摘要：
Feature extraction is a key problem in spectral analysis. Spectrum collected with spectrometer have latent low-rank component. If spectrum can be represented as a superposition of low-rank component and an approximation term, the spectrum feature is obtained. In this paper, a novel low-rank based infrared spectral feature extraction method is proposed. Employing a slide window to convert a single spectrum into a matrix, which can be decomposed as the superposition of a low-rank component and feature. In machine learning, nuclear norm is employed to approximate to low-rank minimization. Thus, the model can be written as a combination of the nuclear norm and an approximation term. We have proposed an efficient algorithm with singular value decomposition to the model. Solving the model, we obtain the latent low-rank component in spectrum. The feature is obtained via derivative of original and low-rank approximation. Then the quantitative analysis model is directly built with the feature. The advantage of proposed method is that extraction procedure of one spectrum is not affected by other spectrum. Extensive experiments are conducted with four public data sets and experimental results demonstrate that our proposed feature extraction method can lead to accuracy improvements over state-of-the-art methods. (C) 2017 Elsevier GmbH. All rights reserved.
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：
Maximum correntropy;Partial least squares;Regression;Robustness
摘要：
Partial least squares (PLS) has been extensively used to solve problems such as infrared quantitative analysis, economic data analysis, object tracking. PLS finds a linear regression model by projecting the predicted variables and the response to a new space. A major drawback of existing PLS methods is that regression coefficient will be affected by outliers. Thus, partial least squares experience significant performance degradation when gross outliers are presented. The problem of robust partial least squares has been relatively unexplored in Chemometrics and other related fields. In this paper, a new maximum correntropy based partial least squares is proposed to build robust model. We then proposed a solution algorithm for proposed model. Moreover, we also conducted convergence analysis to mathematically support the proposed model. Extensive experiments are conducted with four public data sets and experimental results demonstrate that our proposed regression method can lead to better accuracy to existing methods. (C) 2017 Published by Elsevier GmbH.
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：
A social network is a social structure made up of a set of social actors (such as individuals or organizations) and a set of the dyadic ties between these actors. By contrast, for the fixed time duration the size of digital video would be much bigger than that of digital sound. Consequently, providers of social network services can offer real-time chatting among users which could offer satisfactory experiences for users. As one of the most popular content-based social network services (SNS), chatting service plays an important role in current big data era. Also average data packets' transmission via networks is another significant traffic. So how to offer satisfactory Quality of Service (QoS) for users is the key problem which will be solved for SNS provider. For real time communication among users, end-to-end time delay seems to be critical in user's experience. Therefore modeling and evaluating social network systems is an important and urgent issue which offers quantitative basis of SNS with high quality for users. For social network system, the scalability and robust are important for both service provider and users under the circumstance of a large number of users. On the basis of performance evaluation of social network system of one user case, we construct the SPN model and conduct numerical analysis to discover and report the performance with the addition of users. By taking hybrid traffic containing voice and data into account, this paper constructed a Stochastic Petri Net (SPN) model for data and ON/OFF voice traffic for social network system. Then, average time delay of the system was analyzed and model-based simulation is conducted with Stochastic Petri Net Package (SPNP) 6.0. Furthermore, for different parameters of burst rate, idle rate, number of data packets, traffic load and buffer size, variation trends on average time delay are derived thereby. On the basis of the work in this paper, further research on heterogeneous objects of social network systems can be carried on. (C) 2016 Elsevier B.V. All rights reserved.
摘要：
when terrestrial laser point-cloud data are employed for monitoring the facade of a building, point-cloud data collected in different phases cannot be used directly to calculate the deforming displacement due to data points in a homogeneous region caused by inhomogeneous sampling. Aiming at this problem, a triangular patch is built for the previous point-cloud data, the distance is measured between the latter point-cloud data and the former patch in the homogeneous region, and thus the distance of the deforming displacement is determined. On this basis, the software of laser point-cloud monitoring analysis is developed and three series of experiments are designed to verify the effectiveness of the method.
