Stability analysis of a liquid crystal elastomer self-oscillator under a linear temperature field

Self-oscillating systems abound in the natural world and offer substantial potential for applications in controllers,micro-motors,medical equipments,and so on.Currently,numerical methods have been widely utilized for obtaining the characteristics of self-oscillation including amplitude and frequency.However,numerical methods are burdened by intricate computations and limited precision,hindering comprehensive investigations into self-oscillating systems.In this paper,the stability of a liquid crystal elastomer fiber self-oscillating system under a linear temperature field is studied,and analytical solutions for the amplitude and frequency are determined.Initially,we establish the governing equations of self-oscillation,elucidate two motion regimes,and reveal the underlying mechanism.Subsequently,we conduct a stability analysis and employ a multi-scale method to obtain the analytical solutions for the amplitude and frequency.The results show agreement between the multi-scale and numerical methods.This research contributes to the examination of diverse self-oscillating systems and advances the theoretical analysis of self-oscillating systems rooted in active materials.

Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,China

In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.

Linear analysis of the dynamic response of a riser subject to internal solitary waves

The flow field induced by internal solitary waves(ISWs)is peculiar wherein water motion occurs in the whole water depth,and the strong shear near the pycnocline can be generated due to the opposite flow direction between the upper and lower layers,which is a potential threat to marine risers.In this paper,the flow field of ISWs is obtained with the Korteweg-de Vries(Kd V)equation for a two-layer fluid system.Then,a linear analysis is performed for the dynamic response of a riser with its two ends simply supported under the action of ISWs.The explicit expressions of the deflection and the moment of the riser are deduced based on the modal superposition method.The applicable conditions of the theoretical expressions are discussed.Through comparisons with the finite element simulations for nonlinear dynamic responses,it is proved that the theoretical expressions can roughly reveal the nonlinear dynamic response of risers under ISWs when the approximation for the linear analysis is relaxed to some extent.

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this paper.We have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia detection.Several ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected dataset.The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models.According to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.

Two-dimensional face stability analysis in rock masses governed by the Hoek-Brown strength criterion with a new multi-horn mechanism

The face stability problem is a major concern for tunnels excavated in rock masses governed by the Hoek-Brown strength criterion.To provide an accurate prediction for the theoretical solution of the critical face pressure,this study adopts the piecewise linear method(PLM)to account for the nonlinearity of the strength envelope and proposes a new multi-horn rotational mechanism based on the Hoek-Brown strength criterion and the associative flow rule.The analytical solution of critical support pressure is derived from the energy-work balance equation in the framework of the plastic limit theorem;it is formulated as a multivariable nonlinear optimization problem relying on 2m dependent variables(m is the number of segments).Meanwhile,two classic linearized measures,the generalized tangential technique(GTT)and equivalent Mohr-Coulomb parameters method(EMM),are incorporated into the analysis for comparison.Surprisingly,the parametric study indicates a significant improvement in support pressure by up to 13%compared with the GTT,and as expected,the stability of the tunnel face is greatly influenced by the rock strength parameters.The stress distribution on the rupture surface is calculated to gain an intuitive understanding of the failure at the limit state.Although the limit analysis is incapable of calculating the true stress distribution in rock masses,a rough approximation of the stress vector on the rupture surface is permitted.In the end,sets of normalized face pressure are provided in the form of charts for a quick assessment of face stability in rock masses.

Isolated Area Load Forecasting using Linear Regression Analysis: Practical Approach

This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through linear regression and based on the identification of factors on which electrical load growth depends. To determine the identification factors, areas are selected whose histories of load growth rate known and the load growth deciding factors are similar to those of the isolated area. The proposed analysis is applied to an isolated area of Bangladesh, called Swandip where a past history of electrical load demand is not available and also there is no possibility of connecting the area with the main land grid system.

μ ANALYSIS AND μ SYNTHESIS FOR NONLINEAR ROBUST CONTROL SYSTEMS

A μ analysis and μ synthesis method for nonlinear robust control systems was presented. The nonlinear robust contrl problem using μ method was described. By means of the nonlinear state feedback and state coordinates transformation, many uncertain nonlinear systems can be transformed as a linear fractional transformation (LFT) on the generalized plant and the uncertainty. Based on the LFT, a linear robust controller can be obtained by the DK iteration and then a corresponding nonlinear robust control law is constructed. An example was given in the paper.

Direct linear discriminant analysis based on column pivoting QR decomposition and economic SVD

A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.

Modeling and Experiment on Contact Stiffness and Accuracy Analysis of Ball Linear Guide Feed Unit

The contact stiffness and the error analysis have an important effect on the manufacture and the optimization of Ball Linear Guide Feed Unit( BLGFU). In order to analyze the contact stiffness and linear errors or angle errors of BLGFU,in this paper,the contact stress and deformation mechanics between the ball and rail is analyzed. Based on Hertz theory of contact and theory of the multi-body system,a model of the contact stiffness considering the changes in contact angle is established. With the increasing of the external load,the varying trend of the contact deformation can be obtained. Therefore, the motion accuracy degradation of the BLGFU can be analyzed. By using a special experimental device and test system of the rolling linear guide worktable,the horizontal contact stiffness and the vertical linear stiffness are obtained,respectively. By comparing the contact stiffness of the experiment dates and the simulation results,the variation tendency of two curves is consisted and the difference between the measured values and the theoretical values is less than 18%. It is obvious that the model of the contact stiffness considering changes of contact angle has accuracy and feasibility. Thus,while external force point locating at different positions; the contact stiffness and the accuracy analysis of the BLGFU are proved validity by simulations.

Initial Pre-stress Finding and Structural Behaviors Analysis of Cable Net Based on Linear Adjustment Theory

The tensile cable-strut structure is a self-equilibrate pre-stressed system.The initial pre-stress cal- culation is the fundamental structural analysis.A new numerical procedure was developed.The force density method is the cornerstone of analytical formula,and then introduced into linear adjustment theory;the least square least norm solution,the optimized initial pre-stress,is yielded.The initial pre-stress and structural performances of a particular single-layer saddle-shaped cable-net structure were analyzed with the developed method,which is proved to be efficient and correct.The modal analyses were performed with respect to various pre-stress levels.Finally,the structural performances were investigated comprehensively.

Flow pattern analysis of linear gradient flow distribution

This paper uses the Oseen transformation to solve the differential equations governing motion of the vertical linear gradient flow distribution close to a wall surface. The Navier-Stokes equations are used to consider the inertia term along the flow direction. A novel contour integral method is used to solve the complex Airy function. The boundary conditions of linear gradient flow distribution for finite problems are determined. The vorticity function, the pressure function, and the turbulent velocity profiles are provided, and the stability of particle trajectories is studied. An Lx-function form of the third derivative circulation is used to to simplify the solution. Theoretical results are compared with the experimental measurements with satisfactory agreement.

Pattern analysis of a linear dune field on the northern margin of Qarhan Salt Lake,northwestern China

In terms of formation mechanisms of linear dunes,there are open arguments for their widespread distribution and multi-morphological diversities.In order to clarify the formation mechanism of linear dunes of Qarhan Salt Lake,we used pattern analysis method to analyze the statistical characteristics and spatial variation of their pattern parameters.Except at the west-northwest margin,the pattern parameters showed regular spatial variation from the up-middle part towards the downwind end of the dune field.Based on the cumulative probability plots for inter-crest spacing and crest length,we divided the linear dunes into three groups,which corresponding to the three evolution stages of these dunes.The first group comprises erosional relics,with shorter crests,smaller inter-crest spacing and more random dune orientation.The second group comprises dunes whose sand supply is just sufficient to maintain stability and these dunes are approaching the net erosion stage.The crest length and inter-crest spacing of these dunes are much larger than those of the first group,and dune orientation is closer to the resultant drift direction （RDD） .The last group comprises linear dunes that are still undergoing vertical accretion and longitudinal elongation,which follows the RDD of the modern wind regime.The presence of regular spatial variation of pattern parameters and a similar geometry with the vegetated linear dunes suggest that deposition and erosion coexist in the development and evolution of linear dunes of Qarhan Salt Lake,i.e.deposition predominates at the downwind end of linear dunes in the vertical accretion and longitudinal elongation stage,whereas erosion mainly occurs at the upwind end of linear dunes in the degradation stage.Therefore,the formation mechanism of linear dunes in Qarhan Salt Lake can be reasonably explained by the combination of depositional and erosional theories.

Magnetic field and performance analysis of a tubular permanent magnet linear synchronous motor applied in elevator door system

A novel elevator door driven by tubular permanent magnet linear synchronous motor (TPMLSM) is presented. This TPMLSM applies axial magnet array topology of the secondary rod, air-cored armature windings and slotless structure of the forcer to improve the stability of the thrust. The influence of two major dimensions, the pitch and radius of the permanent magnet (PM), on magnetic field was studied and the best values were given by the finite element analysis (FEA). The magnetic field, back EMF and thrust of the motor were analyzed and the PM size was optimized to reduce the harmonic components of the magnetic field and improve the performance of the motor. Predicted results are validated by the experiment. It is shown that the performance of the motor and the novel elevator door system is satisfying.

A New Extended BIC and Sequential Lasso Regression Analysis and Their Application in Classification

In this paper, firstly, we propose a new method for choosing regularization parameter λ for lasso regression, which differs from traditional method such as multifold cross-validation, our new method gives the maximum value of parameter λ directly. Secondly, by considering another prior form over model space in the Bayes approach, we propose a new extended Bayes information criterion family, and under some mild condition, our new EBIC (NEBIC) is shown to be consistent. Then we apply our new method to choose parameter for sequential lasso regression which selects features by sequentially solving partially penalized least squares problems where the features selected in earlier steps are not penalized in the subsequent steps. Then sequential lasso uses NEBIC as the stopping rule. Finally, we apply our algorithm to identify the nonzero entries of precision matrix for high-dimensional linear discrimination analysis. Simulation results demonstrate that our algorithm has a lower misclassification rate and less computation time than its competing methods under considerations.

Expansion of Linear Analysis of Rayleigh-Taylor Interface Instability of Metal Materials

The linear analysis of the Rayleigh-Taylor instability in metal material is extended from the perfect plastic constitutive model to the Johnson-Cook and Steinberg-Guinan constitutive model, and from the constant loading to a time-dependent loading. The analysis is applied to two Rayleigh-Taylor instability experiments in aluminum and vanadium with peak pressures of 20 GPa and 90 GPa, and strain rates of 6 × 106 s−1 and 3 × 107 s−1 respectively. When the time-dependent loading and the Steinberg-Guinan constitutive model are used in the linear analysis, the analytic results are in close agreement with experiments quantitatively, which indicates that the method in this paper is applicable to the Rayleigh-Taylor instability in aluminum and vanadium metal materials under high pressure and high strain rate. From these linear analyses, we find that the constitutive models and the loading process are of crucial importance in the linear analysis of the Rayleigh-Taylor instability in metal material, and a better understanding of the Rayleigh-Taylor instability in metals is gained. These results will serve as important references for evolving high-pressure, high-strain-rate experiments and numerical simulations.

Finite Element Analysis and Linear Regression of Maximum Temperature for Inner Wall of Chimney Foundation

The uniform design method was adopted and the twenty-four groups of different geometric and physical pa-rameters were chosen. The finite element model was built. Comparisons between the simulation results and the test re-sults prove that the simulation results are correct. The distribution of the temperature field of the chimney foundationwas analyzed. The multivariate linear regression of the hightest tomperature was performed on the inner wall of thechimney foundation by the numerical calculated results. The fitting property of the highest temperature with six influ-ence factors was obtained. A simple method for the calculation of the temperature field of the chimney foundation wasprovided.

Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

Kernel Model Applied in Kernel Direct Discriminant Analysis for the Recognition of Face with Nonlinear Variations

A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.

Experimental and Simulation Analysis of Two-Tone and Three-Tone Photodetector Linearity Characterizing Systems

Two measurement techniques are investigated to characterize photodetector linearity. A model for the two-tone and three-tone photodetector systems is developed to thoroughly investigate the influences of setup components on the measurement results. We demonstrate that small bias shifts from the quadrature point of the modulator will induce deviation into measurement results of the two-tone system, and the simulation results correspond well to experimental and calculation results.

Image Processing System for Air Classification Using Linear Discriminant Analysis

An air classifier is used in the recycling process of covered electric wire in the recycling factories, in which the covered electric wires are crushed, sieved, and classified by the air classifier, which generates wastes. In these factories, operators manually adjust the air flow rate while checking the wastes discharged from the separator outlet. However, the adjustments are basically done by trial and error, and it is difficult to do them appropriately. In this study, we tried to develop the image processing system that calculates the ratio of copper (Cu) product and polyvinyl chloride (PVC) in the wastes as a substitute for the operator’s eyes. Six colors of PVC (white, gray, green, blue, black, and red) were used in the present work. An image consists of foreground and background. An image’s regions of interest are objects (Cu particles) in its foreground. However, the particles having a color similar to the background color are buried in the background. Using the difference of two color backgrounds, we separated particles and background without dependent of background. The Otsu’ thresholding was employed to choose the threshold to maximize the degree of separation of the particles and background. The ratio of Cu to PVC pixels from mixed image was calculated by linear discriminant analysis. The error of PVC pixels resulted in zero, whereas the error of Cu pixels arose to 4.19%. Comparing the numbers of Cu and PVC pixels within the contour, the minority of the object were corrected to the majority of the object. The error of Cu pixels discriminated as PVC incorrectly became zero percent through this correction.