VIBRO-IMPACTS OF A SLENDER CANTILEVER BEAM WITH A LUMPED TIP MASS BETWEEN TWO RIGID STOPS

This paper presents a dynamic analysis of vibro impacts of a slender cantilever beam carrying a lumped tip mass between two rigid stops subjected to horizontal harmonic excitation of basement. This vibro impacting system is a simplified model for the vibro impacts between the shell of a flying vehicle and its interior components. The dynamic equation of vibro impacting system is established on the basis of the Galerkin method, the Lagrange method and the Newton rule of collision. The effects of excitation frequency, excitation amplitude and the clearance between the tip mass and a stop on system dynamics are numerically investigated. The nonlinear dynamics, especially various chaotic motions, are observed by using the Poincaré section. Numerical results show that the longterm behavior of system mainly depends on the above three parameters, and there exist a series of processes and corresponding reverse processes, during which a periodic motion undergoes period doubling bifurcation and then becomes chaotic motion, or vice versa.

A Reduced-Order Modeling of Multi-Port RC Networks by Means of Graph Partitioning

A modified reduced-order method for RC networks which takes a division-and-conquest strategy is presented.The whole network is partitioned into a set of sub-networks at first,then each of them is reduced by Krylov subspace techniques,and finally all the reduced sub-networks are incorporated together.With some accuracy,this method can reduce the number of both nodes and components of the circuit comparing to the traditional methods which usually only offer a reduced net with less nodes.This can markedly accelerate the sparse-matrix-based simulators whose performance is dominated by the entity of the matrix or the number of components of the circuits.

Seed Shadow of Quercus liaotungensis in a Broad-leaved Forest in Dongling Mountain

The seed shadow of oak ( Quercus liaotungensis Koidz.) was investigated in a broad_leaved deciduous forest in Dongling Mountain in 1997. The seed rain patterns under oak tree crown for three out of four oak trees fit quadratic distribution, with high coefficients of determination. For each of the four trees selected in this study the seed rain size estimated from the viable acorns collected from the traps deployed under the crown was low, ranging from 26 to 259. For each of the four oak trees, the average density of the seed rain under oak tree crown ranged from 0.76 to 7.26 seeds/m 2. The average seed rain density estimated from the acorns in the traps deployed randomly in the field was 1.57 seeds/m 2. After all viable acorns fell, the acorn ground density estimated from the acorns in the quadrats was 0.13 seeds/m 2, but no viable acorns were found on the ground under the tree crown of the four oak trees, although the area under the crown was over 5 folds than the area of the combined quadrats. These results indicated that a large number of acorns was removed or predated by vertebrates once the acorns fell on the ground and acorns predation was more intensive in the sites directly under the parent trees than the sites away from the parent trees; this supports the distance_dependent hypothesis. Of the total acorns of all four oak trees, the viable acorns only accounted for 18.1%, and 17.8% for acorns infected by insect larvae, 12.1% for immature acorns, 3.6% for decayed acorns, and the acorns predated by rodent directly in the crown was the highest, accounting for 48.4%. The proportion of acorns predated by animals including vertebrates and invertebrates amounts up to 78.3%, indicating that acorn predation by animals is a limiting factor affecting acorn survival before seed dispersal. The sustained time of viable acorn fall ranged from September 8 to October 3 for the four oak trees, with the peak between 16th and 26th September. The number of acorns was not significantly different among the four directions around the trunk base.

Experimental analysis and application of sparsity constrained deconvolution

Sparsity constrained deconvolution can improve the resolution of band-limited seismic data compared to conventional deconvolution. However, such deconvolution methods result in nonunique solutions and suppress weak reflections. The Cauchy function, modified Cauchy function, and Huber function are commonly used constraint criteria in sparse deconvolution. We used numerical experiments to analyze the ability of sparsity constrained deconvolution to restore reflectivity sequences and protect weak reflections under different constraint criteria. The experimental results demonstrate that the performance of sparsity constrained deconvolution depends on the agreement between the constraint criteria and the probability distribution of the reflectivity sequences; furthermore, the modified Cauchy- constrained criterion protects the weak reflections better than the other criteria. Based on the model experiments, the probability distribution of the reflectivity sequences of carbonate and clastic formations is statistically analyzed by using well-logging data and then the modified Cauchy-constrained deconvolution is applied to real seismic data much improving the resolution.

CONSTRAINED VITERBI ALGORITHM AND ITS APPLICATION TO ERROR RESILIENT TRANSMISSION OF SPIHT CODED IMAGES

To overcome some drawbacks of Viterbi algorithm （VA）, such as exponential growing complexity of decoding, and its poor performance under bad channel conditions, some available known information must be used as constrained condition and apriori knowledge for decoding. A new constrained VA is proposed by adding con- straint bits directly for conventional codec. Compared with the conventional VA, under the bad channel condi- tion, the proposed scheme can improve the peak signal to noise ratio （PSNR） of the decoding image 2--10 dB by changing the number of constrained bits. Experimental results show that it is an efficient error-controlling way for the transmission of set partitioning in hierarchical trees （SPIHT） coded image.

OPTIMIZATION WITH QUADRATIC CONSTRAINT IN APPLICATION OF STRUCTURE DYNAMIC MODEL UPDATING

A model updating optimization algorithm under quadratic constraints is applied to structure dynamic model updating. The updating problems of structure models are turned into the optimization with a quadratic constraint. Numerical method is presented by using singular value decomposition and an example is given. Compared with the other method, the method is efficient and feasible.

Radon-Nikodym Theorem of Signed Additive Fuzzy Measure

This is subsequent of , by using the theory of additive fuzzy measure and signed additive fuzzy measure , we prove the Radon_Nikodym Theorem and Lebesgue decomposition Theorem of signed additive fuzzy measure.

Separation method for multi-source blended seismic data

Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

Theoretical framework for distributed reduction in concept lattice

In order to reduce knowledge reasoning space and improve knowledge processing efficiency, a framework of distributed attribute reduction in concept lattices is presented. By employing the idea similar to that of the rough set, the characterization of core attributes, dispensable attributes and unnecessary attributes are described from the point of view of local formal contexts and virtual global contexts. A determinant theorem of attribute reduction is derived. Based on these results, an approach for distributed attribute reduction is presented. It first performs reduction independently on each local context using the existing approaches, and then local reducts are merged to compute reducts of global contexts. An algorithm implementation is provided and its effectiveness is validated. The distributed reduction algorithm facilitates not only improving computation efficiency but also avoiding the problems caused by the existing approaches, such as data privacy and communication overhead.

First Integrals and Reduction of the Birkhoffian System

Refer to the Hamiltonian system, first integrals of the Birkhoffian system can be found by using of the perfect differential method. Through these first integrals, the order of the Birkhoffian system can be reduced. Then according to the alternate of the coordinate, a kind of new partial differential operator was defined in order to hold the Birkhoff form. The result shows that the Birkhoffian system has generalized energy integrals and cyclic integrals. Furthermore, each integral can reduce the order of equations two degrees.

Speech emotion recognition using semi-supervised discriminant analysis

Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.

Method of variation of parameters for solving a constrained Birkhoffian system

For an in-depth study on the integration problem of the constrained mechanical systems the method of integration for the Birkhoffian system with constraints is discussed and the method of variation of parameters for solving the dynamical equations of the constrained Birkhoffian system is provided.First the differential equations of motion for the constrained Birkhoffian system as well as for the corresponding free Birkhoffian system are established.Secondly a system of auxiliary equations is constructed and the general solution of the equations is found.Finally by varying the parameters and utilizing the properties of the generalized canonical transformation of the Birkhoffian system the solution of the problem can be obtained.The proposed method reveals the inherent relationship between the solution of a free Birkhoffian system and that of a constrained Birkhoffian system. The research results are of universal significance which can be further used in a variety of constrained mechanical systems such as non-conservative systems and nonholonomic systems etc.

Effect of contract choice on upstream carbon emission reduction considering carbon taxation

In order to curb the manufacturer’s product carbon emission levels, the leading retailer usually offers three contracts to the manufacturer, i.e., wholesale-price contract(WC), cost-sharing contract(CC) and revenue-sharing contract(RC). The results of implementing the three contracts are discussed and compared. The results reveal that as long as the government levies carbon taxations, all the three contracts can effectively stimulate the manufacturer to invest in carbon emission reduction. Among the three contracts, RC can achieve the highest level of carbon emission reduction of products and the maximum profits for both the manufacturer and retailer in a supply chain. However, the RC fails to reach the level of the centralized supply chain(CSC), thus it cannot coordinate the supply chain. The supply chain members’ contract choices are consistent. Both members prefer RC to the other two contracts. In order to effectively reduce the manufacturer’s carbon emission levels, the government should impose the highest carbon taxation level under RC, the medium carbon taxation level under CC, the lowest carbon taxation level under WC, and the same carbon taxation level as RC under the CSC.

Multichannel deconvolution with spatial refl ection regularization

Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces,which gives the deconvolved result strong ambiguity and poor spatial continuity.To alleviate this issue,we developed a structurally constrained deconvolution algorithm.The proposed method extracts the refl ection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial refl ection regularization.Benefi ting from the introduction of the reflection regularization,the proposed method enhances the stability and spatial continuity of conventional deconvolution methods.Synthetic and field data examples confi rm the correctness and feasibility of the proposed method.

Existence of multi-bump solutions for coupled Schr dinger systems

The Schrodinger equation -△u＋λ2u=｜u｜2q-2u has a unique positive radial solution Uλ, which decays exponentially at infinity. Hence it is reasonable that the Schrolinger system -△u1＋u1=｜u1｜2q-1u1-εb（x）｜u2｜1｜u1｜q-1u1,-△u2＋u2=｜u2｜2q-2u2-εb（x）｜u1｜1｜u2｜q-1u2 has multiple-bump solutions which behave like Uλ in the neighborhood of some points. For u=（u1,u2）∈H1（R3）×H1（R3）, a nonlinear functional Iε（u）=I1（u1）＋I2（u2）-ε/q∫R3b（x）｜u1｜q｜u2｜qdx,is defined,where I1（u1）=1/2｜｜u1｜｜2-1/2q∫R3｜u1｜2qdx and I2（u2）=1/2｜｜u2｜｜2ω-1/2q∫R3｜u2｜2qdx. It is proved that the solutions of the system are the critical points of I,. Let Z be the smooth solution manifold of the unperturbed problem and TzZ is the tangent space. The critical point of I is rewritten as the form of z ＋ w, where w ∈ （TzZ）⊥. Using some properties of Iε, it is proved that there exists a critical point of I, close to the form which is a multi-bump solution.

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.

Analysis on the Economical and Intensive Utilization of Regional Construction Land in Zunyi City of Guizhou Province

Taking Zunyi City of Guizhou Province as research area, the research performed spatial dynamic analysis and elastic coefficient method to evaluate the e- conomical and intensive utilization of the regional construction land, and to discuss the decision direction for economical and intensive utilization of the regional con- struction land. The results showed that, the population based land utilization both in Zunyi and the city districts and counties indicated extensive trend. The economy- based land utilization in Zunyi City, as well as in Huichun, Zunyi and Tongzi County presented intensive trend, however the other districts and counties were in extensive tendency. It concluded that the economical and intensive utilization of construction land should be focused on the rural collective construction land, and the potential tapping of rural collective construction land in rocky desertification area should be the first priority, otherwise, the regional economic development was unbalanced be- tween different district and county in Zunyi.