negative transfer,is provided.Experiments are conducted on two typical anomaly detection problems,i.e.,image recognition detection and online early fault detection of rolling bearings.The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in terms of detection accuracy and robustness.

Blockchain Technology Based Information Classification Management Service

Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information,but also the expansion of areas and assets to be protected.In terms of information security,it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets.Also,it has caused difficulties in managing those issues due to reasons such as mutual interference,countless security events and logs’data,etc.Within this security environment,an organization should identify and classify assets based on the value of data and their security perspective,and then apply appropriate protection measures according to the assets’security classification for effective security management.But there are still difficulties stemming from the need to manage numerous security solutions in order to protect the classified assets.In this paper,we propose an information classification management service based on blockchain,which presents and uses a model of the value of data and the security perspective.It records transactions of classifying assets and managing assets by each class in a distributed ledger of blockchain.The proposed service reduces assets to be protected and security solutions to be applied,and provides security measures at the platform level rather than individual security solutions,by using blockchain.In the rapidly changing security environment of Industry 4.0,this proposed service enables economic security,provides a new integrated security platform,and demonstrates service value.

CLASSIFICATION OF SOLUTIONS TO HIGHER FRACTIONAL ORDER SYSTEMS

Let 0<α,β<n and f,g∈ C([0,∞)×[0,∞))be two nonnegative functions.We study nonnegative classical solutions of the system{(-△)^(α/2)u=f(u,v)in R^(n),(-△)^(β/2)v=g(u,v)in R^(n),and the corresponding equivalent integral system.We classify all such solutions when f(s,t)is nondecreasing in s and increasing in t,g(s,t)is increasing in s and nondecreasing in i,and f(μ^(n-α)s,μ^(n-β)t)/μ^(n-α),g(μ^(n-α)s,μ^(n-β)t)/μ^(n-β)are nonincreasing in μ>0 for all s,t≥0.The main technique we use is the method of moving spheres in integral forms.Since our assumptions are more general than those in the previous literature,some new ideas are introduced to overcome this difficulty.

Using Non-Additive Measure for Optimization-Based Nonlinear Classification

Over the past few decades, numerous optimization-based methods have been proposed for solving the classification problem in data mining. Classic optimization-based methods do not consider attribute interactions toward classification. Thus, a novel learning machine is needed to provide a better understanding on the nature of classification when the interaction among contributions from various attributes cannot be ignored. The interactions can be described by a non-additive measure while the Choquet integral can serve as the mathematical tool to aggregate the values of attributes and the corresponding values of a non-additive measure. As a main part of this research, a new nonlinear classification method with non-additive measures is proposed. Experimental results show that applying non-additive measures on the classic optimization-based models improves the classification robustness and accuracy compared with some popular classification methods. In addition, motivated by well-known Support Vector Machine approach, we transform the primal optimization-based nonlinear classification model with the signed non-additive measure into its dual form by applying Lagrangian optimization theory and Wolfes dual programming theory. As a result, 2n – 1 parameters of the signed non-additive measure can now be approximated with m (number of records) Lagrangian multipliers by applying necessary conditions of the primal classification problem to be optimal. This method of parameter approximation is a breakthrough for solving a non-additive measure practically when there are relatively small number of training cases available (mn-1). Furthermore, the kernel-based learning method engages the nonlinear classifiers to achieve better classification accuracy. The research produces practically deliverable nonlinear models with the non-additive measure for classification problem in data mining when interactions among attributes are considered.

Optimizing Query Results Integration Process Using an Extended Fuzzy C-Means Algorithm

Cleaning duplicate data is a major problem that persists even though many works have been done to solve it, due to the exponential growth of data amount treated and the necessity to use scalable and speed algorithms. This problem depends on the type and quality of data, and differs according to the volume of data set manipulated. In this paper we are going to introduce a novel framework based on extended fuzzy C-means algorithm by using topic ontology. This work aims to improve the OLAP querying process over heterogeneous data warehouses that contain big data sets, by improving query results integration, eliminating redundancies by using the extended classification algorithm, and measuring the loss of information.

Subscription to Digital Libraries and Corresponding Journal Impact: A Value-Based Approach to Demand for Digital Research Data—Confucian Integration of Curricula and “Market String” Digital Education Systems

This paper develops a functional relation between Digital Libraries and Confucion Integrated Curriculum Learning systems. We show that under certain properties of Learning Systems which can implement laissez-faire markets under uncertainty, the systems integration is possible in entropy space.

THE CLASSIFICATION OF THE SURROUNDINGS OF COAL MINING ROADWAYS

This paper introduces the calculation of the deformation of the surroundings of roadways and the division of surroundings into 5 levels by means of fuzzy integral assess matrix, which serves as the scientific basis for selecting supporting pattern of roadways and determining the parameters of support.

水泥稳定碎石基层取芯芯样分类与整体性评价技术标准

为了实现水泥稳碎石基层整体性质量客观和公正的评价,消除7 d取芯芯样质量评判的随意性、弥补因不考虑气候条件和公路等级等因素对施工质量评价的局限性,选择高速、一级、二级和三级共5条公路,对7 d龄期的水泥稳定粒料类基层进行了大量的取芯试验,系统地对取芯芯样的形态进行了调研、统计和分析,根据芯样的完整程度,将其分为完整类、残缺类和松散类3类,芯样完整性的差异代表其扩散荷载应力能力的不同,根据芯样的致密程度,将芯样进一步分成8级,芯样致密性的不同体现服役功能性差别,提出了评价路段芯样完整率的计算方法,确定了芯样完整率技术标准的确定原则,对于季冻区宜采用F(Ⅰ+Ⅱ)作为评价指标,对于非季冻区宜采用F(Ⅰ)作为评价指标,并根据回归曲线,提出了不同区域不同等级道路7 d龄期内的芯样完整率技术标准。结果表明:芯样完整率与道路等级呈线性关系,道路等级对其影响幅度为2%~9%,养生模式的影响幅度为10%左右,气候因素的影响幅度为5%左右。研究成果实现了对半刚性基层取芯芯样质量和整体性质量的定量化评价。

一种基于多分类器和证据理论融合的水质分类方法

针对单分类器对不同水质类别识别不均衡、水质分类准确率较低、适应性较差的问题,提出一种基于多分类器和证据理论融合的水质分类方法。选取深度神经网络分类器、改进支持向量机分类器和贝叶斯分类器3种分类器,通过全概率公式构建信度函数,基于证据理论对信度函数进行融合,获得多分类器融合模型。从国家地表水水质自动站发布的2022年3月1—22日水质数据中选取3 558条数据为样本集,采用DNN水质分类模型、PSO-SVM水质分类模型、贝叶斯水质分类模型和多分类器融合模型对待测样本进行测试。结果表明:多分类器融合模型对水质类别判定的平均准确率、精确率、召回率和F1值分别为94.2%、93.8%、94.2%和94.0%。相较于DNN水质分类模型、PSO-SVM水质分类模型、贝叶斯水质分类模型,多分类器融合模型准确率分别提高5.6%、9.8%和13.6%,精确率分别提高5.2%、10.0%和10.9%,召回率分别提高5.6%、9.8%和13.6%,F1值分别提高5.4%、10.2%和12.3%,多分类器融合模型在水质分类方面的准确性和适应性更高。