Optimal Filtering Algorithm for Stochastic 2-D FMM Ⅱ with Multiplicative Noise

A stochastic two dimensional Fornasini Marchesini’s Model Ⅱ (2 D FMM Ⅱ) with multiplicative noise is given, and a filtering algorithm for this model, which is optimal in the sense of linear minimum variance, is developed. The stochastic 2 D FMM Ⅱ with multiplicative noise can be reduced to a 1 D model, and the proposed optimal filtering algorithm for the stochastic 2 D FMM Ⅱ with multiplicative noise is obtained by using the state estimation theory of 1 D systems. An example is given to illustrate the validity of this algorithm.

Optimal Filtering Algorithm-Based Multiuser Detector for Fast Fading CDMA Systems

A multiuser detector was developed for fast fading code-division multiple-access systems by representing the channels as a system with the multiplicative noise （SMN） model and then using the known optimal filtering algorithm for the SMN for multiuser detection （MUD）. This multiuser detector allows the channel response to be stochastic in one symbol duration, which can be regarded as an effective method of MUD for fast fading CDMA systems. Performance analyses show that the multiuser detector is theoretically valid for CDMA systems over fast fading channels. Simulations show that the multiuser detector performs better than the Kalman filter-based multiuser detector with a faster convergence rate and lower bit error rate.

Optimal filtering for uncertain systems with stochastic nonlinearities, correlated noises and missing measurements

The globally optimal recursive filtering problem is studied for a class of systems with random parameter matrices,stochastic nonlinearities, correlated noises and missing measurements. The stochastic nonlinearities are presented in the system model to reflect multiplicative random disturbances, and the additive noises, process noise and measurement noise, are assumed to be one-step autocorrelated as well as two-step cross-correlated.A series of random variables is introduced as the missing rates governing the intermittent measurement losses caused by unfavorable network conditions. The aim of the addressed filtering problem is to design an optimal recursive filter for the uncertain systems based on an innovation approach such that the filtering error is globally minimized at each sampling time. A numerical simulation example is provided to illustrate the effectiveness and applicability of the proposed algorithm.

Study on Optimality of Two-Stage Estimation with ARMA Model Random Bias

The optimality of two-stage state estimation with ARMA model random bias is studiedin this paper. Firstly, the optimal augmented state Kalman filter is given; Secondly, the two-stageKalman estimator is designed. Finally, under an algebraic constraint condition, the equivalencebetween the two-stage Kalman estimator and the optimal augmented state Kalman filter is proved.Thereby, the algebraic constraint conditions of optimal two-stage state estimation in the presence ofARMA model random bias are given.

CCD signal acquisition and optimal digital denoise technology

To reduce the charge-coupled device(CCD)readout noise and improve the detection ability under low illumination and dim targets,a new low-noise CCD signal processing technology-CCD digital denoiseis gradually being employed in aerospace detection and other fields.In this study,the main readout noise of CCD detectors and its characteristics are analyzed.A CCD digital denoise system and an experimental platform are designed as well as established by using a PCIe data acquisition card.According to the characteristics of readout noise,some digital filters are analyzed and designed based on distributed kernel coefficient,and the optimal kernel coefficients are obtained through iteration.Then,CCD signal and filter model are established,and the optimal filter is designed to apply to the digital denoise system.Finally,according to the image data obtained from the system,the performance of the digital denoise system and digital filtering algorithm is evaluated and compared.At 500 kHz and 1 MHz CCD readout rates,the denoising performance of the optimal filter designed in the experiment is 16%-32%higher than that of the digital filter with kernel distribution coefficient,and 50%-60%higher than that of the traditional correlated double sampling technology.

NEW DESIGN OF ROBUST OPTIMAL ARBITRARY TIME-DELAY FILTER

Zero placement method in the frequency domain is utilized to design robust multi-hump EI optimal arbitrary time-delay filter （OATF） by placing two or more filter zeros near the system poles. A total insensitive OATF can be also achieved if the problem of insensitivity to damping errors is considered. This design strategy is easier to derive and implement. Applications in the anti-swing control of overhead cranes verify the fine performance of this strategy. A better suppression of the load vibrations is obtained using the proposed new OATF, which is more robust to the variation of the cable length.

An optimal filter based MPC for systems with arbitrary disturbances

In this study, a linear model predictive control(MPC) approach with optimal filters is proposed for handling unmeasured disturbances with arbitrary statistics. Two types of optimal filters are introduced into the framework of MPC to relax the assumption of integrated white noise model in existing approaches. The introduced filters are globally optimal for linear systems with unmeasured disturbances that have unknown statistics. This enables the proposed MPC to better handle disturbances without access to disturbance statistics. As a result, the effort required for disturbance modeling can be alleviated. The proposed MPC can achieve offset-free control in the presence of asymptotically constant unmeasured disturbances. Simulation results demonstrate that the proposed approach can provide an improved disturbance ?rejection performance over conventional approaches when applied to the control of systems with unmeasured disturbances that have arbitrary statistics.

Automatic Image Inspection of Fabric Defects Based on Optimal Gabor Filter

An effective method for automatic image inspection of fabric defects is presented. The proposed method relies on a tuned 2D-Gabor filter and quantum-behaved particle swarm optimization( QPSO) algorithm. The proposed method consists of two main steps:( 1) training and( 2) image inspection. In the image training process,the parameters of the 2D-Gabor filters can be tuned by QPSO algorithm to match with the texture features of a defect-free template. In the inspection process, each sample image under inspection is convoluted with the selected optimized Gabor filter.Then a simple thresholding scheme is applied to generating a binary segmented result. The performance of the proposed scheme is evaluated by using a standard fabric defects database from Cotton Incorporated. Good experimental results demonstrate the efficiency of proposed method. To further evaluate the performance of the proposed method,a real time test is performed based on an on-line defect detection system. The real time test results further demonstrate the effectiveness, stability and robustness of the proposed method,which is suitable for industrial production.

Optimal Allocation of Comprehensive Resources for Large-Scale Access of Electric Kiln to the Distribution Network

With the significant progress of the“coal to electricity”project,the electric kiln equipment began to be connected to the distribution network on a large scale,which caused power quality problems such as low voltage,high harmonic distortion rate,and high reactive power loss.This paper proposes a two-stage power grid comprehensive resource optimization configuration model.A multi-objective optimization solution based on the joint simulation platform of Matlab and OpenDSS is developed.The solution aims to control harmonics and optimize reactive power.In the first stage,a multi-objective optimization model is established to minimize the active network loss,voltage deviation,and equipment cost under the constraint conditions of voltage margin,power factor,and reactive power compensation capacity.Furthermore,the first stage uses a particle swarm optimization(PSO)algorithm to optimize the location and capacity of both series and parallel compensation devices in the distribution network.In the second stage,the optimal configuration model of the active power filter assumes the cost of the APF as the objective function and takes the harmonic voltage content rate,the total voltage distortion rate,and the allowable harmonic current as the constraint conditions.The proposed solution eliminates the harmonics by uniformly configuring active filters in the distribution network and centrally control harmonics at the system level.Finally,taking the IEEE33 distribution network as the object and considering the change of electric furnace permeability in the range of 20%–50%,the simulation results show that the proposed algorithm effectively reduces the distribution network’s loss,its harmonic content and significantly improve its voltage.

Improved Speech Denoising Algorithm Based on Discrete Fractional Fourier Transform

The speech signal and noise signal are the typical non-stationary signals,however the speech signa is short-stationary synchronously.Presently,the denoising methods are always executed in frequency domain due to the short-time stationarity of the speech signal.In this article,an improved speech denoising algorithm based on discrete fractional Fourier transform（DFRFT）is pre sented.This algorithm contains linear optimal filtering and median filtering.The simulation shows that it can easily eliminate the noise compared to Wiener filtering improve the signal to noise ratio（SNR）,and enhance the original speech signal.

Improved design of reconfigurable frequency response masking filters based on second-order cone programming

In order to improve the design results for the reconfigurable frequency response masking FRM filters an improved design method based on second-order cone programming SOCP is proposed.Unlike traditional methods that separately design the proposed method takes all the desired designing modes into consideration when designing all the subfilters. First an initial solution is obtained by separately designing the subfilters and then the initial solution is updated by iteratively solving a SOCP problem. The proposed method is evaluated on a design example and simulation results demonstrate that jointly designing all the subfilters can obtain significantly lower minimax approximation errors compared to the conventional design method.

Single Source Self-Screen Jamming Elimination and Target Detection for Distributed Dual Antennas Radar System

Detecting target echo in the existence of self-screen jamming is a challenging work for radar system, especially when digital radio frequency memory(DRFM) technique is employed that mixes the jamming and target echo both in spatial and time-frequency domain. The conventional way to solve this problem would suffer from performance degradation when physical target(PT) and false target(FT) are superposed in time. In this paper, we propose a new spatial filter according to the different correlation characteristic between PT and FT. The filter takes the ratio of expected signal power to expected jamming and noise power as the objective function under the constant filter modulus constraint. The optimal filter coefficients are derived with a generalized rayleigh quotient approach. Moreover, we analytically compute the target detection probability and demonstrate the applicability of the proposed method to the correlation coefficient. Monte Carlo simulations are provided to corroborate the proposed studies. Furthermore, the proposed method has simple architecture and low computation complexity, making it easily applied in modern radar system.

OPTIMAL TARGET TRAJECTORY ESTIMATION AND FILTERING USING NETWORKED SENSORS

Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear measurements and partially unknown input, which is generated by an exogenous system. In the worst case where the input is completely unknown, the exogenous dynamics is reduced to the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.

Anti-aliasing wavelet packet method for harmonic detection

A novel anti-aliasing wavelet packet transform method for harmonic detection is proposed. Aiming at the low measurement precision and poor robustness which exists in the former traditional wavelet methods for lack of the aliasing_reduction scheme, an optimal interpolation wavelet packet filter is designed according to new optimal criteria. First, the limitation of anti-aliasing on the traditional wavelet filter bank is analyzed. Second, the designed optimal interpolation filters axe denoted, and then the solution algorithm is given. This devised wavelet packet filter can seek a reasonable balance between signal preservation and aliasing reduction; it overcomes the inherent bug of traditional wavelet transforms, which rooted from just only concerning total aliasing cancellation but not aliasing-reduction in decomposition. Simulation and several comparative results indicate that the proposed method can effectively eliminate aliasing and precisely extract harmonic information.

Optimal Dependence of Performance and Efficiency of Collaborative Filtering on Random Stratified Subsampling

Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering(CF)algorithms.The effect of this subsampling on the computing time and accuracy of CF is not fully understood,and clear guidelines for selecting optimal or even appropriate subsampling levels are not available.In this paper,we present a Density-based Random Stratified Subsampling using Clustering(DRSC)algorithm in which the desired Fraction of Users Dropped(FUD)and Fraction of Items Dropped(FID)are specified,and the overall density during subsampling is maintained.Subsequently,we develop simple models of the Training Time Improvement(TTI)and the Accuracy Loss(AL)as functions of FUD and FID,based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens,Yahoo Music Rating,and Amazon Automotive data.Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods.The TTI linear regression of a CF method appears to be same for all datasets.Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only,but AL requires considering additional dataset characteristics.The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL.A simple sub-optimal approximation was found,in which the optimal AL is proportional to the optimal Training Time Reduction Factor(TTRF)for higher values of TTRF,and the optimal subsampling levels,like optimal FID/(1-FID),are proportional to the square root of TTRF.

Frequency Weighting Filter Design for Automotive Ride Comfort Evaluation

Few study gives guidance to design weighting filters according to the frequency weighting factors,and the additional evaluation method of automotive ride comfort is not made good use of in some countries.Based on the regularities of the weighting factors,a method is proposed and the vertical and horizontal weighting filters are developed.The whole frequency range is divided several times into two parts with respective regularity.For each division,a parallel filter constituted by a low-and a high-pass filter with the same cutoff frequency and the quality factor is utilized to achieve section factors.The cascading of these parallel filters obtains entire factors.These filters own a high order.But,low order filters are preferred in some applications.The bilinear transformation method and the least P-norm optimal infinite impulse response（IIR） filter design method are employed to develop low order filters to approximate the weightings in the standard.In addition,with the window method,the linear phase finite impulse response（FIR） filter is designed to keep the signal from distorting and to obtain the staircase weighting.For the same case,the traditional method produces 0.330 7 m · s^–2 weighted root mean square（r.m.s.） acceleration and the filtering method gives 0.311 9 m · s^–2 r.m.s.The fourth order filter for approximation of vertical weighting obtains 0.313 9 m · s^–2 r.m.s.Crest factors of the acceleration signal weighted by the weighting filter and the fourth order filter are 3.002 7 and 3.011 1,respectively.This paper proposes several methods to design frequency weighting filters for automotive ride comfort evaluation,and these developed weighting filters are effective.

Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods

The effects of sea surface temperature（SST） data assimilation in two regional ocean modeling systems were examined for the Yellow Sea（YS）. The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis（OSTIA） were assimilated. The National Marine Environmental Forecasting Center（NMEFC） modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University（KNU） modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error（RMSE） of the SST from 1.78°C（1.46°C） to 1.30°C（1.21°C） for the NMEFC（KNU） modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature（OISST） SST dataset. A comparison with the buoy SST data indicated a 41%（31%） decrease in the SST error for the NMEFC（KNU） modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.

A Sample-Wise Data Driven Control Solver for the Stochastic Optimal Control Problem with Unknown Model Parameters

In this work,an efficient sample-wise data driven control solver will be developed to solve the stochastic optimal control problem with unknown model parameters.A direct filter method will be applied as an online parameter estimation method that dynamically estimates the target model parameters upon receiving the data,and a sample-wise optimal control solver will be provided to efficiently search for the optimal control.Then,an effective overarching algorithm will be introduced to combine the parameter estimator and the optimal control solver.Numerical experiments will be carried out to demonstrate the effectiveness and the efficiency of the sample-wise data driven control method.

Real-Time Optimal State Estimation-Based Feedback Control for Stochastic Quantum Systems in the Non-Markovian Case

This paper studies the real-time optimal state estimation-based feedback control for twolevel stochastic quantum systems in the non-Markovian case.The system model is established by combining the time-convolutionless non-Markovian master equation and the stochastic master equation.A nonlinear filter based on the state-dependent Riccati equation is designed in order to achieve the realtime optimal estimation of quantum states.A quadratic function multiplied with an exponential term is selected as the Lyapunov function,and a continuous-time control law is deduced via the stochastic Lyapunov stability theorem to realize eigenstate feedback control based on real-time optimal state estimation.Numerical simulation results illustrate that the proposed control scheme is capable of steering the two-level quantum system from an arbitrary initial state to the desired eigenstate with a fidelity higher than 99%within a time of 3 a.u.

Multiple Nanoparticles Coupling Strategy for Enhancing Optical Filter Performance of Spectral Splitter Used in Photovoltaic/Thermal System

Selective absorptive nanofluid can pre-absorb certain sunlight wavelength that cannot be used by PV and transmits remaining sunlight to the surface of PV,which can decouple PV from the thermal receiver spatially.In order to improve the harvesting of electricity and high-temperature thermal nanofluid,it is important to design an optimal optical filter window(transmit sunlight with wavelengths of 732-1067 nm to the surface of the photovoltaic cell and absorb the remaining sunlight).However,designing optimal optical filter is facing following challenges:(1) inherently narrow selective absorptivity property of single nanoparticle;(2) simplified numerical calculation method calculating transmittance;(3) ignoring the shape of the nanoparticle.In this study,the idea of using multiple nanoparticles coupling effect to design an optical filter is proposed,which can superimpose the narrow absorption bandwidth of different nanoparticles to obtain a wide absorption bandwidth of the whole system.In addition,an improved transmission method considering light-matter interaction at air/vessel and liquid/vessel interfaces is adopted to compute the transmittance.The results calculated by improved transmission method are more accurate than widely used traditional Lambert-Beer law,which is verified by experimental test.Furthermore,the effect of nanoparticle shape on spectral transmittance is also investigated,which shows that spiny Ag can approximately extend absorbance from 400 nm to 600 nm compared to nanosphere silver.Finally,the results show that optical filter efficiency of nanofluids with multiple nanoparticles coupling(Ag,spiny Ag,ZnO,ITO) can reached up to 35%.