Eigenvalue Extraction Research Articles

Early age time-dependent mechanical properties of 3D-printed concrete with coarse aggregates

Incorporating coarse aggregate into 3D-printed concrete is essential for promoting its practical application, yet the impact on the early age time-dependent mechanical properties, crucial for the multilayer structure stability and quality, is underexplored. In this study, 3D-printed concrete with coarse aggregate (3DPCA) with varying mortar-to-coarse aggregate ratios (M/A) and coarse aggregate gradations were prepared, of which uniaxial unconfined compression and direct shear performance were experimentally investigated. The fundamental formulations between the early age mechanical parameters and the resting time of 3DPCA were characterized and then utilized in a digital model simulating actual printing. Results indicated that the 3DPCA early age mechanical properties, including compressive strength, Young's modulus, cohesion, and friction angle, increased with longer resting time and decreasing M/A, and higher proportions of aggregates above 9.5 mm. High coarse aggregate content caused three distinct compressive strength-displacement curve stages and complicated strength eigenvalue extraction. Coarse aggregate gradation affected shear properties more than content, with larger aggregates improving cohesion and friction angles. The failure mode prediction model based on ABAQUS could accurately predict the maximum collapse layer of 3DPCA that occurred in overall unstable collapse and over-expansion failure but was incapable of localized deformation.

A study on hyperspectral soil total nitrogen inversion using a hybrid deep learning model CBiResNet-BiLSTM

Rapid, accurate and non-destructive acquisition of soil total nitrogen (TN) content in the black soil zone is significant for achieving precise fertilization. In this study, the soil types of corn and soybean fields in Jilin Agricultural University, China, were selected as the study area. A total of 162 soil samples were collected using a five-point mixed sampling method. Then, spectral data were obtained and the noisy edge were initially eliminated. Subsequently, the denoised spectral data underwent smoothing by using the Savitzky–Golay (SG) method. After performing the first-order difference (FD) and second-order difference (SD) transformations on the data, it was input to the model. In this study, a hybrid deep learning model, CBiResNet-BiLSTM, was designed for precise prediction of soil TN content. This model was optimized based on ResNet34, and its capabilities were enhanced by incorporating CBAM in the residual module to facilitate additional eigenvalue extraction. Also, Bidirectional Long Short-Term Memory (BiLSTM) was integrated to enhance model accuracy. Besides, partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and back propagation neural network (BP), as well as ResNet(18, 34, 50, 101, 152) models were taken for comparative experiments. The results indicated that the traditional machine learning model PLSR achieved good performance, with R2 of 0.883, and the hybrid deep learning model CBiResNet-BiLSTM had the best inversion capability with R2 of 0.937, with the R2 being improved by 5.4%, compared with the PLSR model. On this basis, we present the LUCAS dataset to demonstrate the generalisability of the model. Therefore, the CBiResNet-BiLSTM model is a fast and feasible hyperspectral estimation method for soil TN content.Graphical abstract

Numerical Prediction and Experimental Verification of Inherent Defect Influences on Frequencies of Adhesively Bonded Joint

The vibrational response of adhesively bonded joints with and without defects in the adhesive layer is predicted by simulation for the different overlapping lengths in this work. The eigenvalue extraction method (Block Lanczo’s) is employed within the finite element analysis (FEA) framework to predict the modal values of adhesively bonded joints. The numerical analysis has adopted a solid 20-node quadratic brick elements with reduced integration (C3D20R) to predict the realistic values. The predicted eigenvalues of adhesively bonded joints using an FEA solution are compared with published data and verified the accuracy of the FEA model through experimentation considering defective and non-defective joints. Additionally, simulation solutions are predicted for different overlap lengths (i.e., 25, 30, 35 and 40 mm) by changing the boundary conditions, adhesive bond thickness aspect ratio, adherend thickness ratio, defect position, and defect aspect ratio. The geometries of the adhesive and adherend significantly affect vibration responses through the dampening effect and structural weight, enhancing the stiffness. Defect position and geometry have negligible impact on the natural frequency values for all overlapping lengths except when located around the overlapping edges.

Fault diagnosis study of ship solar photovoltaic power generation system

In this paper, a fault diagnosis method of ship photovoltaic (PV) power generation system based on a convolutional neural network (CNN) is proposed to ensure the smooth navigation of ships. Firstly, to understand the normal operation and fault state of the generation system, a simulation model of the photovoltaic power generation system is constructed using MATLAB / Simulink. Then, the model is simulated to extract the fault data under different fault conditions, and the eigenvalue extraction of the fault data is carried out by using the fast Fourier transform method. Following that, a fault diagnosis model is built using the convolutional neural network and trained accordingly. The final diagnostic results show that the established simulation model and convolutional neural network can provide support for the fault diagnosis of ship photovoltaic power generation system, and can autonomously detect the type of faults and locate the location of the faults after the faults occur in the generation system, to ensure the stable and safe operation of ship power generation system.

Numerical investigation on the inhibition of porpoising instability for high-speed amphibious vehicles

ABSTRACT Porpoising instability may occur in high-speed amphibious vehicles with certain hull shapes and structural properties, resulting in speed limitations and severe safety risks. This study analysed the causes and inhibition of porpoising instability by theoretical and numerical methods. The pitch-heave-coupled model of amphibious vehicles was established based on the linear hydrodynamics assumption. After obtaining the hydrodynamic coefficients of forced pitching and heave, the Routh–Hurwitz criterion and eigenvalue extraction method were then employed to assess the stability of the vehicle’s longitudinal motion. The result of the linear stability analysis was compared with numerical simulation and experiments, showing good agreement. Research manifested that trim angle is a crucial factor affecting porpoising instability. Once the instability is mitigated, the resistance will also be decreased. This indicated the great significance of restraining porpoising instability for the comprehensive behaviour of high-speed amphibious vehicles.

MultiBEATS: Blocks of eigenvalues algorithm for multivariate time series dimensionality reduction

Multivariate Time Series are sequences of observations taken from multiple sources. The proliferation of environments in which data is collected by means of sensors and the adoption of data-based services reveals the importance of their efficient analysis. In smart environments, the analysis of the raw Multivariate Time Series is cumbersome. The algorithms are heavy, slow, and fail to extract all the knowledge available. In that sense, representation techniques reduce the data points but maintain the inner patterns so that the information present in data persists. There exist several approaches to represent Univariate Time Series, but for the multivariate case they are scarce. We have adapted the existent univariate representation algorithm BEATS, which is based on Discrete Cosine transformation and the extraction of eigenvalues to multivariate scenarios and we have called it MultiBEATS. Our method allows flexibility with regard to data reduction and does not require labelled data. To investigate the efficacy of MultiBEATS, we selected 8 open multivariate time series datasets and compared the running time and accuracy of their classification using raw data and MultiBEATS transformed data. We have also compared our results with the representation algorithm Seq2VAR, based on autoencoders, for having a baseline. The MultiBEATS transformation reduces the execution time of classification algorithms, has improved the results in accuracy in 2 of the datasets and matches a third one. With regards to the baseline, MultiBEATS is faster than Seq2VAR and more accurate in 7 out of the 8 datasets. In conclusion, MultiBEATS is a promising approach for efficiently reducing multivariate time series data, therefore helping decision-making in dynamic smart environments.

Construction of a linguistically interactive thesaurus for English second language acquisition based on an eigenvalue-fitting superiority algorithm

Abstract This paper completes the overall design of a linguistic interactive terminology database based on the characteristics of second language acquisition and terminology and completes the construction of the terminology database by combining a goodness-of-fit detection algorithm based on terminology eigenvalue extraction. The efficiency of terminology information recognition is analyzed and compared with the terminology conversion rate of the eigenvalue goodness-offit algorithm using a neural network learning model of long and short-term memory to optimize the performance of the terminology database. The metric approach's classifier performance evaluation metrics are used to compare the accuracy and recall of the two algorithms accurately. The results show that the accuracy of the fitted superiority classifier with the application of word eigenvalue embedding compared to the LSTM classifier for the classification of electric power terms is improved by about 11% in all categories, and the average accuracy of the classifier exceeds 76.5%.

Intelligent Diagnosis Model of Working Conditions in Variable Torque Pumping Unit Wells Based on an Electric Power Diagram

Because of the problems, such as the lack of an electric power diagram atlas under different working conditions and the difficulty in intelligent diagnosis of variable torqued pumping unit wells, this paper proposes a diagnosis model of working conditions based on feature recognition. The mathematical relationship model between the polished rod load and motor output power is derived based on the analysis of geometric structure, motion law, and process of energy transformation and transfer of the variable torque pumping unit. It can calculate the electric power diagram based on a dynamometer card. On this basis, the electric power diagram atlas is created, and the feature analysis and eigenvalue extraction of the electric power diagrams under different working conditions are carried out to realize the direct diagnosis of the working conditions in the variable torque pumping unit wells. The application and analysis of examples show that the electric power diagram atlas created in this paper has good practicability, and the working condition diagnosis model accuracy is high. It can provide a theoretical basis and technical support for the intelligent diagnosis of oil production working conditions and improve the intellectual management level of the oilfield, which is conducive to reducing production management costs and improving the oilfield’s production efficiency and benefits.

On the Numerical Treatment of the Temporal Discontinuity Arising from a Time-Varying Point Mass Attachment on a Waveguide

A vibrating pylon, modeled as a waveguide, with an attached point mass that is time-varying poses a numerically challenging problem regarding the most efficient way for eigenvalue extraction. The reason is three-fold, starting with a heavy mass attachment that modifies the original eigenvalue problem for the stand-alone pylon, plus the fact that the point attachment results in a Dirac delta function in the mixed-type boundary conditions, and finally the eigenvalue problem becomes time-dependent and must be solved for a sequence of time steps until the time interval of interests is covered. An additional complication is that the eigenvalues are now complex quantities. Following the formulation of the eigenvalue problem as a system of first-order, time-dependent matrix differential equations, two eigenvalue extraction methods are implemented and critically examined, namely the Laguerre and the QR algorithms. The aim of the analysis is to identify the most efficient technique for interpreting time signals registered at a given pylon as a means for detecting damage, a procedure which finds application in structural health monitoring of civil engineering infrastructure.

Comprehensive retrieval method of MOOC teaching resources based on eigenvalue extraction

Comprehensive retrieval method of MOOC teaching resources based on eigenvalue extraction

Comprehensive retrieval method of MOOC teaching resources based on eigenvalue extraction

Comprehensive retrieval method of MOOC teaching resources based on eigenvalue extraction

Comparison Between Bézier Extraction and Associated Bézier Elements in Eigenvalue Problems

This paper shows that the control points which are implicitly encountered in the Bézier extraction during isogeometric analysis can be explicitly used to form Bézier elements of C^0-continuity in several ways, thus eventually leading to a superior accuracy and performance than the C^p-continuity. The study is reduced to the eigenvalue extraction in problems governed by the Helmholtz equation. Analysis is performed in conjunction with piecewise cubic interpolation for three benchmark tests in one and two dimensions. In the latter case a rectangular and a circular acoustical cavity under Neumann boundary conditions are analyzed. Several computational details are also discussed.

Discussing the Construction of a Budget Management System Combining Multimedia Technology and Financial Risk Management

The development of modern technology allows enterprises to continuously expand the means of financial management. The use of advanced information technology and modern management means makes the financial management of enterprises continue to improve, but there are also many problems. In the face of various types of risks that enterprises may face at present, in addition to strengthening financial accounting, enterprises should also recognize the various risks that the double-edged sword of financial management informatization may bring to enterprise financial management and carefully analyze the reasons for the emergence of risks, from the ideological and technical means to continuously improve the entire process of financial management informatization, which is an important topic for every financial worker to discuss. This paper mainly studies and innovates the data mining process and the support vector machine model and designs the data preprocessing method in the data mining process to perform feature selection and optimize the parameters of the support vector machine model. The specific process is as follows: based on CRISP-DM, the industry-standard model of the data mining process, a series of data preprocessing, eigenvalue extraction, parameter optimization, training set pruning, and other methods are designed from business understanding, data understanding, data preparation, and other aspects to improve data quality. In the traditional support vector machine, when the test sample is located at the boundary point of the hyperplane, the judgment may be wrong. In the aspect of SVM model improvement, according to the discrimination method of SVM, the weighted K-nearest neighbor algorithm is introduced to redistinguish the qualified test samples in the feature space. From the whole process of data mining, a data mining system is designed, which is “data preprocessing standardization + genetic algorithm feature selection + training set pruning + support vector machine classifier discrimination optimization.” Finally, this paper uses a series of data mining optimization methods designed to mine the actual financial data of listed companies and has achieved good results.

A novel oil pipeline leakage detection method based on the sparrow search algorithm and CNN

A novel sparrow search algorithm and convolutional neural network (SSA-CNN) method is proposed for oil pipeline leakage detection. Firstly, the proposed SSA-CNN method converts the input data from time series to two-dimensional matrix, and the classification conditions of different convolution kernel sizes and different pooling sizes are compared. Then, the SSA algorithm is used to optimize the parameters of the CNN. The simulation results show that, compared with the traditional machine learning method, using two-dimensional data as input can enhance the neural network's extraction of eigenvalues. The proposed SSA-CNN method was able to accurately classify 148 sample points in 150 test sets with an accuracy rate of 98.67%, which is not only higher than traditional machine learning methods, but also further improves the classification capability of CNN, while the SSA-CNN method can use the parameters already learned in the test set to ensure real-time detection.

Real-Time Leak Detection in High Frequency Hydraulic Cylinder Based on Intelligent Control

Most of the existing hydraulic cylinder internal leakage detection methods are laboratory testing methods, mainly the pressure-holding method, measurement of hydraulic cylinder settlement method, and measuring cup measurement method. The internal leakage of the hydraulic cylinder affects the damping characteristics of the control system. Therefore, real-time internal leakage detection plays an important role in the control characteristics. This paper first proposes a wavelet analysis-based extraction of hydraulic cylinder internal leakage fault eigenvalues for analysis, that is, data processing. A convolutional neural network-based detection method is also proposed, in which the pressure signal of a chamber of a hydraulic cylinder is first obtained through simulation under four operating conditions: no leakage, small leakage, medium leakage, and large leakage. Compared to traditional modelling methods, the method overcomes the difficulties in modelling nonlinear hydraulic systems, requires only pressure signal acquisition, is simple and reliable, and is compared with traditional BP neural networks to demonstrate its superiority.

Design and Mechanical Behavior of a Custom Adapter for Dimensional Stone Mining

The paper deals with the development of a dimensional rock sawing machine to optimize the production of small-volume dimensional stone products in limestone quarries by re-thinking the use of production and transport equipment. A low-volume mining production and transportation adapter to be developed for both production and transport can reduce the number of mining machines to be used in the mine. After cutting around the block stone, the rock material can be moved with the help of the machine. Cutting tests were performed to determine the winning properties of the limestone. The first objective of the paper was to calculate the cutting forces and cutting powers and eliminate the failure possibilities and vibrations. In this study, the eigenvalue extraction used to calculate the natural frequencies and mode shapes was based on the Lanczos iteration methods using the Abaqus software to identify any potentially dangerous frequencies. By taking the load cases from modelling of the extraction of limestone and the transport of block stones, the deformation and strength properties of the adapter were also examined using FE analysis for stress/displacement analysis. Drawing the conclusion from finite element analysis, the construction of the adapter can be developed and checked. The results can be used with great confidence, so the main dimensions of the concept serve as the basis for the dimensions of the adapter.

Study on upper limb joint angle prediction method based on sEMG

Aiming at the problems of insufficient human-computer interaction and human-machine coupling in the rehabilitation training process, a prediction model of upper limb joint angle is proposed and verified by experiments. Firstly, a mixture vector that can well represent the motion intention of the upper limbs is obtained based on sEMG; secondly, the signal preprocessing, feature optimization and extraction of temporal eigenvalues are completed; finally, for the problems of unsatisfactory prediction accuracy and slow prediction speed of the current models in the field of motion control, the least square method (LSM) is adopted. The upper limb joint angle prediction is realized by multiplying the support vector machine (LSSVM) first. The experimental results show that the prediction model proposed in this paper can well predict the motion trajectory of the upper limb joints of the human body according to the sEMG and attitude information, effectively reduce the prediction time delay and error, and has certain advantages.

On the Detection of Fracture within Vibrating Beams Traversed by a Moving Force

In this work, we examine the influence of a crack in the span of a beam as it is being traversed by a point force with constant velocity. This problem presents two types of discontinuities: one spatial, where the crack is modelled as a discontinuity in the slope of the deflection curve of the beam, and a temporal one, with the former derived as the point force moves forward in time. The aim is to interpret time signals registered at a given node on the beam, either during the forced vibration or the free vibration regimes, by using the Gabor transform of the transient beam response so as to observe a pattern that alludes to the location of the discontinuity. Three analytical methods are examined, namely eigenvalue extraction, Laplace transformation and the transform matrix technique. A numerical example is presented using the Laplace transformation, where it is possible to detect the location of damage during the traverse of a point force across the bridge span. Validation studies of the methodology presented here can be conducted in the future, either through field measurements or through experimental setups, which constitutes an important step in realizing applications in structural health monitoring of civil engineering infrastructure.

On‐load tap‐changer fault mode recognition based on the singular value of Hilbert energy spectrum time‐frequency matrix and spectrum entropy

To solve the difficulty of eigenvalue extraction of on-load tap-changer (OLTC) vibration signals, an eigenvalue extraction method of time-frequency matrix based on Hilbert energy spectrum and spectral entropy is proposed in this study. By optimizing the Hilbert-Huang transform (HHT) algorithm, the Hilbert energy spectrum of the vibration signal based on the time-frequency distribution is obtained. The spectrum entropy of Hilbert energy spectrum was extracted to characterize the chaotic degree of vibration signal energy distribution, and avoided the problem of inaccurate feature quantity such as similarity due to the minute time difference during signal acquisition. A new time-frequency matrix was constructed by frequency band division and singular value decomposition (SVD) was carried out. The singular value vector obtained contains the time, frequency and energy information, such as vibration frequency distribution and time-frequency plane energy distribution, which represent the essential features of the original signal. The mechanical states of OLTC were characterized by Hilbert spectrum entropy and singular value. The features of the measured signals under the three typical faults of the driving mechanism are input into the intelligent multi-classification support vector machine (DAG-SVM) for pattern classification. The results show that this method can accurately identify the mechanical state of OLTC, and has practical application significance.

Research on key index evaluation of power transmission and transformation wiring based on three-dimensional intelligent evaluation

In order to better define the important indicators of power transmission and transformation lines and make their important indicators in the evaluation more accurate, so as to ensure the effectiveness of their evaluation. This paper introduces a three-dimensional intelligent evaluation technology to evaluate the main indexes of power transmission and transformation lines. Based on the transmission and transformation wiring, the data structure of transmission and transformation wiring is established by using three-dimensional digital technology. Based on the analysis of different 3D design results, the extraction of eigenvalues and key data is discussed, and a 3D intelligent evaluation system is established. On this basis, the main evaluation index system of transmission line is established. Using the fuzzy comprehensive evaluation method, the weight of the main indexes of transmission line is calculated, and the evaluation model of important indexes is established and evaluated. The experimental results show that this method is used to evaluate the main indexes of power transmission and transformation lines, and its weight is about 0.56, so that the weight of each index tends to be balanced. The contribution values of construction and progress of power transmission and transformation lines and quality control indexes of power transmission and transformation lines are 0.8 respectively. This shows that the main indexes of transmission lines are evaluated and good results are achieved.