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Search Results: 1 - 10 of 120162 matches for " Lijia Wang "
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Generalized Parseval’s Theorem on Fractional Fourier Transform for Discrete Signals and Filtering of LFM Signals  [PDF]
Xiaotong Wang, Guanlei Xu, Yue Ma, Lijia Zhou, Longtao Wang
Journal of Signal and Information Processing (JSIP) , 2013, DOI: 10.4236/jsip.2013.43035
Abstract:

This paper investigates the generalized Parseval’s theorem of fractional Fourier transform (FRFT) for concentrated data. Also, in the framework of multiple FRFT domains, Parseval’s theorem reduces to an inequality with lower and upper bounds associated with FRFT parameters, named as generalized Parseval’s theorem by us. These results theoretically provide potential valuable applications in filtering, and examples of filtering for LFM signals in FRFT domains are demonstrated to support the derived conclusions.

Time-Varying Bandpass Filter Based on Assisted Signals for AM-FM Signal Separation: A Revisit  [PDF]
Guanlei Xu, Xiaotong Wang, Xiaogang Xu, Lijia Zhou, Limin Shao
Journal of Signal and Information Processing (JSIP) , 2013, DOI: 10.4236/jsip.2013.43031
Abstract:

In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.

Discrete Entropic Uncertainty Relations Associated with FRFT  [PDF]
Guanlei Xu, Xiaotong Wang, Lijia Zhou, Xiaogang Xu, Limin Shao
Journal of Signal and Information Processing (JSIP) , 2013, DOI: 10.4236/jsip.2013.43B021
Abstract:

Based on the definition and properties of discrete fractional Fourier transform (DFRFT), we introduced the discrete Hausdorff-Young inequality. Furthermore, the discrete Shannon entropic uncertainty relation and discrete Rényi entropic uncertainty relation were explored. Also, the condition of equality via Lagrange optimization was developed, as shows that if the two conjugate variables have constant amplitudes that are the inverse of the square root of numbers of non-zero elements, then the uncertainty relations reach their lowest bounds. In addition, the resolution analysis via the uncertainty is discussed as well.

The Modeling and the Sensor Fault Diagnosis of a Continuous Stirred Tank Reactor with a Takagi-Sugeno Recurrent Fuzzy Neural Network
Li Shi,Lijia Wang,Zhizhong Wang
International Journal of Distributed Sensor Networks , 2009, DOI: 10.1080/15501320802524037
Abstract: In this paper, a novel Takagi-Sugeno recurrent fuzzy neural network (TSRFNN) is constructed for modeling and sensor fault diagnosis of a Continuous Stirred Tank Reactor (CSTR), a nonlinear dynamic system. The TSRFNN is composed of 9 layers, including premise network and consequence network. The temporal information is embedded in the TSRFNN by adding the feedback connections between the output layer and the input layer of the fuzzy neural network (FNN). It is assumed that the inputs are Gaussian membership functions; the product operation is utilized for the premise and implication, and the weighted center-average method is adopted for defuzzification. The network is a Fuzzy Basis Function(FBF). The general approximation characteristic of the network was proven by the theory reasoning. The identification of the TSRFNN consists of two steps: structure identification and parameter identification. Unsupervised clustering is used to determine the structure of the fuzzy system, the number of fuzzy rules, and the membership functions of the premise using the input-output data of a system. Then in the parameter identification, the Dynamic Backpropagation (DBP) is adopted to determine the membership functions of the conclusion of the fuzzy system.
Low-cycle fatigue behavior of permanent mold cast and die-cast
Chen Lijia,Wang Di,Che Xin
China Foundry , 2012,
Abstract: Fatigue failure is one of the main failure forms of Al-Si-Cu-Mg aluminum alloys. To feature their mechanical aspect of fatigue behavior, the low-cycle fatigue behavior of permanent mold cast and die-cast Al-Si-Cu-Mg alloys at room temperature was investigated. The experimental results show that both permanent mold cast and die-cast Al-Si-Cu-Mg alloys mainly exhibit cyclic strain hardening. At the same total strain amplitude, the die-cast Al-Si-Cu-Mg alloy shows higher cyclic deformation resistance and longer fatigue life than does the permanent mold cast Al-Si-Cu-Mg alloy. The relationship between both elastic and plastic strain amplitudes with reversals to failure shows a monotonic linear behavior, and can be described by the Basquin and Cof n-Manson equations, respectively.
Influences of electric pulse on solidification structure of LM-29 Al-Si alloy
He Lijia,Wang Jianzhong,Qi Jingang
China Foundry , 2010,
Abstract: The metallographic structure of LM-29 aluminum-silicon alloy modified by electric pulse treatment has been investigated and compared with those untreated. The solidification structure of LM-29 alloy has been analyzed by means of M1AP3 Quantimet image processing and analysis system, and then the solidification process has been analyzed by means of differential scanning calorimetry (DSC). The results indicate that the primary silicon phase was refined remarkably by electric pulse while the tensile strength and elongation properties increased accordingly. Electric pulse treatment can also increase the binding power between silicon clusters and alloy melt matrix, as a result, the precipitation of primary silicon phase is suppressed to meet the demand of supercooling degree for nucleating, correspondingly. The electric pulse modification has great influence on the size of silicon atomic cluster as well as its distribution in the melt, subsequently, leads to the refinement of solidification structure.
Inviscid limit for the derivative Ginzburg-Landau equation with small data in higher spatial dimensions
Lijia Han,Baoxiang Wang,Boling Guo
Mathematics , 2010,
Abstract: We study the inviscid limit for the Cauchy problem of derivative Ginzburg-Landau equation in higher dimension space n>2. We show that it is global well-posed and its solution will converge to that of derivative Schrodinger equation.
Global Smooth Effects and Well-Posedness for the Derivative Nonlinear Schr?dinger Equation with Small Rough Data
Wang Baoxiang,Han Lijia,Huang Chunyan
Mathematics , 2008,
Abstract: \rm We obtain the global smooth effects for the solutions of the linear Schr\"odinger equation in anisotropic Lebesgue spaces. Applying these estimates, we study the Cauchy problem for the generalized elliptical and non-elliptical derivative nonlinear Schr\"odinger equations (DNLS) and get the global well posedness of solutions with small data in modulation spaces $M^{3/2}_{2,1}(\mathbb{R}^n)$. Noticing that $H^{\tilde{s}} \subset M^s_{2,1}$ $(\tilde{s}-s>n/2)$ is an optimal inclusion, we have shown the global well posedness of DNLS with a class of very rough data.
The Aide Diagnosis of Cardiac Heart Diseases Using a Deoxyribonucleic Acid Based Backpropagation Neural Network
Li Shi,Zhizhong Wang,Lijia Wang,Jinying Zhang
International Journal of Distributed Sensor Networks , 2009, DOI: 10.1080/15501320802533418
Abstract: In this paper, a novel diagnostic scheme of cardiac heart diseases is presented using a Deoxyribonucleic Acid (DNA)-based BP(DNA-BP) Neural Network by distinguishing the shapes of ST segments automatically. First, wavelet transform is applied to extract the ST segments by identifying the characteristic points in the ECG. ECG signals are decomposed by aTrous Algorithm using dyadic spline wavelets. The relationship between the feature points of ECG signals and the modulus maximum pairs of the signals' wavelet transform is established, and the R-wave and ST segment's fiducial points are extracted at different wavelet scales. Second, in order to overcome the disadvantages of the BP neural network, a DNA optimization method is adopted to optimize the original weights and bias of a BP neural network. The BP algorithm is used to find the most optimal values of the weights and bias of the BP network. At last the effectiveness of the proposed aided diagnostic scheme is demonstrated via the experiments which the data are from the clinical study and MIT/BIH ECG data base. In order to validate the advantages of the DNA-based BP (DNA-BP) network and determine the best application conditions of the network, two types of experiments were conducted. In each experiment, 30 samples were selected as the training data and testing data respectively. In the first type of experiments, the data were obtained from the ECG of different people. While in the second type of experiments, the data were obtained from the ECG of one person at different times. The same data and the conditions were used in both BP and GA-based BP (GA-BP) networks to illustrate the advantages of the proposed DNA-based BP network. The experiment results illustrate that the proposed DNA-based BP network overcomes the limitation of the sloping method in identifying straight line ST segments and the limitation of function fitting method in fitting accuracy. It also surmounts the disadvantages of a BP network in terms of the local minimum, slow convergence, and the shortcomings of GA-BP in terms of its limitation in algorithm coding and evolution ways.
Molecular evolution of the exon 2 of CHS genes and the possibility of its application to plant phylogenetic analysis
Jinling Wang,Lijia Qu,Jun Chen,Hongya Gu,Zhangliang Chen
Chinese Science Bulletin , 2000, DOI: 10.1007/BF02886256
Abstract: The exon 2 of chalcone synthase (CHS) gene is relatively conserved during evolution. In this study, three exon 2 fragments from two species in gymnosperm (Cycas panzhihuaensis, Ginkgo biloba) and seven from four species in angiosperm (Magnolia denudata, Salix babylonica, Nymphaea tetragona, Camellia japonica) have been amplified by PCR from genomic DNA and sequenced. Together with other 73 sequences ofCHS collected from EMBL database and literature, these sequences, which embrace 19 families of gymnosperm and angiosperm, have been analyzed for their phylogenetic relations by parsimony method. The result indicated that sequences from the same systematic family usually grouped together except those from Theaceae, Magnoliaceae and Nymphaeaceae. The relative rate test revealed the rate heterogeneity of CHS genes among the families. For the nucleotide substitution the sequences from Asteraceae and Solanaceae evolve faster than those from the other families analyzed while the sequences from Poaceae, Asteraceae and Solanaceae evolve faster for the nonsynonymous substitution. These results suggest that the duplication and extinction events of CHS genes are different among systematic families, therefore it seems impractical to look for orthologous sequences from CHS genes to study plant phylogeny at the family level andlor above. However, it is possible to do so below the family level.
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