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 dev...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.展开更多
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 ...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.展开更多
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 ar...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.展开更多
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. Final...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.展开更多
Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with v...Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.展开更多
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 emp...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.展开更多
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...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.展开更多
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 fram...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.展开更多
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 m...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.展开更多
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...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.展开更多
Diabetes is a significant issue in the medical field. The detection and identification of the human eye diseases caused by excessive blood sugar levels in diabetes mellitus are important. The main objective of this st...Diabetes is a significant issue in the medical field. The detection and identification of the human eye diseases caused by excessive blood sugar levels in diabetes mellitus are important. The main objective of this study is to propose a viable solution for diagnosis using fundus images. This study presents a stage by stage implementation methodology. The original fundus image is first preprocessed, then the blood vessels are segmented, and finally the features are extracted and classified. This work uses an effective way to introduce a meta-heuristic algorithm. Blood Vessel Segmentation(BVS) is vital in DR(Diabetic Retinopathy) detection;hence, this research proposes a Firefly-Optimized Frangi based Filter(FOFF). Categorizing the disease is the last procedure. The classifier K-Nearest Neighbour(KNN) has an accuracy of 91.62%, while the SVM does well with an accuracy score of 95.54%.展开更多
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 s...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.展开更多
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 se...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.展开更多
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 b...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.展开更多
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...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.展开更多
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 ...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.展开更多
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 understo...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.展开更多
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 ...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.展开更多
Nowadays,brain tumor is a serious life-threatening disease that can often be treated with risky surgeries.Various classification and segmentation methods for MR(magnetic resonance)brain images have been proposed,but t...Nowadays,brain tumor is a serious life-threatening disease that can often be treated with risky surgeries.Various classification and segmentation methods for MR(magnetic resonance)brain images have been proposed,but the expected accuracy value could not be reached so far.In this paper,we proposed a hybrid approach that includes modified fuzzy C-means and artificial neural network(ANN)classifier.It consists of five stages:(a)noise removal,(b)feature extraction,(c)feature selection,(d)classification,and(e)segmentation.Initially,a genetic optimized median filter is used to remove noise present in the input image,and then the essential features are extracted and selected using discrete wavelet transform and principle component analysis algorithms,respectively.The normal and abnormal images are classified using the ANN classifier.Finally,it is processed through a modified fuzzy C-means algorithm to segment the tumor portion separately.The proposed segmentation technique has been tested on the BRATS dataset and produces a sensitivity of 98%,Jaccard index of 97%,specificity of 98%,and accuracy of 95%.展开更多
Multi-rotor aircraft has great potential in urban traffic and military use and its noise problem has attracted more attention recently.Multi-rotor aircrafts are typically controlled by changing the rotation speeds of ...Multi-rotor aircraft has great potential in urban traffic and military use and its noise problem has attracted more attention recently.Multi-rotor aircrafts are typically controlled by changing the rotation speeds of the rotors.To reduce the noise of multiple frequency-modulated rotors,a global noise attenuation method is proposed in this study.First,the fast prediction method is used to estimate the global noise of the multirotor with different configurations online.Meanwhile,the sound field reproduction method is used to obtain the control signal of the loudspeaker array to achieve global noise attenuation.Then,the influence of array arrangement on noise reduction is analyzed in the acoustic modal domain,which reveals that different optimization models are needed to minimize the noise power or/and the noise pressure in some directions when the scale of the array is limited.Next,to improve the real-time performance of the system,the online calculation of the optimal control signal is transformed into the offline design of the optimal filter,which satisfies the target frequency-domain characteristics.Finally,the experimental results of the noise of a model quadrotor in the anechoic chamber were consistent with the predicted results.The simulation results of noise attenuation for the quadrotor show that the method proposed reduced the global noise power by about 13 dB.Moreover,the noise region radiated from the quadrotor to the ground with the boundary of 40 dB was reduced to 8.4%of that before control.展开更多
基金supported by NSFS Project for Tianyuan Mathematical Fund(No.A0324676)the Science&Technology Research Key Projects of the Ministry of Education of China(No.02131).
文摘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.
基金Supported by the Basic Research Foundation of Tsinghua Na-tional Laboratory for Information Science and Technology (TNList) the Major Program of the National Natural Science Foundation of China (No. 60496311)
文摘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.
基金supported by the National Natural Science Foundation of China(61233005)the National Basic Research Program of China(973 Program)(2014CB744200)
文摘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.
文摘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.
文摘Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.
基金the National Program on Key Basic Research Project(973 Program)(No.6132570201).
文摘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.
基金This project is supported by National Hi-tech Research and Development Program of China (863 Program, No. 2002AA412010).
文摘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.
基金Supported by the Startup Foundation of Hangzhou Dianzi University(ZX150204302002/009)the Open Project Program of the State Key Laboratory of Industrial Control Technology(Zhejiang University)National Natural Science Foundation of China(No.61374142,61273145,and 61273146)
文摘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.
基金the Innovation Fund Projects of Cooperation among Industries,Universities&Research Institutes of Jiangsu Province,China(Nos.BY2015019-11,BY2015019-20)National Natural Science Foundation of China(No.51403080)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.JUSRP51404A)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘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.
基金Science and Technology Project of State Grid Corporation of China,Scale application and benefit evaluation of typical power substitution technology considering power quality influence(52182018000H).
文摘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.
文摘Diabetes is a significant issue in the medical field. The detection and identification of the human eye diseases caused by excessive blood sugar levels in diabetes mellitus are important. The main objective of this study is to propose a viable solution for diagnosis using fundus images. This study presents a stage by stage implementation methodology. The original fundus image is first preprocessed, then the blood vessels are segmented, and finally the features are extracted and classified. This work uses an effective way to introduce a meta-heuristic algorithm. Blood Vessel Segmentation(BVS) is vital in DR(Diabetic Retinopathy) detection;hence, this research proposes a Firefly-Optimized Frangi based Filter(FOFF). Categorizing the disease is the last procedure. The classifier K-Nearest Neighbour(KNN) has an accuracy of 91.62%, while the SVM does well with an accuracy score of 95.54%.
文摘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.
基金The National Natural Science Foundation of China(No.61231002,61273266,61375028)the Ph.D.Programs Foundation of Ministry of Education of China(No.20110092130004)
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘Nowadays,brain tumor is a serious life-threatening disease that can often be treated with risky surgeries.Various classification and segmentation methods for MR(magnetic resonance)brain images have been proposed,but the expected accuracy value could not be reached so far.In this paper,we proposed a hybrid approach that includes modified fuzzy C-means and artificial neural network(ANN)classifier.It consists of five stages:(a)noise removal,(b)feature extraction,(c)feature selection,(d)classification,and(e)segmentation.Initially,a genetic optimized median filter is used to remove noise present in the input image,and then the essential features are extracted and selected using discrete wavelet transform and principle component analysis algorithms,respectively.The normal and abnormal images are classified using the ANN classifier.Finally,it is processed through a modified fuzzy C-means algorithm to segment the tumor portion separately.The proposed segmentation technique has been tested on the BRATS dataset and produces a sensitivity of 98%,Jaccard index of 97%,specificity of 98%,and accuracy of 95%.
文摘Multi-rotor aircraft has great potential in urban traffic and military use and its noise problem has attracted more attention recently.Multi-rotor aircrafts are typically controlled by changing the rotation speeds of the rotors.To reduce the noise of multiple frequency-modulated rotors,a global noise attenuation method is proposed in this study.First,the fast prediction method is used to estimate the global noise of the multirotor with different configurations online.Meanwhile,the sound field reproduction method is used to obtain the control signal of the loudspeaker array to achieve global noise attenuation.Then,the influence of array arrangement on noise reduction is analyzed in the acoustic modal domain,which reveals that different optimization models are needed to minimize the noise power or/and the noise pressure in some directions when the scale of the array is limited.Next,to improve the real-time performance of the system,the online calculation of the optimal control signal is transformed into the offline design of the optimal filter,which satisfies the target frequency-domain characteristics.Finally,the experimental results of the noise of a model quadrotor in the anechoic chamber were consistent with the predicted results.The simulation results of noise attenuation for the quadrotor show that the method proposed reduced the global noise power by about 13 dB.Moreover,the noise region radiated from the quadrotor to the ground with the boundary of 40 dB was reduced to 8.4%of that before control.
