摘要：
Large structure modal parameter estimation has always been an important research content. Its core content is to obtain the eigenvalues of large-scale structural system. Based on the environmental excitaion, to obtain high precision system free response is particularly important. This paper presents a random reduction de-noising-ARMA method(RDT-ARMA method). By analyzing the characteristics of free response root and imaginary part under the condition of health and damage, the noise modal of free response obtained by stochastic reduction method can be effectively eliminated to obtain a more realistic system free response signal. Based on this, the modal of large structural system is identified by ARMA method. And through the actual acquisition of the WSN signal processing measured acceleration, indicating the effectiveness of the proposed method can be proved.
关键词：
Multiview data;Partial least squares;Regression
摘要：
In practice, multiple distinct features are need to comprehensively analyze complex samples. In machine learning, data set obtained with a feature extractor is referred as a view. Most of data used in practics are collected with various feature extractors. It is practical to assume that an individual view is unlikely to be sufficient for effective analyzing the property of the sample. Therefore, integration of multiview information is both valuable and necessary. But, traditional partial least squares is proposed for single view high dimensional data modeling,which is invalid for multiview data. In this paper, multiveiw partial least squares is proposed. This model finds a series of direction vectors which guarantee covariance between response and weighted component reach maximum as well as pairwise correlation of component. We then proposed an algorithm for multiview partial least squares. Convergence and bound discussion are also given. Experiments demonstrate that proposed multiview partial least squares is an effective and promising algorithm for practical applications.
摘要：
In this paper, the novel method for free response signal of large-scale system is proposed. On the basis of random decrement method on the basic idea, two key parameters are discussed. One is the length of the system's output signal N, the other is the reference value of the amplitude A. in order to obtain more precise free response signal of system, using the result of eigenvalues analysis to modify the primary free response signal. Processing the measured acceleration from real system, this novel method can get approximate free response signal.
摘要：
Compressed Sensing (CS) is an emerging theory which can sample the sparse signal or compressible signal via sub-Nyquist sampling rate and reconstruct the original signal with small amount of measurements. Since speech signal is sparse in Discrete Cosine Transform (DCT) domain, a kind of adaptive Bayesian Compressed Sensing (BCS) based on speech signal is proposed in this paper. In the one hand, our proposed method exploits the difference of energy within different speech frame to allot measurements adaptively for each speech frame aim to promote the quality of recovery speech signal. In the other hand, the position information of sparse coefficient in each speech frame is also utilized by our proposed method to recover its neighboring speech frame for reducing the recovery time of speech signal. The experimental results prove that our proposed method is surely effective and practical.
摘要：
Due to the poor spectrum utilization in time and space, the spectrum in cognitive radio is not only sparse but clusters in blocks in many situations. Based on the fact, a novel Bayesian compressed sensing algorithm for wideband spectrum sensing is proposed. In our proposed framework, firstly, the original signal should be sampled with sub-Nyquist sampling rate. Secondly, the clustering of the spectrum molded by many block structures is also utilized to increase the accuracy of spectrum sensing. Lastly, with a variational Bayesian inference, the experimental results show the validity and advantage of our proposed approach.
摘要：
Conventional surveying method could not solve the problem of detecting building's whole deformation in a short time, including in particular flatness and inclination values of the surface of the building. Aiming at this problem, this paper introduce a method to solve it based on terrestrial laser point cloud, first step is fitting point-cloud datum to point-cloud model for reducing random errors in original point cloud, and then calculating the distance between original points to fitting model on homogeneous region and upon determining root mean square error about this distance as a flatness index. The second step, calculating the angle between model and reference plane (such as a horizontal or vertical), and which will be used as the inclination value of a building surface. Project tests proved the effectiveness of this method.
摘要：
Calibration model transfer is an important issue in infrared spectra analysis. For identical sample, spectra collected with master and slave spectrometers share same components. In the sense of mathematics, they share same basis. If the basis and corresponding coefficient matrices can be obtained, the model transfer can be efficiently realized. On the other hand, the performance of calibration model transfer method will degrade if there are outliers and noise in samples. In this paper, a robust calibration transfer model is proposed. Cauchy estimator are employed to learn same basis shared by master and slave spectra robustly. Transformation matrix can be calculated with the two corresponding coefficient matrices. Slave testing spectra are represented with the common basis and corresponding coefficients are then transferred using the transformation matrix. The slave testing spectra can be transferred using common basis and the corrected coefficients. The convergence property and bound of proposed model are also discussed. Extensive experiments are conducted, experimental results demonstrate that our robust calibration transfer model can generally outperform the existing methods.
摘要：
Based on the fact that the spectrum in cognitive radio system is typically sparse, a novel wideband spectrum sensing algorithm is proposed taking advantage of Bayesian compressed sensing. Under our proposed scheme, the signal of interest can be sampled at sub-Nyquist rate so relaxing the sampling tension of front-end hardware. Furthermore, the block structure of the spectrum molded by a set of double-level binary tree is also exploited in the spectrum sensing process so promoting the spectrum sensing accuracy with lower sampling rate. Taking the MCMC sampling method as efficient inference, experimental results show the validity of our proposed method.
会议名称：
Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)
会议时间：
October 2014
会议地点：
Wuhan, China
会议主办单位：
Huazhong Univ Sci & Technol
会议论文集名称：
Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)
关键词：
Complicated systems;Grey system;Grey state model;Modal Control
摘要：
As we all known that complicated system is too difficult to be identified. A system designed here may be expected to get good control effects. Firstly, grey state modeling and modal decomposition is proposed. Secondly, an approach to modal control, which is to control the complicated system's behavior is designed. The applicable field of modal control systems is rather far and wide especially for varieties of multivariable complicated systems.
期刊：
WIT Transactions on Engineering Sciences,2014年87:477-487 ISSN：1743-3533
作者机构：
[Long Zhou; Weizhen Chen] School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, China;[Jinglu Hu; Weizhen Chen] Graduate School of Information, Production and Systems, Waseda University, Fukuoka-Ken, Japan
会议名称：
2013 3rd International Conference on Advanced Materials and Information Technology Processing (AMITP 2013)
作者机构：
[Chen W.Z.; Liang L.J.] School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China
会议名称：
4th International Conference on Materials Science and Information Technology, MSIT 2014
会议时间：
14 June 2014 through 15 June 2014
关键词：
Agriculture output value;Grey forecasting model;Multifactor;Poor information
摘要：
In this paper, a multifactor grey forecasting model of agriculture output value is proposed. Agriculture output value is very important in the country. It can help us to well know the situation of agriculture, and to modify the policy of agriculture. When the data change greatly, one-factor forecasting model may not be get good results. Grey forecasting model with multifactor would be better. From the simulation, this multifactor grey forecasting model with gross and total farm output value can get better effects.
