Modeling of Electromagnetic Wave Propagation and Its Applicationin Virtual Test

With the development of virtual test,the computation of the effect of different weather conditions on electromagnetic wave propagation is required in many simulation systems. Firstly,this paper presents a unique point of view for computing the electromagnetic wave attenuation ratio under different weather conditions by means of an independent electromagnetic wave propagation component that can be directly implemented in virtual test, and is easy to configure and easy to reuse. We present an analysis of the principles of electromagnetic wave propagation and the algorithms designed for realization of various propagation models within the electromagnetic wave propagation component. Secondly,this paper presents a use-case analysis and outlines the design of the component,verifies the developed models under various weather conditions,and obtains equivalent values as those obtained theoretically. Finally,we build a virtual test system,verify the system in different weather conditions,and again obtain equivalent values to those obtained theoretically. The algorithms in the electromagnetic wave propagation component are developed in the C language, which substantially improves the computational speed,and meets the real-time requirements of the virtual testing platform.

Application of MSPCA to Sensor Fault Diagnosis

A multiscale principal component analysis method is proposed for sensor fault detection and identification. After decomposition of sensor signal by wavelet transform, the coarse-scale coefficients from the sensors with strong correlation are employed to establish the principal component analysis model. A moving window is designed to monitor data from each sensor using the model. For the purpose of sensor fault detection and identification, the data in the window is decomposed with wavelet transform to acquire the coarse-scale coefficients firstly, and the square prediction error is used to detect the failure. Then the sensor validity index is introduced to identify faulty sensor, which provides a quantitative identifying index rather than qualitative contrast given by the approach with contribution. Finally, the applicability and effectiveness of the proposed method is illustrated by sensors of industrial boiler.

Application of Total Least Square Algorithm in Static Decoupling of NGMIMU

The linear coupling of Non-gyro Micro Inertial Measurement Unit (NGMIMU) is akind of system error that affects the accuracy of measurement seriously. In this article, theauthor puts forward a new linear decoupling algorithm which simultaneously considers the error ofstandard input signal and output of accelerators when the coupling parameters are calculated. TheTotal Least Square (TLS) solutions of coupling parameters own the minimum characteristic to theinput and output values. Then these parameters are used to reconstruct the outputs of acceleratorsso as to realize the decoupling. The emulation result show that the ratio of decoupling error isless than 8 percent and verify the feasibility of this algorithm.

Propagation factor of electromagnetic concentric rings Schell-model beams in non-Kolmogorov turbulence

We derive an analytical expression for the propagation factor（known as M2-factor） of electromagnetic concentric rings Schell-model（EM CRSM） beams in non-Kolmogorov turbulence by utilizing the extended Huygens–Fresnel diffraction integral formula and the second-order moments of the Wigner distribution function（WDF）. Our results show that the EM CRSM beam has advantage over the scalar CRSM beam for reducing the turbulence-induced degradation under suitable conditions. The EM CRSM beam with multi-rings far-fields in free space is less affected by the turbulence than the one with dark-hollow far-fields or the electromagnetic Gaussian Schell-model（EGSM） beam. The dependence of the M2-factor on the beam parameters and the turbulence are investigated in detail.

Data-driven prognostics and remaining useful life estimation for lithium-ion battery: A Review

As an important and necessary part in the intelligent battery management systems(BMS),the prognostics and remaining useful life(RUL)estimation for lithium-ion batteries attach more and more attractions.Especially,the data-driven approaches use only the monitoring data and historical data to model the performance degradation and assess the health status,that makes these methods flexible and applicable in actual lithium-ion battery applications.At first,the related concepts and definitions are introduced.And the degradation parameters identification and extraction is presented,as the health indicator and the foundation of RUL prediction for the lithium-ion batteries.Then,data-driven methods used for lithium-ion battery RUL estimation are summarized,in which several statistical and machine learning algorithms are involved.Finally,the future trend for battery prognostics and RUL estimation are forecasted.

Parameter Estimation of Multi-component Chirp Signal Based on Differential Evolution

Chirp signals show energy aggregation in the fractional Fourier domain(FrFD) w hich can be used to estimate the parameter of the signals. In this paper,a parameter estimation method for multi-component chirp signal w hich corrupted by w hite Gaussian noise is proposed based on the discrete fractional Fourier transform(DFrFT) and the differential evolution( DE) algorithm. The proposed algorithm uses the DE algorithm instead of the conventional fine search algorithm to detect the peak of the signals in the FrFD. The paper simulated the influence of the noise and the resolution of the proposed algorithm. The results of the simulation show the proposed method does not only improve the estimation accuracy of the peak coordinate,but also reduces time consuming.

A novel temperature-insensitive strain sensor based on tapered fiber grating

A novel temperature-insensitive strain sensor based on bandwidth demodulation of the reflected light from the tapered fiber grating is presented, which is simple and low-cost and has considerable potential particularly application for strain sensing,and with the development of the interrogation system, it can demodulate both the bandwidth and the center wavelength of the reflected light from TFG to measure strain and temperature simultaneously.

High performance heterogeneous embedded computing: a review

As increasingly widening gap of computing demand and performance in embedded computing domain,heterogeneous computing architecture which delivers better performance as well as lower power in limited size is gaining more and more attention. At first,the heterogeneous computing model is presented. And the different tightly coupled single chip heterogeneous architectures and their application domain are introduced. Then,task partitioning methods are described. Several programming model technology are analyzed and discussed. Finally,main challenges and future perspective of High Performance Embedded Computing(HPEC) are summarized.

Measurement of spatial object's exterior attitude based on linear CCD

It is difficult to realize real-time measurement of exterior attitude by the traditional systems based on the area image sensor which have conflict between speed and accuracy. The subsystem for three-dimensional （3D） coordinate reconstruction of point target （S3DCRPT） which is composed of three one-dimensional （1D） cameras based on linear charge-coupled device （CCD） can determine the distant light spots＇ spatial position. The attitude angle of the measured object is determined by the spatial solution while the coordinate reconstruction is separately carried on by the S3DCRPT with some point cooperation targets （PCTs） on the measured object. A new optical system is designed to solve the interference problem with one-to-one relationship between the PCTs and the S3DCRPT optical subsystems, which improves the measurement accuracy and saves space. The mathematical model of the attitude measurement is established, and partial and global calibrations are realized for the multi-camera attitude measurement system. The test results show the feasibility of the exterior attitude measurement based on linear CCD.

A novel pose and illumination robust face recognition with a single training image per person algorithm

In the real-world application of face recognition system, owing to the difficulties of collecting samples or storage space of systems, only one sample image per person is stored in the system, which is so-called one sample per person problem. Moreover, pose and illumination have impact on recognition performance. We propose a novel pose and illumination robust algorithm for face recognition with a single training image per person to solve the above limitations. Experimental results show that the proposed algorithm is an efficient and practical approach for face recognition.

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life （RUL） estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine （RVM） is a data-driven algorithm used to estimate a battery＇s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery＇s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.

Novel optical fiber current sensor based on magnetic fluid

A novel fiber optic Fabry-Perot（F-P） current sensor is developed based on magnetic fluid as the medium in F-P interference cavity.A signal demodulation method based on slanted fiber Bragg grating（FBG） wavelength measurement system is proposed.Theory and principle of electromagnetic-controlled refractive index of the magnetic fluid is described,as well as the structure of the sensor system.Preliminary experiments are carried out,and the results indicate that there is a fairly good linearity of the measurement characteristic.The thickness of magnetic fluid film and initial concentration will affect the measurement results.