Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

LOCALIZED RADON-WIGNER TRANSFORM AND GENERALIZED-MARGINAL TIME-FREQUENCY DISTRIBUTIONS

This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the properties of LRWT and its relationship with Radon-Wigner transform, Wigner distribution (WD), ambiguity function (AF), and generalized-marginal time-frequency distributions are analyzed.

A NEW QUADRATIC TIME-FREQUENCY DISTRIBUTIONAND A COMPARATIVE STUDY OF SEVERAL POPULARQUADRATIC TIME-FREQUENCY DISTRIBUTIONS

A new quadratic time-frequency distribution (TFD) with a compound kernel is proposed and a comparative study of several popular quadratic TFD is carried out. It is shown that the new TFD with compound kernel has stronger ability than the exponential distribution (ED) and the cone-shaped kernel distribution (CKD) in reducing cross terms, meanwhile almost not decreasing the time-frequency resolution of ED or CKD.

Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.

MATHEMATICAL ANALYSIS OF DRUG RELEASE FROM A MONOLITHIC MATRIX WITH AN INITIALLY LINEAR CONCENTRATION DISTRIBUTION

将化学工程学原理应用到药物释放这一多学科领域，研究了初始浓度分布为线性的平板混合药膜体系释放动力学模型，该模型可用来描述高装填量平板混合药膜的释放后期，并可简化为无限渗阱时的特定情况．根据该模型，计算了混合药膜内的浓度－距离分布和浓度－时间分布曲线，研究了释放介质体积对释放过程的影响．

Thermal residual stresses and stress distributions under tensile and compressive loadings of short fiber reinforced metal matrix composites

The thermal residual stresses and the stress distributions of short fiber reinforced metal matrix composite under tensile and compressive loadings were studied using large strain axisymmetric elasto plastic finite element method. It is demonstrated that the thermal residual stresses can result in asymmetrical stress distributions and matrix plasticity. The thermal residual stresses decrease the stress transfer in tension and enhance the stress transfer in compression. The fiber volume fraction has more important effects on the thermal residual stresses and the stress distributions under tensile and compressive loadings than the fiber aspect ratio and the fiber end distance. [

Distribution and relative activity of matrix metalloproteinase-2 in human coronal dentin

The presence of matrix metalloproteinase-2 （MMP-2） in dentin has been reported, but its distribution and activity level in mature human coronal dentin are not well understood. The purpose of this study was to determine the MMP-2 distribution and relative activity in demineralized dentin. Crowns of twenty eight human molars were sectioned into inner （ID）, middle （MD）, and outer dentin （OD） regions and demineralized. MMP-2 was extracted with 0.33 mol·L-1 EDTA/2 mol·L-1 guanidine-HCl, pH 7.4, and MMP-2 concentration was estimated with enzyme-linked immunoabsorbant assay （ELISA）. Further characterization was accomplished by Western blotting analysis and gelatin zymography. The mean concentrations of MMP-2 per mg dentin protein in the dentin regions were significantly different （P=0.043）： 0.9 ng （ID）, 0.4 ng （MD）, and 2.2 ng （OD）, respectively. The pattern of MMP-2 concentration was OD〉ID〉MD. Western blotting analysis detected -66 and -72 kDa immunopositive proteins corresponding to pro- and mature MMP-2, respectively, in the ID and MD, and a -66 kDa protein in the OD. Gelatinolytic activity consistent with MMP-2 was detected in all regions. Interestingly, the pattern of levels of Western blot immunodetection and gelatinolytic activity was MD〉ID〉OD. The eoneentration of MMP-2 in human coronal dentin was highest in the region of dentin that contains the dentinoenamel junction and least in the middle region of dentin. However, levels of Western blot immunodetection and gelatinolytic activity did not correlate with the estimated regional concentrations of MMP-2, potentially indicating region specific protein interactions.

Multi-Model Based PSO Method for Burden Distribution Matrix Optimization With Expected Burden Distribution Output Behaviors

Burden distribution is one of the most important operations, and also an important upper regulation in blast furnace(BF) iron-making process. Burden distribution output behaviors(BDOB) at the throat of BF is a 3-dimensional spatial distribution produced by burden distribution matrix(BDM),including burden surface output shape(BSOS) and material layer initial thickness distribution(MLITD). Due to the lack of effective model to describe the complex input-output relations,BDM optimization and adjustment is carried out by experienced foremen. Focusing on this practical challenge, this work studies complex burden distribution input-output relations, and gives a description of expected MLITD under specific integral constraint on the basis of engineering practice. Furthermore, according to the decision variables in different number fields, this work studies optimization of BDM with expected MLITD, and proposes a multi-mode based particle swarm optimization(PSO) procedure for optimization of decision variables. Finally, experiments using industrial data show that the proposed model is effective, and optimized BDM calculated by this multi-model based PSO method can be used for expected distribution tracking.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Non-random tissue distribution of human na ve umbilical cord matrix stem cells

AIM: To determine the tissue and temporal distribution of human umbilical cord matrix stem (hUCMS) cells in severe combined immunodeficiency (SCID) mice. METHODS: For studying the localization of hUCMS cells, tritiated thymidine-labeled hUCMS cells were injected in SCID mice and tissue distribution was quantitatively determined using a liquid scintillation counter at days 1, 3, 7 and 14. Furthermore, an immunofluorescence detection technique was employed in which anti-human mitochondrial antibody was used to identify hUCMS cells in mouse tissues. In order to visualize the distribution of transplanted hUCMS cells in H&E stained tissue sections, India Black ink 4415 was used to label the hUCMS cells. RESULTS: When tritiated thymidine-labeled hUCMS cells were injected systemically (iv) in female SCID mice, the lung was the major site of accumulation at 24 h after transplantation. With time, the cells migrated to other tissues, and on day three, the spleen, stomach, and small and large intestines were the major accumulation sites. On day seven, a relatively large amount of radioactivity was detected in the adrenal gland, uterus, spleen, lung, and digestive tract. In addition, labeled cells had crossed the blood brain barrier by day 1. CONCLUSION: These results indicate that peripherally injected hUCMS cells distribute quantitatively in a tissue-specific manner throughout the body.

Measuring the flexibility matrix of an eagle’s flight feather and a method to estimate the stiffness distribution

Flight feathers stand out with extraordinary mechanical properties for flight because they are lightweight but stiff enough.Their elasticity has great effects on the aerodynamics, resulting in aeroelasticity.Our primary task is to figure out the stiffness distribution of the feather to study the aeroelastic effects.The feather shaft is simplified as a beam, and the flexibility matrix of an eagle flight feather is tested.A numerical method is proposed to estimate the stiffness distributions along the shaft length based on an optimal Broyden–Fletcher–Goldfarb–Shanno(BFGS) method with global convergence.An analysis of the compressive behavior of the shaft based on the beam model shows a good fit with experimental results.The stiffness distribution of the shaft is finally presented using a 5 th order polynomial.

A Direct Derivation of the Exact Fisher Information Matrix for Bivariate Bessel Distribution of Type I

This paper deals with a direct derivation of Fisher’s information matrix for bivariate Bessel distribution of type I. Some tools for the numerical computation and some tabulations of the Fisher’s information matrix are provided.

UHAS-MIDA Software Package: Mass Isotopomer Distribution Analysis-Applying the Inverse Matrix for the Determination of Stable Isotopes of Chromium Distribution during Red Blood Cell Labelling

Clinical assessment of fluid volume status in children during malaria can be taxing and often inaccurate. During malaria, changes in fluid volume are rather multifarious and estimating this parameter, especially in sick children is very challenging for clinicians who frequently rely on indices such as long capillary refill times, tachycardia, central venous pressure and decreased urine volume as guides. Here, we present the UHAS-MIDA, an open-source software tool that calculates the red blood cell (RBC) concentration and blood volume during malaria in children determined using a stable isotope of chromium (53Cr as the label) by gas chromatography-mass spectrometry in selective ion monitoring (GC/MS-SIM) analysis. A key component involves the determination of the compositions of the most abundant naturally occurring isotopes of Cr (50Cr, 52Cr, 53Cr), and converting the proportions into a 3 × 3 matrix. To estimate unknown proportions of chromium isotopic mixtures from the measured abundances of three ions, an inverse matrix was calculated. The inverse together with several inputs is then used to calculate the corrected MS ion abundances. Thus, we constructed the software tool UHAS- MIDA using HTML, CSS/Bootstrap, JavaScript, and PHP scripting languages. The tool enables the user to efficiently determine RBC concentration and fluid volume. The source code, binary packages and associated materials for UHAS-MIDA are freely available at https://github.com/bentil078/Abaye-et-al_UHASmida

Thermal conductivity of diamond/copper composites with a bimodal distribution of diamond particle sizes prepared by pressure infiltration method

The thermal conductivity of diamond/copper composites with bimodal particle sizes was studied. The composites were prepared through pressure infiltration of liquid copper into diamond preforms with a mixture of 40 and 100 pm-size diamonds. The permeability of the preforms with different coarse-to-fine volume ratios of diamonds was investigated. The thermal conductivity of the diamond/copper composites with bimodal size distribution was compared to the theoretical value derived from an analytical model developed by Chu. It is predicted that the diamond/copper composites could reach a higher thermal conductivity and their surface roughness could be improved by applying bimodal diamond particle sizes.

Parametric adaptive time-frequency representation based on time-sheared Gabor atoms

A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization of Gabor atom and is more delicate for matching most of the signals encountered in practice, especially for those having frequency dispersion characteristics. The time-frequency distribution of this atom concentrates in its time center and frequency center along energy curve, with the curve being oblique to a certain extent along the time axis. A novel parametric adaptive time-frequency distribution based on a set of the derived atoms is then proposed using a adaptive signal subspace decomposition method in frequency domain, which is non-negative time-frequency energy distribution and free of cross-term interference for multicomponent signals. The results of numerical simulation manifest the effectiveness of the approach in time-frequency representation and signal de-noising processing.

Effect of tool rotational speed on the particle distribution in friction stir welding of AA6092/17.5 SiCp-T6 composite plates and its consequences on the mechanical property of the joint

This study investigates the effect of tool rotational speed(TRS)on particle distribution in nugget zone(NZ)through quantitative approach and its consequences on the mechanical property of friction stir welded joints of AA6092/17.5 SiCp-T6 composite.6 mm thick plates are welded at a constant tool tilt angle of 2°and tool traverse speed of 1 mm/s by varying the TRS at 1000 rpm,1500 rpm and 2000 rpm with a taper pin profiled tool.Microstructure analysis shows large quantity of uniformly shaped smaller size SiC particle with lower average particle area which are homogeneously distributed in the NZ.The fragmentation of bigger size particles has been observed because of abrading action of the hard tool and resulting shearing effect and severe stress generation due to the rotation of tool.The particles occupy maximum area in the matrix compared to that of the base material(BM)due to the redistribution of broken particles as an effect of TRS.The migration of particles towards the TMAZ-NZ transition zone has been also encountered at higher TRS(2000 rpm).The microhardness analysis depicts variation in average hardness from top to bottom of the NZ,minimum for 1500 rpm and maximum for 2000 rpm.The impact strength at 1000 rpm and 1500 rpm remains close to that of BM(21.6 J)while 2000 rpm shows the accountable reduction.The maximum joint efficiency has been achieved at 1500 rpm(84%)and minimum at 1000 rpm(68%)under tensile loading.Fractographic analysis shows mixed mode of failure for BM,1000 rpm and 1500 rpm,whereas 2000 rpm shows the brittle mode of failure.

Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis

Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.

On Graphs with Same Distance Distribution

In the present paper we investigate the relationship between Wiener number W, hyper-Wiener number R, Wiener vectors WV, hyper-Wiener vectors HWV, Wiener polynomial H, hyper-Wiener polynomial HH and distance distribution DD of a (molecular) graph. It is shown that for connected graphs G and G*, the following five statements are equivalent:?;and if G and G* have same distance distribution DD then they have same W and R but the contrary is not true. Therefore, we further investigate the graphs with same distance distribution. Some construction methods for finding graphs with same distance distribution are given.

Distributions of Ratios: From Random Variables to Random Matrices

The ratio R of two random quantities is frequently encountered in probability and statistics. But while for unidimensional statistical variables the distribution of R can be computed relatively easily, for symmetric positive definite random matrices, this ratio can take various forms and its distribution, and even its definition, can offer many challenges. However, for the distribution of its determinant, Meijer G-function often provides an effective analytic and computational tool, applicable at any division level, because of its reproductive property.

Data-Driven Control of Distributed Event-Triggered Network Systems

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional approach.Marrying the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is established.Meanwhile,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data.Finally,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.