Data classification method based on network dynamics analysis and cloud model

In order to reduce amount of data storage and improve processing capacity of the system, this paper proposes a new classification method of data source by combining phase synchronization model in network clustering with cloud model. Firstly, taking data source as a complex network, after the topography of network is obtained, the cloud model of each node data is determined by fuzzy analytic hierarchy process （AHP）. Secondly, by calculating expectation, entropy and hyper entropy of the cloud model, comprehensive coupling strength is got and then it is regarded as the edge weight of topography. Finally, distribution curve is obtained by iterating the phase of each node by means of phase synchronization model. Thus classification of data source is completed. This method can not only provide convenience for storage, cleaning and compression of data, but also improve the efficiency of data analysis.

Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification

Using melting layer(ML)and non-melting layer(NML)data observed with the X-band dual linear polarization Doppler weather radar(X-POL)in Shunyi,Beijing,the reflectivity(ZH),differential reflectivity(ZDR),and correlation coefficient(CC)in the ML and NML are obtained in several stable precipitation processes.The prior probability density distributions(PDDs)of the ZH,ZDR and CC are calculated first,and then the probabilities of ZH,ZDR and CC at each radar gate are determined(PBB in the ML and PNB in the NML)by the Bayesian method.When PBB>PNB the gate belongs to the ML,and when PBB<PNB the gate belongs to the NML.The ML identification results with the Bayesian method are contrasUsing melting layer(ML)and non-melting layer(NML)data observed with the X-band dual linear polarization Doppler weather radar(X-POL)in Shunyi,Beijing,the reflectivity(ZH),differential reflectivity(ZDR),and correlation coefficient(CC)in the ML and NML are obtained in several stable precipitation processes.The prior probability density distributions(PDDs)of the ZH,ZDR and CC are calculated first,and then the probabilities of ZH,ZDR and CC at each radar gate are determined(PBB in the ML and PNB in the NML)by the Bayesian method.When PBB>PNB the gate belongs to the ML,and when PBB<PNB the gate belongs to the NML.The ML identification results with the Bayesian method are contrasted under the conditions of the independent PDDs and joint PDDs of the ZH,ZDR and CC.The results suggest that MLs can be identified effectively,although there are slight differences between the two methods.Because the values of the polarization parameters are similar in light rain and dry snow,it is difficult for the polarization radar to distinguish them.After using the Bayesian method to identify the ML,light rain and dry snow can be effectively separated with the X-POL observed data.ted under the conditions of the independent PDDs and joint PDDs of the ZH,ZDR and CC.The results suggest that MLs can be identified effectively,although there are slight differences between the two methods.Because the values of the polarization parameters are similar in light rain and dry snow,it is difficult for the polarization radar to distinguish them.After using the Bayesian method to identify the ML,light rain and dry snow can be effectively separated with the X-POL observed data.

Dense Pellicular Agarose-Glass Beads for Expanded Bed Application: Flow Hydrodynamics and Solid Phase Classifications

Two dense pellicular agarose-glass matrices of different sizes and densities, i.e., AG-S and AG-L, have been characterized for their bed expansion behavior, flow hydrodynamics and particle classifications in an expanded bed system. A 26 mm ID column with side ports was used for sampling the liquid-solid suspension during expanded bed operations. Measurements of the collected solid phase at different column positions yielded the particle size and density distribution data. It was found that the composite matrices showed particle size as well as density classifications along the column axis, i.e., both the size and density of each matrix decreased with increasing the axial bed height. Their axial classifications were expressed by a correlation related to both the particle size and density as a function of the dimensionless axial bed height. The correlation was found to fairly describe the solid phase classifications in the expanded bed system. Moreover, it can also be applied to other two commercial solid matrices designed for expanded bed applications.

Forward prediction for tunnel geology and classification of surrounding rock based on seismic wave velocity layered tomography

Excavation under complex geological conditions requires effective and accurate geological forward-prospecting to detect the unfavorable geological structure and estimate the classification of surround-ing rock in front of the tunnel face.In this work,a forward-prediction method for tunnel geology and classification of surrounding rock is developed based on seismic wave velocity layered tomography.In particular,for the problem of strong multi-solution of wave velocity inversion caused by few ray paths in the narrow space of the tunnel,a layered inversion based on regularization is proposed.By reducing the inversion area of each iteration step and applying straight-line interface assumption,the convergence and accuracy of wave velocity inversion are effectively improved.Furthermore,a surrounding rock classification network based on autoencoder is constructed.The mapping relationship between wave velocity and classification of surrounding rock is established with density,Poisson’s ratio and elastic modulus as links.Two numerical examples with geological conditions similar to that in the field tunnel and a field case study in an urban subway tunnel verify the potential of the proposed method for practical application.

Classification of power quality combined disturbances based on phase space reconstruction and support vector machines

Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.

Electrical responses and classification of complex waterflooded layers in carbonate reservoirs: A case study of Zananor Oilfield, Kazakhstan

Experiments of electrical responses of waterflooded layers were carried out on porous,fractured,porous-fractured and composite cores taken from carbonate reservoirs in the Zananor Oilfield,Kazakhstan to find out the effects of injected water salinity on electrical responses of carbonate reservoirs.On the basis of the experimental results and the mathematical model of calculating oil-water relative permeability of porous reservoirs by resistivity and the relative permeability model of two-phase flow in fractured reservoirs,the classification standards of water-flooded layers suitable for carbonate reservoirs with complex pore structure were established.The results show that the salinity of injected water is the main factor affecting the resistivity of carbonate reservoir.When low salinity water(fresh water)is injected,the relationship curve between resistivity and water saturation is U-shaped.When high salinity water(salt water)is injected,the curve is L-shaped.The classification criteria of water-flooded layers for carbonate reservoirs are as follows:(1)In porous reservoirs,the water cut(fw)is less than or equal to 5%in oil layers,5%–20%in weak water-flooded layers,20%–50%in moderately water-flooded layers,and greater than 50%in strong water-flooded layers.(2)For fractured,porous-fractured and composite reservoirs,the oil layers,weakly water-flooded layers,moderately water-flooded layers,and severely water-flooded layers have a water content of less than or equal to 5%,5%and 10%,10%to 50%,and larger than 50%respectively.

Digital modulation classification using multi-layer perceptron and time-frequency features

Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.

Modulation classification of MPSK signals based on nonparametric Bayesian inference

A nonparametric Bayesian method is presented to classify the MPSK （M-ary phase shift keying） signals. The MPSK signals with unknown signal noise ratios （SNRs） are modeled as a Gaussian mixture model with unknown means and covariances in the constellation plane, and a clustering method is proposed to estimate the probability density of the MPSK signals. The method is based on the nonparametric Bayesian inference, which introduces the Dirichlet process as the prior probability of the mixture coefficient, and applies a normal inverse Wishart （NIW） distribution as the prior probability of the unknown mean and covariance. Then, according to the received signals, the parameters are adjusted by the Monte Carlo Markov chain （MCMC） random sampling algorithm. By iterations, the density estimation of the MPSK signals can be estimated. Simulation results show that the correct recognition ratio of 2/4/8PSK is greater than 95% under the condition that SNR 〉5 dB and 1 600 symbols are used in this method.

Water masses classification of the upper layer in the Equatorial Western Pacific using ISODATA of fuzzy cluster

-In this paper,by using ISODATA of fuzzy cluster,the water masses classification of the upper layer in the E-quatorial Western Pacific is carried out. On the basis of the degree of the membership in the obtained optima) classification matrix, the solid distribution of the detailed structure of water masses is made. The water of the upper layer,consisting of six water masses,may be divided into three layers,i, e. ,the surface,subsurface and intermediate layer. Besides analyzing the features of various water masses,a discussion on their distribution structure and formation mechanism is also made.

Analysis on Systematic Water Scarcity Based on Establishment of Water Scarcity Classification System

It would be very helpful for making countermeasures against complex water scarcity by analysis on systematic water scarcity.Based on the previous researches on water scarcity classification,a classification system of water scarcity was established according to contributing factors,which comprises three water scarcity categories caused by anthropic factors,natural factors and mixed factors respectively.Accordingly,the concept of systematic water scarcity was proposed,which can be defined as one type of water scarcity category caused by the discordance between water demand pattern determined by anthropic factors and water supply pattern controlled by natural factors in an evaluation region during a period.Systematic water scarcity has four features,namely space-time characteristic,scale property,externality and integrity,and can be divided into four developing phases including critical phase,early phase,middle phase and late phase according to various degrees of water scarcity.

Feature Extraction for Audio Classification of Gunshots Using the Hartley Transform

In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.

Modified Visual Geometric Group Architecture for MRI Brain Image Classification

The advancement of automated medical diagnosis in biomedical engineering has become an important area of research.Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences.The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal.The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models.One of the best models for image localization and classification is the Visual Geometric Group(VGG)model.In this study,an efficient modified VGG architecture for brain image classification is developed using transfer learning.The pooling layer is modified to enhance the classification capability of VGG architecture.Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5%improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa(REMBRANDT)database.

Comparative Study of the Geomagnetic Activity Effect on foF2 Variation as Defined by the Two Classification Methods at Dakar Station over Solar Cycle Phases

This paper aims to establish a comparison between both geomagnetic activity classification methods on foF2 diurnal variation over solar cycle phases. It concerns first a comparison of geomagnetic activity occurrences according to both classification methods;and second the geomagnetic effect on foF2 diurnal variation profiles as defined for the equatorial latitudes. The occurrences of the different disturbed geomagnetic activities (recurrent activity (RA), shock activity (SA) and fluctuant activity (FA)) according to both classifications (ancient classification (AC) and new classification (NC)) have been studied at Dakar ionosonde station (Lat: 14.8°N;Long: 342.6°E). Regarding both classifications, the RA occurs more during the decreasing phase. And it’s observed that the RA occurs the most during the increasing phase for the AC and during the minimum phase for the NC. The maximum gap of occurrence () between both classifications is -11.1% (for the negative value which is observed during the increasing phase) and +16.74% (for the positive one which is observed during the decreasing phase). The occurrence of the SA in relation with both classifications is the lowest during the minimum phase and the maximum occurrence is observed during the maximum and decreasing phases, for the AC, with a value close to 37% and for the NC at the maximum phase with a percentage of 54.47%. The maximum gap of occurrence () between both classifications is -17.85% (for the negative value which is observed at maximum phase) and +13.53% (for the positive one which is observed during the decreasing phase). For both classifications, the FA occurs the least during the minimum phase and the most during the maximum phase for the AC and at maximum and decreasing phases with percentage values of occurrence of roughly 37% for the NC. The maximum gap of occurrence () between both classifications is -10% (for the negative value which is observed during the decreasing phase) and +20.11% (for the positive one which is observed during the maximum phase). foF2 diurnal profiles throughout solar cycle phases concerning the AC and the NC have been compared. The FA diurnal profiles don’t present a difference. The RA and the SA present a difference during minimum and increasing phases and the least at maximum and decreasing phases.

Fast detection of the early decay in oranges using visible-LED structuredillumination imaging combined with spiral phase transform and feature-based classification model

The early decay of citrus can cause economic and serious food safety issues.The early decayed area has no obvious visual characteristics,making effective detection of this damage very difficult for the citrus industry.This study constructed a new detection system based on visible-light emitting diode(LED)structured-illumination imaging and proposed an effective methodology combined with a spiral phase transform(SPT)algorithm for the early detection of decayed oranges.Each sample obtained three phase-shifting pattern images with phase shifts of−2π/3,0,and 2π/3 at a spatial frequency of 0.25 cycles/mm.Three strategies(i.e.,the conventional three-phase-shifting method,2-phase SPT,and 1-phase SPT)were used to demodulate the original patterned images to recover the direct component(DC)and amplitude component(AC)images.The partial least squares discriminant analysis(PLS-DA)and least squares support vector machine(LS-SVM)classification models were established based on the texture features of DC,AC,and RT(i.e.the ratio of AC to DC)images.Then,the random frog(RF)algorithm was used to simplify the optimal full-featured model.Finally,the LS-SVM model constructed using 7 texture features from the RT image obtained an average classification accuracy of 95.1%for all tested samples.This study indicates that the proposed structured-illumination imaging technique combined with 2-phase SPT and feature-based classification model can achieve the fast identification of early decayed oranges.

Structure and contents of layered classification system of digital geomorphology for China

This paper presents the structure and contents of a standardized layered classification system of digital geomorphology for China.This digital classification method combines landforms characteristics of morphology with genesis.A total of 15 categories of exogenic and endogenic forces are divided into two broad categories：morpho-genetic and morpho-structural landforms.Polygon patches are used to manage the morpho-genetic types,and solitary points,lines and polygons are used to manage the morpho-structural types.The classification method of digital morpho-genetic types can be divided into seven layers,i.e.basic morphology and altitude,genesis,sub-genesis,morphology,micro-morphology,slope and aspect,material and lithology.The method proposes combinations of matrix forms based on layered indicators.The attributes of every landform types are obtained from all or some of the seven layers.For the 15 forces categories,some classification indicators and calculation methods are presented for the basic morphology,the morphologic and sub-morphologic landforms of the morpho-genetic types.The solitary polygon,linear and point types of morpho-structural landforms are presented respectively.The layered classification method can meet the demands of scale-span geomorphologic mapping for the national primary scales from 1：500,000 to 1：1,000,000.The layers serve as classification indicators,and therefore can be added and reduced according to mapping demands,providing flexible expandability.

Refined land-cover classification algorithm in airborne POLSAR system

在机载高分辨率X波段雷达影像上,城市水体、水泥道路、裸露土壤等地物回波信号弱,具有较强的相似性。目前的H/Alpha-Wishart、Freeman-Durden等系列算法对这些地物的区分不理想。本文针对X波段的全极化数据,提出基于预分类再分割的方法,重新估计极化协方差矩阵;然后根据协方差矩阵的熵值与同极化通道相位差的标准差构建特征空间,利用阈值法方法进行特征空间分割,获取最终精细分类结果。实验证明,本文方法可以在高分辨率条件下对水体、水泥道路、裸露土壤进行较为精确的划分,总体分类精度优于80%、Kappa系数高于0.7,是一种有效的低后向散射地物精细分类方法。

Efficient phase-induced gabor cube selection and weighted fusion for hyperspectral image classification

Spectral-spatial Gabor filtering(GF),a robust feature extraction tool,has been widely investigated for hyperspectral image(HSI)classification.Recently,a new type of GF method,named phase-induced GF,which showed great potential for HSI feature extraction,was proposed.Although this new type of GF possibly better explores the frequency characteristics of HSIs,with a new parameter added,it generates a much larger amount of features,yielding redundancies and noises,and is therefore risky to severely deteriorate the efficiency and accuracy of classification.To tackle this problem,we fully exploit phase-induced Gabor features efficiently,proposing an efficient phase-induced Gabor cube selection and weighted fusion(EPCS-WF)method for HSI classification.Specifically,to eliminate the redundancies and noises,we first select the most representative Gabor cubes using a newly designed energy-based phase-induced Gabor cube selection(EPCS)algorithm before feeding them into classifiers.Then,a weighted fusion(WF)strategy is adopted to integrate the mutual information residing in different feature cubes to generate the final predictions.Our experimental results obtained on four well-known HSI datasets demonstrate that the EPCS-WF method,while only adopting four selected Gabor cubes for classification,delivers better performance as compared with other Gabor-based methods.The code of this work is available at https://github.com/cairlin5/EPCS-WF-hyperspectral-image-classification for the sake of reproducibility.

A dynamic two-zone model of continuous fluidized bed layering granulation with internal product classification

A dynamic two-zone model is proposed to address the formation of granulation and drying zones in fluidized bed layering granulation processes with internal product classification. The model assumes a constant volume for the granulation zone, but a variable overall volume for the fluidized bed to account for classified product removal. The model is used to study the effect of various process parameters on dynamics and process stability. Stability is shown to depend on the separation diameter of product removal and the flow rate of the injected liquid. A lower and upper range of separation diameters with stable process behavior are found. In an intermediate range instability in the form of self-sustained oscillations is observed. The lower stability boundary is in qualitative agreement with recent experimental observations （Schmidt, Bück, ＆ Tsotsas, 2015）; the upper boundary was reported in a theoretical paper by Vreman, Van Lare, and Hounslow （2009） based on a single zone model.

A novel interpretable multilevel wavelet decomposition deep network for actual heartbeat classification

Arrhythmias may lead to sudden cardiac death if not detected and treated in time.A supraventricular premature beat(SPB)and premature ventricular contraction(PVC)are important categories of arrhythmia disease.Recently,deep learning methods have been applied to the PVC/SPB heartbeats detection.However,most researchers have focused on time-domain information of the electrocardiogram and there has been a lack of exploration of the interpretability of the model.In this study,we design an interpretable and accurate PVC/SPB recognition algorithm,called the interpretable multilevel wavelet decomposition deep network(IMWDDN).Wavelet decomposition is introduced into the deep network and the squeeze and excitation(SE)-Residual block is designed for extracting time-domain and frequency-domain features.Additionally,inspired by the idea of residual learning,we construct a novel loss function for the constant updating of the multilevel wavelet decomposition parameters.Finally,the IMWDDN is evaluated on the Third China Physiological Signal Challenge Dataset and the MIT-BIH Arrhythmia database.The comparison results show IMWDDN has better detection performance with 98.51%accuracy and a 93.75%F1-macro on average,and its areas of concern are similar to those of an expert diagnosis to a certain extent.Generally,the IMWDDN has good application value in the clinical screening of PVC/SPB heartbeats.

The effect of Laves phases and nano-precipitates on the electrochemical corrosion resistance of Mg-Al-Ca alloys under alkaline conditions

The electrochemical corrosion mechanisms of Mg alloys were extensively studied in previous investigations of different chemical com-positions,modified surface states and various electrolyte conditions.However,recent research focused on the active state of Mg dissolution,leading to unresolved effects of secondary phases adjacent to a stableα-solid solution passive layer.The present study investigates the fundamental electrochemical corrosion mechanisms of three different Laves phases with varying phase morphologies and phase fractions in the passive state of Mg-Al-Ca alloys.The microstructure was characterized by(transmission-)electron microscopy and synchrotron-based transmission X-ray microscopy.The electrochemical corrosion resistance was determined with a standard three-electrode setup and advanced in-situ flow cell measurements.A new electrochemical activity sequence(C15>C36>α-Mg>C14)was obtained,as a result of a stable passive layer formation on theα-solid solution.Furthermore,nm-scale Mg-rich precipitates were identified within the Laves phases,which tend to inhibit the corrosion kinetics.