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Search Results: 1 - 10 of 25697 matches for " Junsheng Cheng "
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Optimization model of a structural simulation design for a CICC
HuaWei Jiang,SongTao Wu,JunSheng Cheng
Chinese Science Bulletin , 2011, DOI: 10.1007/s11434-011-4408-0
Abstract: The engineering design of a Cable-In-Conduit Conductor (CICC) is complicated. A model for the optimal design of a CICC based on conductor stability, AC loss and strain is proposed. The model considers the critical current density as a function of applied strain. A mathematical programming method that minimizes the AC loss of the CICC is established to yield an optimal design for the CICC structure. The optimized structure and related performance agree well with the engineering design values used for the KSTAR project.
A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM
Junsheng Cheng,Dejie Yu,Yu Yang
EURASIP Journal on Advances in Signal Processing , 2008, DOI: 10.1155/2008/647135
Abstract: An accurate autoregressive (AR) model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD), which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs), is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM) is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.
Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis
Jinde Zheng,Junsheng Cheng,Yu Yang
Shock and Vibration , 2014, DOI: 10.1155/2014/154291
Abstract: A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS) is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively. 1. Introduction The vibration signals of mechanical systems, especially for ones with fault, often show mutation, nonlinearity, and nonstationarity because of the strike, velocity chopping, structure transmutation, loading, and friction. Hence, it is very crucial for mechanical fault diagnosis to extract the fault feature information from the nonlinear and nonstationary signal. A primary method for dealing with the nonlinear and nonstationary signal is time-frequency analysis [1], which has been applied to the mechanical fault diagnosis field widely for its ability to provide local information both in time and frequency domains of vibration signals [2]. However, the time-frequency analysis method, such as wavelet transform or Hilbert-Huang transform [3, 4], which decomposes the vibration signal into several stationary monocomponent signals, cannot reflect the subtle dynamic changes of vibration signal effectively and, therefore, inevitably will have some limitations [5]. With the development of nonlinear dynamic theories, especially in recent years, a number of nonlinear parameters and methods, such as chaos theory, fractal dimension, and information entropy, have been applied to machine condition monitoring and fault diagnosis. For instance, Logan and Mathew elaborated the application of the correlation dimension to vibration fault diagnosis of rolling element bearing
Higher-Order Numeric Solutions for Nonlinear Systems Based on the Modified Decomposition Method  [PDF]
Junsheng Duan
Journal of Applied Mathematics and Physics (JAMP) , 2014, DOI: 10.4236/jamp.2014.21001

Higher-order numeric solutions for nonlinear differential equations based on the Rach-Adomian-Meyers modified decomposition method are designed in this work. The presented one-step numeric algorithm has a high efficiency due to the new, efficient algorithms of the Adomian polynomials, and it enables us to easily generate a higher-order numeric scheme such as a 10th-order scheme, while for the Runge-Kutta method, there is no general procedure to generate higher-order numeric solutions. Finally, the method is demonstrated by using the Duffing equation and the pendulum equation.

Novel Compact Multiband MIMO Antenna for Mobile Terminal
Cheng Yang,Yuan Yao,Junsheng Yu,Xiaodong Chen
International Journal of Antennas and Propagation , 2012, DOI: 10.1155/2012/691681
Abstract: A novel compact MIMO antenna for personal digital assistant (PDA) and pad computer is proposed. The proposed antenna is composed by two multipatch monopole antennas which are placed 90° apart for orthogonal radiation. To strengthen the isolation, a T-shaped ground branch with proper dimension is used to generate an additional coupling path to lower the mutual coupling (below −15 dB), especially at GSM850/900 band. The proposed MIMO antenna is fabricated and tested, both the simulated and the measured results are presented, and some parametric studies are also demonstrated. In addition, there are some advantages about the proposed antenna such as simple structure, easy fabrication, and low cost.
Multistage Numerical Picard Iteration Methods for Nonlinear Volterra Integral Equations of the Second Kind  [PDF]
Lian Chen, Junsheng Duan
Advances in Pure Mathematics (APM) , 2015, DOI: 10.4236/apm.2015.511061
Abstract: Using the Picard iteration method and treating the involved integration by numerical quadrature formulas, we propose a numerical scheme for the second kind nonlinear Volterra integral equations. For enlarging the convergence region of the Picard iteration method, multistage algorithm is devised. We also introduce an algorithm for problems with some singularities at the limits of integration including fractional integral equations. Numerical tests verify the validity of the proposed schemes.
A Controlled Mixing Method for Stabilizing the Purity and Reducing the Waste in Gas Delivery Systems  [PDF]
Junsheng Wu, Farhang Shadman
Advances in Chemical Engineering and Science (ACES) , 2019, DOI: 10.4236/aces.2019.91002
Abstract: The variation of impurity concertation in the ultra-high purity (UHP) gases, delivered from cryogenic storage tanks and transported through long pipes, is a major problem in systems like those used in semiconductor manufacturing facilities. A method is developed for stabilizing the purity and reducing the gas consumption in these systems. This technique uses a dynamically controlled mixing of gases supplied by multiple cryogenic tanks. The control scheme uses software modules that simulate the processes that cause purity variation in both the cryogenic tanks and the transport lines. These processes include vaporization and supply in tanks, various modes of transport in delivery pipes, and the adsorption and desorption on surfaces. The method also includes and corrects for variations caused by transience in gas usage rate as well as ambient conditions.
A Shaping Technique for Air-Borne Scanning Reflector Antenna
Xiaoming Liu;Junsheng Yu;Xiaodong Chen;Yuan Yao;Cheng Yang;Zejiang Lu
PIER B , 2013,
Abstract: With the renewed application of millimeter technology in remote sensing, radio astronomy, and meteorological satellite, millimeter wave antennas of electrically large aperture are frequently deployed. Shaping techniques are accordingly developed to meet different requirements. In this paper, a shaping technique for the scanning reflector antenna system of a remote sensing spacecraft is presented. The shaping technique is based on Fourier optical theory to control the maximal radiating direction of the antenna system. To implement such functionality, a new shaping technique of the sub-reflector has been developed. In addition, rotation of the shaped sub-reflector can achieve scanning purpose with identical footprints in all scanning angles. Case studies have been performed to verify the shaping technique.
BP Neural Network Algorithm Optimized by Genetic Algorithm and Its Simulation
Junsheng Jiang
International Journal of Computer Science Issues , 2013,
Abstract: Aiming at the drawbacks of slowly converging and easily getting in the local minimum appearing in the BP neural network, this paper combines the general optimization of the genetic algorithm together with the local optimization of BP neural network to improve the performance of BP neural network. The numerical experiment shows that, compared with the original BP neural network, the improved BP neural network can effectively reduce the average error of model calculation and prediction, greatly cut the times of iteration, and raise the calculation accuracy and convergence speed. This paper also demonstrates the ability of the genetic algorithm to improve the performance of BP neural network.
On maximal injective subalgebras of tensor products of von Neumann algebras
Junsheng Fang
Mathematics , 2006,
Abstract: Let M_i be a von Neumann algebra, and B_i be a maximal injective von Neumann subalgebra of M_i, i=1,2. If M_1 has separable predual and the center of B_1 is atomic, e.g., B_1 is a factor, then B_1\tensor B_2 is a maximal injective von Neuamnn subalgebra of M_1\tensor M_2. This partly answers a question of Popa
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