bluuebunny/crossref_metadata_2025_split huggingface dataset:
Three different approaches to analysis cracked ring with opposite concentrated loads
Authors: Young Lih-jier , Chen Ming-Li 
Published: September 2020

Abstract
Circular ring is the most useful specimen in industry. However, any small crack exists on it will cause some damage about the equipment and even more some serious disaster. As we understand plastic hinge will appear if we increase the load of the ring. The ring will collapse when there are four plastic hinges. The sequence of formation of these hinges is also depended on the location of the crack of the ring. In this paper three different approaches i.e., theoretical, numerical, and experimental will be presented. The theoretical approach is based on the linear elastic fracture mechanics (LEFM) and the method of superposition. The boundary element method (BEM) will be employed in the numerical approach. Finally, the cracked ring specimen was made of Aluminum 6061-T6 whose property is close to the ideally elastic-plastic material property in the experimental approach. The results show the consistency of these three different approaches and provide reasonable information to inspect the circular ring used in daily life.
The signed (| G |−1)-subdomination number of lollipop graphs
Authors: Shi Wei , Wu Suichao 
Published: September 2020

Abstract
Let G be a lollipop graph obtained by appending a cycle Cn
to a pendent vertex of a path Pm
. In this paper, the signed (| G |−1) - subdomination number of a lollipop graph G is determined by classified discussion and exhaustived method.
Prediction of the crowd behavior in campus based on time series model
Authors: Chen Liangwei , Yang Jingjing , Chen Zhigang , Huang Ming 
Published: September 2020

Abstract
Recognizing and understanding the behavior of the campus crowd is of great significance for improving the comprehensive management capabilities of the campus. The increasingly abundant of mobile terminal WIFI signals provide a unique data source for studying the behavior of the campus crowd. A Wifi-based personnel activity monitoring system is developed in this paper. Through monitoring and analyzing the MAC address (Media Access Control Address), RSSI (Received Signal Strength Indication) and Wireless AP (Access Point) in and around the Science Museum, Huaizhou building of Yunnan University, the association between WIFI signal and the crowd behavior is studied. Time series analysis is used to make predictions, and some valuable information about the gathering and activity status of campus crowd behavior is obtained. It provides an experimental platform for studying the dynamic behavior of people in campus or other crowd areas.
Study on nonlinear analysis of MEG in patients with brain diseases
Authors: Zhang Yiling , Zhang Yu , Chen Junwei , Yan Wei , Yuan Zizhen , Wang Jun 
Published: September 2020

Abstract
Magnetoencephalography (MEG) is a kind of brain function detection technology without trauma, which detects the electromagnetic physiological signal in the brain. It is widely used in depression research. We used permutation entropy, improved permutation entropy, conditional entropy to study the difference of MEG between depressed patients and healthy people, respectively. We implement three different entropy algorithms and compare the relative differences of entropy values in different brain regions of healthy people and depression patients under positive, neutral and negative emotional stimuli. The experimental results show that the relative difference of conditional entropy > the relative difference of improved permutation entropy > the relative difference of permutation entropy, and the difference of entropy value in the frontal region of human brain is the most obvious, which may provide the basis for the diagnosis of depression.
An efficient method to reduce ill-posedness for dynamic load identification in short duration wind tunnels
Authors: Wu Long , Wang Feng , Ding Zhijian , Du Yufeng , Chen Shengbing 
Published: September 2020

AbstractFor the inverse problem of dynamic load identification in short duration wind tunnels, high system ill-posedness is a main cause leading to instability and low accuracy. In this study, an efficient method is proposed to reduce ill-posedness and identify dynamic load stably. The load to be identified is written as superposition of specific basis functions and each basis function is determined by specific parameters. A genetic algorithm is built to solve the parameters and construct the load. Compared with Tikhonov regularization method, the basis function method proposed can reduce the ill-posedness and numerical studies demonstrate its efficiency and accuracy.
Identification of adulterated olive oil by fusion of near infrared and Raman spectroscopy
Authors: Xu Qiang , Yang Xingxing , Hu Zhui , Li Yuan , Zheng Xiao 
Published: September 2020

Abstract
Aiming at the identification of adulterated olive oil, based on the fusion of near-infrared and Raman data on the feature level, a model for the identification of adulterated olive oil was established.24 pure olive oil samples and 248 adulterated olive oil samples were used for model establishment in the experiment. With three parameter optimization methods, support vector machine classification and regression models were established based on fusion of near-infrared and Raman data on the feature level with canonical correlation analysis (CCA). The results showed that the accuracy of correction and prediction sets of olive oil adulteration classification model based on the fusion of near-infrared and Raman data was high, and the correlation coefficient of prediction model based on the fusion of near-infrared and Raman data reached 99.13%. The results showed that the classification and prediction models were feasible and effective in the identification of adulterated olive oils, and this work shed light on the development and applications of fusion of spectral data.
Analysis of main control factors of CBM productivity and optimization of sweet spot area in S block of Surat basin
Authors: Cui Zehong , Liu Lingli , Wang Jianjun , Xia Zhaohui 
Published: September 2020

Abstract
S block is located in the eastern margin of Surat basin, with large area and great difference in geological conditions. How to optimize the sweet spot development zone is the key to achieve the economic and efficient development of the block. Based on the static and dynamic data, this paper conducts the sensitivity analysis for peak gas rate of well, Estimated ultimate recovery(EUR) of well and coal reservoir geological parameters. On the precondition of coal seam permeability higher than 100md, the coal seam thickness and gas content are the main factors affecting the development effect. According to the differences of key reservoir parameters, four types of reservoirs are divided: type 1 reservoir thickness≥20m, dry-ash-free gas content≥4m3/t; type 2 reservoir Thickness ≥20m, dry-ash-free gas content between 3.6-4m3/t; type 3 reservoir thickness between 10-20m, dry-ash-free gas content between 3.2-3.6m3/t; type 4 reservoir thickness between 10-20m, dry-ash-free gas content between 2.8-3.2m3/t. Considering the reserve abundance and exploitable value, reservoirs of type 1 and 2 are considered as the development sweet spot area, which provides guidance for optimum selection of development area of coalbed methane(CBM) in S block of Surat basin.
An efficient ADMM-type algorithm for deep semi-nonnegative matrix factorization
Authors: Zhou Yijia , Xu Lijun 
Published: September 2020

Abstract
In this paper, we focus on deep semi-nonnegative matrix factorization (DSemiNMF) which has a wider application in the real world than traditional NMF. We propose an efficient algorithm based on the classic alternating direction method of multipliers (ADMM) for DSemiNMF. By utilizing structures in DSemiNMF, we derive an efficient updating rule for updating subproblems according to its KKT conditions. Numerical experiments are conducted to compare the proposed algorithm with state-of-the-art deep semi-NMF algorithm. Results show that our algorithm performs better and the deep model indeed results in better clustering accuracy than single-layer model.
Research on AHP-fuzzy comprehensive evaluation method and application
Authors: Yuan Kaiyou , Li Heng , Jiang Ming 
Published: September 2020

Abstract
Considering the limitation of AHP-fuzzy comprehensive evaluation method on the isomorphism and mutual exclusion of comment sets, it is difficult to use this method to evaluate actual cases with different dimension comment sets or check comment grades. This paper propounded an improved AHP-fuzzy comprehensive evaluation method with heterogeneous and check comment set, and the proposed method was further applied to the comprehensive evaluation of the evaluation state of collegiate teaching quality. Results illustrated that the improved method excelled not only in analysing and comparing the membership degree of each evaluation grade of the ultimate evaluation factors, but also in examining and evaluating the expected compliancedegree of each evaluation factor. The evaluation results were demonstrated to be more objective, accurate and applicable in a much wider scope.
A right preconditioner for the LSMR method
Authors: Hasanpour Afsaneh , Mojarrab Maryam 
Published: September 2020

AbstractThe LSMR (Least Squares Minimal Residual) method is an absorbing solver that can solve linear systemAx=band least squares problemmin||Ax=b|| where A is a sparse and large matrix. This method is based on the Golub-Kahan bidiagonalization process and sometimes it may converge slowly like other methods. I n order to prevent this event, a right preconditioner for LSMR method is presented to solve large and sparse linear system which used for LSQR (Least Squares with QR factorization) method before. Numerical examples and comparing the preconditioned LSMR method to unpreconditioned LSMR method would show the effectiveness of the preconditioner. I t is obtained from this paper that PLSMR (Preconditioned LSMR) method has a better performance in reducing the number of iterations and relative residual norm in comparing with the original LSMR method.
Time fractional CGMY model for the numerical pricing of European call options
Authors: Wang Xiaoxia , Xing Jianhong 
Published: September 2020

Abstract
Evolutionary equations containing fractional derivatives have been widely used in financial models and can describe anomalous diffusion and transmission dynamics in some complex systems. We assumes that option pricing obeys the infinite jump CGMY process and treats stock price fluctuations as a fractal transmission system, A time fractional CGMY option pricing (TFCGMY) model is obtained. The L1 approximation of the Caputo fractional derivative and the modified GL approximation are used to discretize the time and space fractional order operators; the first order space derivative is discretized using the central difference quotient. The TFCGMY model and the above numerical techniques are used for the pricing analysis of European call options. By comparing and analyzing the option pricing model with the Black-Scholes model and the CGMY model shows that the introduction of time fractional differentiation can better capture the jump characteristics of stock prices of options.
Compact implicit integration factor method for two-dimensional space-fractional advection-diffusion-reaction equations
Authors: Jian Huanyan , Huang Tingzhu , Gu Xianming , Zhao Yongliang 
Published: September 2020

AbstractIn this paper, we intend to develop an effective numerical method to solve a class of two-dimensional space-fractional advection-diffusion-reaction equations. After spatially discretizing this equation using the fractional centered difference formula, it leads to a system of nonlinear ordinary differential equations. The compact implicit integration factor method is applied to solve the resulting system to achieve good stability and robustness. Linear stability analysis and numerical experiments are given to verify that the compact implicit integration factor method has excellent efficiency and stability properties.
Existence and uniqueness of positive fuzzy solution for a three-order fuzzy difference equation
Authors: Jia Lili , Wang Changyou , Yang Qiang 
Published: September 2020

Abstract
Our aim in this paper is to study a three-order fuzzy difference equation. Firstly, the fuzzy set theory is used to transform the fuzzy difference equation into the corresponding ordinary difference equations with parameters α, α ∊ (0,1]. And then, the existence and uniqueness for the solution to the ordinary difference equations are obtained by the iteration method, the inequality technique and the mathematical induction. Finally, the existence and uniqueness for the solution of the fuzzy difference equation is proved.
Solving fuzzy linear matrix equation
Authors: Shang Dequan , Guo Xiaobin 
Published: September 2020

Abstract
In this paper a class of fuzzy linear matrix equation AXB=C in which A and B are crisp matrices and C is a fuzzy numbers matrix, is investigated by using the embedding approach. A new matrix method for calculating the solution is designed in detail and a sufficient condition for the existence of strong fuzzy solution is derived. An example is given to illustrate our proposed method. Our result enriches the fuzzy linear systems theory.
A Bayesian level set method for inverse source scattering problems with multi-frequencies
Authors: Huang Jiangfeng 
Published: September 2020

Abstract
This paper concerns the reconstruction of the support of source function based on the Bayesian level set approach. The unknown source to be reconstructed is assumed to be piecewise constant with a known value. In this setting, the support of the source function can be characterized by the level set functions. In the Bayesian level set inversion, the solution of the inverse problem is posterior distribution. The Markov Chain Monte Carlo (MCMC) algorithm is applied to generate the samples of the posterior distribution. The numerical results show the effectiveness of the proposed method and the dependence of the posterior samples on the flexible and proper smoothness priors with the Whittle-Matérn Gaussian random fields.
On the distance spectrum of windmill graphs
Authors: Wang Zhiping , Xu Jiaxue , Xu Xiaojing 
Published: September 2020

Abstract
The distance Laplacian matrix and the distance signless Laplacian matrix of a connected graph G are defined by LD(G) = TrG−D(G) and QD(G) = TrG + D(G). By a harmonious labelings of vertex set, the distance matrix of windmill graphs

K

n

(
m
)

is described as a block matrix. In this paper, we obtain the eigenvalues and the corresponding eigenvectors of the distance matrix, the distance Laplacian matrix and the distance signless Laplacian matrix of windmill graphs

K

n

(
m
)

.
Based on multiple linear regression analysis of ability of medical postgraduate students to express SPSS experiment results with three-line table
Authors: Hu Ziquan 
Published: September 2020

Abstract
Medical postgraduate students (MPS) can select the appropriate statistical methods to do the SPSS experiments and view the experimental results after they have taken SPSS course. It is vital for MPS to exactly convert SPSS experiment results into three-line table in their academic papers. This paper investigated the Ability of MPS in Expressing SPSS experiment Results with Three-Line Table (AERTLT). The enrolled MPS in Southwest Medical University filled in the questionnaire about AERTLT on Jan, 2019. Questionnaire data are analysed using Multiple Linear Regression (MLR). Complex decision factor R
2=0.834254 and adjoint probability p<0.001 in MLR suggest the linear correlation between AERTLT and 16 factors passes test. Then backward deletion variable method is employed to repeatedly delete the factor whose coefficient is the minimum value in partial f-test. AERTLT can be expressed by four significant factors i.e. “Analytical results in comparison of mean and population mean”, “Analytical results in regression analysis”, “Comment description in three-line table”, and “P-Error in insufficient sample size or unsatisfactory univariate analysis”. These four factors are the key point for SPSS course to cultivate MPS in the medical universities and colleges.
Leaf image recognition and classification based on GBDT-probabilistic neural network
Authors: Tang Zhixuan 
Published: September 2020

Abstract
In this paper, the binary images of 100 kinds of leaves are used for leaf recognition. Firstly, we screen 35 important features and use the grey clustering analysis to establish the quantitative feature system of leaves. Then we use the gradient descent tree algorithm (GBDT) to select core features and use probabilistic neural network (PNN) to recognize and classify leaves, constructing a hybrid GBDT-PNN model. In the end, we obtain the classification results of leaves to evaluate model performance and the influence of core features on the model. The results show that the accuracy rate of GBDT-PNN model using 12 core features is 92.75%. And the accuracy rate with all 35 features is 93.5%. It illustrates that the model has great performance and core features have high influence on the model. By comparing with other commonly used deep learning algorithms and models, it is verified that the GBDT-PNN image recognition and classification model is effective and has high accuracy.
The application of Euclid approach degree comprehensive evaluation method in credit evaluation of commercial circulation enterprises
Authors: Zhou Li , Wang Lizhi , Zhao Yan , Li Xianghua 
Published: September 2020

Abstract
Based on the thorough analysis of the factors affecting the credit level of commercial circulation enterprises, a scientific and systematic evaluation index system was established. By adopting the fuzzy matter-element analytical method, which especially suits the multi-index and incompatibility issue, the evaluation model was built. The comprehensive integrated weighting method was applied for the determination of the index weight. Meanwhile, the subjective and objective information were considered comprehensively to elaborate the inherent law of the commercial circulation enterprise credit risk evaluation, which provide a practical and effective method for judging the credit level of commercial circulation enterprises and analyzing the key influencing factors of credit evaluation, and also provide a direction and reference for improving the credit level of commercial circulation enterprises.
A dynamical-scanning inner product based on Euclidean inner product
Authors: Chen Ray-Ming 
Published: September 2020

Abstract
Euclidean dot product plays an important role in data analysis and relation comparison for its intuitive properties. However, for some complicated structures, for example, time series, it would be insufficient to construct a complete theory via this product. In order to resolve and accommodate these issues, we devise a dynamical-scanning inner product which would take the interaction, in particular in the sense of time shift, between two vectors into consideration and yield a relation that contains much more information. Though this device is based on Euclidean dot product, it is more much suitable and flexible for handling some complicated mathematical objects. In addition, we also construct a dynamical-scanning inner product for function spaces. In the end, we show how to apply our devices on both matrix representations and time series. These exploration might delve into some intrinsic properties of some mathematical concepts or models.
Convergence of an alternating direction and projection method for sparse dictionary learning
Authors: Zhou Yijia , Xu Lijun 
Published: September 2020

Abstract
In this paper, we focus on sparse dictionary learning that is widely used as a data processing technique in many real-world applications. Based on the frame of alternating direction method of multiplier (ADMM), we extend to an alternating direction and projection method for sparse dictionary learning. By introducing proximal mapping and the equivalence to the corresponding projection, a partial convergence result of this multi-block and nonconvex ADMM algorithm is given that the algorithm converges to a Karush-Kuhn-Tucker point whenever it converges.
An algorithm for generating uniform points on a cylindrical surface
Authors: Zhu Jiayi , Li Hongjun 
Published: September 2020

AbstractThe existing Poisson-disk sampling methods work well in traditional space, whatever two dimensional or three dimensional space. However, there are few methods on generating random points in non-Euclid space. For generating uniform points on the cylinder surface, we propose an algorithm which combines Poisson-Disk sampling algorithm with cylindrical distance constraints in this paper. Experiment results show that the quality of the point set generated by the proposed algorithm is better than that generated by the pseudo-random number method in both uniformity and stability.
A numerical method for solving the time fractional reaction-diffusion equation with variable coefficients on the whole line
Authors: Liu Zeting , Shen Yanfei 
Published: September 2020

Abstract
This paper focuses on numerically solving the time fractional reaction-diffusion equation on the whole line. A numerical scheme is constructed based on Hermite pseudospectral method, we use finite difference scheme in time direction while Hermite-Gauss points in space. Several numerical results for the Hermite pseudospectral scheme are provided to confirm that a second-order time accuracy and spectral accuracy in space can be obtained.
Influence factors analysis on traffic jam for a new lattice hydrodynamic model on gyroidal road
Authors: Meng Xiangpei , Yan Liying 
Published: September 2020

Abstract
In the paper, an extended one-dimensional lattice hydrodynamic model is proposed to investigate the related factors on a gyroidal road (i.e. a curved road with a slope). We investigate the related influences on uniform traffic jam analytically and numerically. Based on control theory, condition for no traffic jam is obtained analytically. Finally, simulations are carried out to verify the new lattice hydrodynamic model and numerical simulations are consistent with the theoretical results.Results show that the related factors including the friction coefficient, radius of curvature and slope of a gyroidal road have major effect on the stability of traffic flow respectively.
Short-term traffic flow prediction on campus based on modified PSOBP neural network
Authors: Zeng Cheng , Hua Chenye , Lei Tingyu , Xiao Xinping 
Published: September 2020

Abstract
In order to ensure the efficiency of daily passage on campus and prevent the occurrence of safety accidents, an improved prediction method of optimized BP neural network based on modified particle swarm optimization algorithm (PSO) was proposed. In this modified PSO algorithm, we propose a mutation operator to avoid particles plunging into local optimization, using the modified PSO algorithm to optimize the solution of weight and threshold in BP neural network. The prediction method is applied to the time series of the observed campus traffic flow for effective verification, and the result shows that the method has better nonlinear fitting ability and higher prediction accuracy for the short-term traffic flow on campus.
Exact controllability of a first-order hyperbolic equation with an intermediate point memory
Authors: Chen Jingwen , Zhou Zhongcheng 
Published: September 2020

Abstract
In this paper, we discuss the exact controllability of a first-order hyperbolic equation with an intermediate point memory by boundary control. We choose a Volterra and Fredholm transformation transferring the original control system into a target control system with controllability property. Then, we prove the exact controllability of the original control system in terms of the invertibility of the transformation and the controllability of the target control system.