Application of the blind source separation method to feature extraction of machine sound signals

As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semi-anechoic chamber demonstrate the effectiveness of the presented methods .     

A calibration method for the energy-integrating function in photovoltaic system monitoring

Solar radiation has an irregularly varying factor due to a basic day and night cycle and climatic conditions. For such conditions a data sampling interval is important to ensure the accuracy of energy monitoring in a photovoltaic system. While treating a system monitoring equipment as a black box. the author has developed the method of calibrating an energy-integrating function. At first for various input waveforms, the relationship between sampling intervals and quasi-integration outputs have been examined by trapezoidal rule. In the numerical simulation the phases of the sampling clock also are considered Then it is concluded that a sampling interval can be inspected through outside observation only by using a rectangular single pulse. By applying the pulse to the energy-integrating process, two kinds of integrated outputs can be obtained for different sampling phases. The calculated difference between both outputs can uniquely give the sampling interval being inspected. Conditions to ensure measuring accuracy are discussed and the validity of this method has been demonstrated experimentally. Practical calibrating procedures also are proposed for the integrating function of PV system monitoring.     

整合资源调整职能促进发展--关于陕煤技校改革发展的思考

经过60年的发展,陕西煤炭技工院校形成了以服务煤业为特色的专业结构和办学模式,为祖国煤炭发展做出了不可替代的贡献。近年来,由于煤炭市场遇冷,行业技校吸引力持续减弱,如何走出困境,重铸辉煌,作者从整合职教资源,调整管理职能,转换办学模式的角度进行了分析思考,或许能给陕煤技校改革发展提供一些有益参考。     

企业各软件的数据共享和整合分析浅谈

本文主要根据大多数企业存在各种软件各自独立运行,各软件之间数据不能相互共享,一些旧软件或不适用的软件与目前主流操作系统不兼容等问题,分析问题并用接口和整合方法解决,此平台利用云计算技术,解决大多数企业建设信息平台投资大的问题,同时方便企业使用和维护.     

TESTS FOR FRACTIONAL INTEGRATION:A MONTE CARLO INVESTIGATION

Abstract. The performance of the Geweke-Porter-Hudak (GPH) test, the modified rescaled range (MRR) test and two Lagrange multiplier (LM) type tests for fractional integration in small samples is examined using Monte Carlo methods. Both the GPH and MRR tests are found to be robust to moderate autoregressive moving-average components, autoregressive conditional heteroskedasticity effects and shifts in the variance. However, these two tests are sensitive to large autoregressive moving-average components and shifts in the mean. It is also found that the LM tests are sensitive to deviations from the null hypothesis. As an illustration, the GPH test is applied to two economic data series.     

网络环境下的信息技术课程整合教学

基于信息技术的课程整合作为小学信息技术教学的发展方向之一,越来越受到信息技术科任老师的关注。本文对基于网络环境的信息技术课程整合优势、网络环境在课程整合中的利用策略、网络环境下的信息技术课程整合的核心活动等方面进行了分析,探讨了如何利用网络技术促进信息技术课与其它学科的整合。     

DCS系统在新型干法水泥生产线中的应用

介绍了在新型干法水泥生产线中全厂全线采用DCS系统进行设计应用,对企业降低费用、提高效率和生产率具有十分重大意义。     

αB-spline: a linear singular blending B-spline

A linear singular blending (LSB) technique can enhance the shape—control capability of the B-spline. This capability is derived from the blending parameters defined at the B-spline control vertices and blends LSB line segments or bilinear surface patches with the B-spline curve or surface. Varying the blending parameters between zero and unity applies tension for reshaping. The reshaped curve or surface retains the same smoothness properties as the original B-spline; it possesses the same strict parametric continuities. This is different from the -spline, which introduces additional control to the B-spline by imposing geometrical continuities to the joints of curve segments or surface patches. For applications in which strict parametric continuities cannot be compromised, LSB provides an intuitive way to introduce tension to the B-spline.     

基于传统文化艺术资源整合基础上的山东区域特色动漫设计研究

山东区域的传统文化艺术资源主要包括:民间传说、寓言故事、文学作品、文物古迹、传统艺术等内容。     

Combining feature spaces for classification

In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous sources in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis-Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel matrix.