To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from patches.RGB crack images and their corresponding masks o...To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from patches.RGB crack images and their corresponding masks obtained from a public dataset are cropped into patches of 256 square pixels that are classified with a pre-trained deep convolution neural network,the true positives are segmented,and crack properties are extracted using two different methods.The first method is primarily based on active contour models and level-set segmentation and the second method consists of the domain adaptation of a mathematical morphology-based method known as FIL-FINDER.A statistical test has been performed for the comparison of the stated methods and a database prepared with the more suitable method.An advanced convolution neural network-based multi-output regression model has been proposed which was trained with the prepared database and validated with the held-out dataset for the prediction of crack-length,crack-width,and width-uncertainty directly from input image patches.The pro-posed model has been tested on crack patches collected from different locations.Huber loss has been used to ensure the robustness of the proposed model selected from a set of 288 different variations of it.Additionally,an ablation study has been conducted on the top 3 models that demonstrated the influence of each network component on the pre-diction results.Finally,the best performing model HHc-X among the top 3 has been proposed that predicted crack properties which are in close agreement to the ground truths in the test data.展开更多
Biological data in fishery ecology have complex structures and are highly heterogeneous.Catch per unit effort(CPUE)estimated from fishery-dependent data are often used to characterize abundance indices(AI)of fish spec...Biological data in fishery ecology have complex structures and are highly heterogeneous.Catch per unit effort(CPUE)estimated from fishery-dependent data are often used to characterize abundance indices(AI)of fish species,which is critical in fish stock assessment.However,additional considerations need to be undertaken to ensure robust estimation because of the latently complicated structures in fishery-dependent data.Here,we elaborated the process of constructing multi-output artificial neural network models to standardize CPUE for heterogeneous fishing operations and applied it to the skipjack tuna(Katsuwonus pelamis)in the western and central Pacific Ocean(WCPO).Seasonal,spatial,and environmental factors were input variables,and the CPUE of four types of skipjack tuna fisheries were set as output variables.The optimal structure for multi-output neural network was evaluated by systematic comparison in 100 runs hold-out cross-validation.The results showed that the final multi-output neural network model with high accuracy can predict the spatial and temporal trends of skipjack tuna abundance.展开更多
In this paper, the definition of multl-output partially Bent functions is presented and some properties are discussed. Then the relationship between multi-output partially Bent functions and multi-output Bent function...In this paper, the definition of multl-output partially Bent functions is presented and some properties are discussed. Then the relationship between multi-output partially Bent functions and multi-output Bent functions is given in Theorem 4, which includes Walsh spectrum expression and function expression. This shows that multi-output partially Bent functions and multi-output Bent functions can define each other in principle. So we obtain the general method to construct multi-output partially Bent functions from multi-output Bent functions.展开更多
Both auto-power spectrum and cross-power spectrum need to be controlled in multi-input multi-output （MIMO） random vibration test. During the control process with the difference control algorithm （DCA）, a lower tri...Both auto-power spectrum and cross-power spectrum need to be controlled in multi-input multi-output （MIMO） random vibration test. During the control process with the difference control algorithm （DCA）, a lower triangular matrix is derived from Cholesky decomposition of a reference spectrum matrix. The diagonal elements of the lower triangular matrix （DELTM） may become negative. These negative values have no meaning in physical significance and can cause divergence of auto-power spectrum control. A proportional root mean square control algorithm （PRMSCA） provides another method to avoid the divergence caused by negative values of DELTM, but PRMSCA cannot control the cross-power spectrum. A new control algorithm named matrix power control algorithm （MPCA） is proposed in the paper. MPCA can guarantee that DELTM is always positive in the auto-power spectrum control. MPCA can also control the cross-power spectrum. After these three control algorithms are analyzed, three-input three-output random vibration control tests are implemented on a three-axis vibration shaker. The results show the validity of the proposed MPCA.展开更多
A control method for Multi-Input Multi-Output（MIMO） non-Gaussian random vibration test with cross spectra consideration is proposed in the paper. The aim of the proposed control method is to replicate the specified ...A control method for Multi-Input Multi-Output（MIMO） non-Gaussian random vibration test with cross spectra consideration is proposed in the paper. The aim of the proposed control method is to replicate the specified references composed of auto spectral densities, cross spectral densities and kurtoses on the test article in the laboratory. It is found that the cross spectral densities will bring intractable coupling problems and induce difficulty for the control of the multioutput kurtoses. Hence, a sequential phase modification method is put forward to solve the coupling problems in multi-input multi-output non-Gaussian random vibration test. To achieve the specified responses, an improved zero memory nonlinear transformation is utilized first to modify the Fourier phases of the signals with sequential phase modification method to obtain one frame reference response signals which satisfy the reference spectra and reference kurtoses. Then, an inverse system method is used in frequency domain to obtain the continuous stationary drive signals. At the same time, the matrix power control algorithm is utilized to control the spectra and kurtoses of the response signals further. At the end of the paper, a simulation example with a cantilever beam and a vibration shaker test are implemented and the results support the proposed method very well.展开更多
Noises always disturb the control effect of an environment test especially in multi-input multi-output(MIMO) systems. If the frequency response function matrices are ill-conditioned, the noises in the driving forces w...Noises always disturb the control effect of an environment test especially in multi-input multi-output(MIMO) systems. If the frequency response function matrices are ill-conditioned, the noises in the driving forces will be amplified and the response spectral lines may awfully exceed their tolerances. Most of the major biases between the response spectra and the reference spectra are produced by the amplified noises. However, ordinary control algorithms can hardly reduce the level of noises. The influences of the noises on both the auto- and cross-power spectra are analyzed in this paper. As a conventional frequency domain method on the inverse problem, the Tikhonov filter is adopted in the environment test to suppress the exceeding spectral lines. By altering regularization parameters gradually, the auto-power spectra can be improved in a closed control loop. Instead of using the traditional way of selecting regularization parameters, we observe the coherence change to estimate noise eliminations. Incidentally, the requirement of coherence control can be realized. The errors of the phase are then studied and a phase control algorithm is introduced at the end as a supplement of cross-power spectra control. The Tikhonov filter and the proposed phase control algorithm are tested numerically and experimentally. The results show that the noises in the vicinity of lightly damped resonant peaks are more stubborn. The response spectra are able to be greatly improved by the combination of these two methods.展开更多
Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs l...Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs least square support vector regression （LSSVR） is applied to the compensation of on-board self-tuning model of aero-engine, and particle swarm optimization （PSO） is used to the kernels selection of multi-outputs LSSVR. The method need not reconstruct the model of aero-engine because of the differences in the individuals of the same type engines and engine degradation after use. The concrete steps for the application of the method are given, and the simulation results show the effectiveness of the algorithm.展开更多
Orthomorphic permutations have good characteristics in cryptosystems. In this paper, by using of knowledge about relation between orthomorphic permutations and multi-output functions, and conceptions of the generalize...Orthomorphic permutations have good characteristics in cryptosystems. In this paper, by using of knowledge about relation between orthomorphic permutations and multi-output functions, and conceptions of the generalized Walsh spectrum of multi-output functions and the auto-correlation function of multi-output functions to investigate the Walsh spectral characteristics and the auto-correlation function characteristics of orthormophic permutations, several results are obtained.展开更多
Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-...Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.展开更多
Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensin...Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted.Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.展开更多
:Multicast-based quantum teleportation(QT) is extensively used in quantum information transmission where a sender sends different information to multiple receivers at the large distance through the quantum entangled c...:Multicast-based quantum teleportation(QT) is extensively used in quantum information transmission where a sender sends different information to multiple receivers at the large distance through the quantum entangled channel. In this paper, we introduce the multi-output QT scheme, which deals with the situation that the synchronous transfer of the arbitrary m-and(m+1)-qubit GHZ-class states from one sender to two receivers. Notably, the requirement about synchronous diverse information transmission is satisfied in our scheme with high efficiency. Moreover, we demonstrate the implementation of the special case of above quantum multi-output teleportation scheme on a sixteenqubit quantum computer and a 32-qubit simulator provided by IBM quantum platform, then discuss it in four types of noisy environments, and calculate the fidelities of the output states.展开更多
This paper for the first time improved a Robust Multi-Output machine learning regression model for seismic hazard zoning of Turkey, Iraq and Iran using constructed 3-D shear-wave velocity(Vs), seismic tomography datas...This paper for the first time improved a Robust Multi-Output machine learning regression model for seismic hazard zoning of Turkey, Iraq and Iran using constructed 3-D shear-wave velocity(Vs), seismic tomography dataset model for the crust and uppermost mantle beneath the study area. The focus of this paper’s opportunity is to develop a scientific framework leveraging machine learning that will ultimately provide the rapid and more complete characterization of earthquake properties. This work can be targeted at improving the seismic hazard zones system ability to detect and associate seismic signals, or at estimating other seismic characteristics(crust acceleration and crust energy) while traditionally, methods cannot monitor the earthquakes system. This work has derived some physical equations for extraction of many variables as inputs for our developed machine learning model based on a reliable understanding of the tomography data to physical variables by preparing huge dataset from different physical conditions of crust. We have extracted the velocity values of the shear waves from the original NETCDF file, which contains the S velocity values for every one km of the depths of the crust for the study area from one km down to the uppermost mantle beneath the Middle East. For the first time, this study calculated new seismic hazard parameter called Peak Crust Acceleration(PCA) for seismic hazard analysis by considering the transmitted initial seismic energy through the Earthy wrote in python language ’s crust layers from hypocenter. All machine learning algorithms in this studusing anaconda platform the open-source Individual Edition(Distribution).展开更多
A form of iterative learning control (ILC) is used to update the set-point for the local controller. It is referred to as set-point-related (SPR) indirect ILC. SPR indirect ILC has shown excellent performance: as a su...A form of iterative learning control (ILC) is used to update the set-point for the local controller. It is referred to as set-point-related (SPR) indirect ILC. SPR indirect ILC has shown excellent performance: as a supervision module for the local controller, ILC can improve the tracking performance of the closed-loop system along the batch direction. In this study, an ILC-based P-type controller is proposed for multi-input multi-output (MIMO) linear batch processes, where a P-type controller is used to design the control signal directly and an ILC module is used to update the set-point for the P-type controller. Under the proposed ILC-based P-type controller, the closed-loop system can be transformed to a 2-dimensional (2D) Roesser s system. Based on the 2D system framework, a sufficient condition for asymptotic stability of the closed-loop system is derived in this paper. In terms of the average tracking error (ATE), the closed-loop control performance under the proposed algorithm can be improved from batch to batch, even though there are repetitive disturbances. A numerical example is used to validate the proposed results.展开更多
Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Mu...Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom(DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.展开更多
This paper presents the design of decentralized repetitive control （RC） for multi-input multi-output （MIMO） systems. An optimization method is used to obtain a RC compensator that ensures system stability and good...This paper presents the design of decentralized repetitive control （RC） for multi-input multi-output （MIMO） systems. An optimization method is used to obtain a RC compensator that ensures system stability and good tracking performance. The designed compensator is in the form of a stable, low order, and causal filter, in which the compensator can be implemented separately without being merged with the RC internal model. This will reduce complexity in the implementation. Simulation results and comparison study are given to demonstrate the effectiveness of the proposed design. The novelty of design is also verified in experiments on a 2 degrees of freedom （DOF） robot.展开更多
In this paper,the application of extraction principle for logic function minimiza-tion to multi-output case is studied.A defect in original algorithm in dealing with multi-outputextrema is made up,and on this base,thr...In this paper,the application of extraction principle for logic function minimiza-tion to multi-output case is studied.A defect in original algorithm in dealing with multi-outputextrema is made up,and on this base,three kinds of less-than terms in different conditions aredefined.In addition,three kinds of generalized definition of less-than terms are given,so as to findout more efficiently the covers with minimal number of terms and irredundant function outputs.This work makes the multi-output extraction principle closer to perfection.An algorithm basedon the work is presented.展开更多
Recently, there have been more debates on the methods of measuring efficiency. The main objective of this paper is to make a sensitivity analysis for different frontier models and compare the results obtained from the...Recently, there have been more debates on the methods of measuring efficiency. The main objective of this paper is to make a sensitivity analysis for different frontier models and compare the results obtained from the different methods of estimating multi-output frontier for a specific application. The methods include stochastic distance function frontier, stochastic ray frontier, and data envelopment analysis. The stochastic frontier regressions with and without the inefficiency effects model are also com-pared and tested. The results indicate that there are significant correlations between the results obtained from the alternative estimation methods.展开更多
This paper discusses the best affine approach （BAA） of multi-output m-valued logical functions. First, it gives the spectra of rate of accordance between multi-output m-valued logical functions and their affine func...This paper discusses the best affine approach （BAA） of multi-output m-valued logical functions. First, it gives the spectra of rate of accordance between multi-output m-valued logical functions and their affine functions, then analyzes the BAA of multi-output m-valued logical functions and finally gives the spectral characteristics of BAA of multi-output m-valued logical functions.展开更多
A double-input–multi-output linearized system is developed using the state-space method for dynamic analysis of methanation process of coke oven gas.The stability of reactor alone and reactor with feed-effluent heat ...A double-input–multi-output linearized system is developed using the state-space method for dynamic analysis of methanation process of coke oven gas.The stability of reactor alone and reactor with feed-effluent heat exchanger is compared through the dominant poles of the system transfer functions.With single or double disturbance of temperature and CO concentration at the reactor inlet,typical dynamic behavior in the reactor,including fast concentration response,slow temperature response and inverse response,is revealed for further understanding of the counteraction and synergy effects caused by simultaneous variation of concentration and temperature.Analysis results show that the stability of the reactor loop is more sensitive than that of reactor alone due to the positive heat feedback.Remarkably,with the decrease of heat exchange efficiency,the reactor system may display limit cycle behavior for a pair of complex conjugate poles across the imaginary axis.展开更多
The FRF estimator based on the errors-in-variables （EV） model of multi-input multi-output （MIMO） system is presented to reduce the bias error of FRF HI estimator. The FRF HI estimator is influenced by the noises i...The FRF estimator based on the errors-in-variables （EV） model of multi-input multi-output （MIMO） system is presented to reduce the bias error of FRF HI estimator. The FRF HI estimator is influenced by the noises in the inputs of the system and generates an under-estimation of the true FRF. The FRF estimator based on the EV model takes into account the errors in both the inputs and outputs of the system and would lead to more accurate FRF estimation. The FRF estimator based on the EV model is applied to the waveform replication on the 6-DOF （degree-of-freedom） hydraulic vibration table. The result shows that it is favorable to improve the control precision of the MIMO vibration control system.展开更多
文摘To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from patches.RGB crack images and their corresponding masks obtained from a public dataset are cropped into patches of 256 square pixels that are classified with a pre-trained deep convolution neural network,the true positives are segmented,and crack properties are extracted using two different methods.The first method is primarily based on active contour models and level-set segmentation and the second method consists of the domain adaptation of a mathematical morphology-based method known as FIL-FINDER.A statistical test has been performed for the comparison of the stated methods and a database prepared with the more suitable method.An advanced convolution neural network-based multi-output regression model has been proposed which was trained with the prepared database and validated with the held-out dataset for the prediction of crack-length,crack-width,and width-uncertainty directly from input image patches.The pro-posed model has been tested on crack patches collected from different locations.Huber loss has been used to ensure the robustness of the proposed model selected from a set of 288 different variations of it.Additionally,an ablation study has been conducted on the top 3 models that demonstrated the influence of each network component on the pre-diction results.Finally,the best performing model HHc-X among the top 3 has been proposed that predicted crack properties which are in close agreement to the ground truths in the test data.
基金supported by the National Key R&D Program of China(No.2023YFD2401303).
文摘Biological data in fishery ecology have complex structures and are highly heterogeneous.Catch per unit effort(CPUE)estimated from fishery-dependent data are often used to characterize abundance indices(AI)of fish species,which is critical in fish stock assessment.However,additional considerations need to be undertaken to ensure robust estimation because of the latently complicated structures in fishery-dependent data.Here,we elaborated the process of constructing multi-output artificial neural network models to standardize CPUE for heterogeneous fishing operations and applied it to the skipjack tuna(Katsuwonus pelamis)in the western and central Pacific Ocean(WCPO).Seasonal,spatial,and environmental factors were input variables,and the CPUE of four types of skipjack tuna fisheries were set as output variables.The optimal structure for multi-output neural network was evaluated by systematic comparison in 100 runs hold-out cross-validation.The results showed that the final multi-output neural network model with high accuracy can predict the spatial and temporal trends of skipjack tuna abundance.
基金Supported by State Key Laboratory of InformationSecurity Opening Foundation(01-02) the Doctorate Foundation ofInstitute of Information Engineering (YP20014401)HenanInno-vation Project for University Prominent Research Talents(2003KJCX008)
文摘In this paper, the definition of multl-output partially Bent functions is presented and some properties are discussed. Then the relationship between multi-output partially Bent functions and multi-output Bent functions is given in Theorem 4, which includes Walsh spectrum expression and function expression. This shows that multi-output partially Bent functions and multi-output Bent functions can define each other in principle. So we obtain the general method to construct multi-output partially Bent functions from multi-output Bent functions.
基金National Natural Science Foundation of China （10972104） The Fundamental Research Funds for NUAA（NS2010007）
文摘Both auto-power spectrum and cross-power spectrum need to be controlled in multi-input multi-output （MIMO） random vibration test. During the control process with the difference control algorithm （DCA）, a lower triangular matrix is derived from Cholesky decomposition of a reference spectrum matrix. The diagonal elements of the lower triangular matrix （DELTM） may become negative. These negative values have no meaning in physical significance and can cause divergence of auto-power spectrum control. A proportional root mean square control algorithm （PRMSCA） provides another method to avoid the divergence caused by negative values of DELTM, but PRMSCA cannot control the cross-power spectrum. A new control algorithm named matrix power control algorithm （MPCA） is proposed in the paper. MPCA can guarantee that DELTM is always positive in the auto-power spectrum control. MPCA can also control the cross-power spectrum. After these three control algorithms are analyzed, three-input three-output random vibration control tests are implemented on a three-axis vibration shaker. The results show the validity of the proposed MPCA.
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutionsthe Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX17_0234)
文摘A control method for Multi-Input Multi-Output（MIMO） non-Gaussian random vibration test with cross spectra consideration is proposed in the paper. The aim of the proposed control method is to replicate the specified references composed of auto spectral densities, cross spectral densities and kurtoses on the test article in the laboratory. It is found that the cross spectral densities will bring intractable coupling problems and induce difficulty for the control of the multioutput kurtoses. Hence, a sequential phase modification method is put forward to solve the coupling problems in multi-input multi-output non-Gaussian random vibration test. To achieve the specified responses, an improved zero memory nonlinear transformation is utilized first to modify the Fourier phases of the signals with sequential phase modification method to obtain one frame reference response signals which satisfy the reference spectra and reference kurtoses. Then, an inverse system method is used in frequency domain to obtain the continuous stationary drive signals. At the same time, the matrix power control algorithm is utilized to control the spectra and kurtoses of the response signals further. At the end of the paper, a simulation example with a cantilever beam and a vibration shaker test are implemented and the results support the proposed method very well.
基金supported by the Fundamental Research Funds for the Central Universities (No. NS2015008)the corresponding work was performed in the State Key Laboratory of Mechanics and Control of Mechanical Structures
文摘Noises always disturb the control effect of an environment test especially in multi-input multi-output(MIMO) systems. If the frequency response function matrices are ill-conditioned, the noises in the driving forces will be amplified and the response spectral lines may awfully exceed their tolerances. Most of the major biases between the response spectra and the reference spectra are produced by the amplified noises. However, ordinary control algorithms can hardly reduce the level of noises. The influences of the noises on both the auto- and cross-power spectra are analyzed in this paper. As a conventional frequency domain method on the inverse problem, the Tikhonov filter is adopted in the environment test to suppress the exceeding spectral lines. By altering regularization parameters gradually, the auto-power spectra can be improved in a closed control loop. Instead of using the traditional way of selecting regularization parameters, we observe the coherence change to estimate noise eliminations. Incidentally, the requirement of coherence control can be realized. The errors of the phase are then studied and a phase control algorithm is introduced at the end as a supplement of cross-power spectra control. The Tikhonov filter and the proposed phase control algorithm are tested numerically and experimentally. The results show that the noises in the vicinity of lightly damped resonant peaks are more stubborn. The response spectra are able to be greatly improved by the combination of these two methods.
文摘Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs least square support vector regression （LSSVR） is applied to the compensation of on-board self-tuning model of aero-engine, and particle swarm optimization （PSO） is used to the kernels selection of multi-outputs LSSVR. The method need not reconstruct the model of aero-engine because of the differences in the individuals of the same type engines and engine degradation after use. The concrete steps for the application of the method are given, and the simulation results show the effectiveness of the algorithm.
基金Supported by State Key Laboratory of InformationSecurity Opening Foundation(01-02) .
文摘Orthomorphic permutations have good characteristics in cryptosystems. In this paper, by using of knowledge about relation between orthomorphic permutations and multi-output functions, and conceptions of the generalized Walsh spectrum of multi-output functions and the auto-correlation function of multi-output functions to investigate the Walsh spectral characteristics and the auto-correlation function characteristics of orthormophic permutations, several results are obtained.
基金supported by the National Natural Science Foundation of China(61172127)the Natural Science Foundation of Anhui Province(1408085MF121)
文摘Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.
基金Supported by the National Natural Science Foundation of China(61273160)the Fundamental Research Funds for the Central Universities(14CX06067A,13CX05021A)
文摘Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted.Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.
基金the Key Industry Projects in Shaanxi Province(Grant No.2019ZDLGY09-03,2020ZDLGY15-09)the National Natural Science Foundation of China(Grants No.61771296,61372076,61301171)+1 种基金the Natural Science Foundation of Shaanxi province(Grant No.2018JM60-53,2018JZ60-06)the 111 Project under Grant B08038。
文摘:Multicast-based quantum teleportation(QT) is extensively used in quantum information transmission where a sender sends different information to multiple receivers at the large distance through the quantum entangled channel. In this paper, we introduce the multi-output QT scheme, which deals with the situation that the synchronous transfer of the arbitrary m-and(m+1)-qubit GHZ-class states from one sender to two receivers. Notably, the requirement about synchronous diverse information transmission is satisfied in our scheme with high efficiency. Moreover, we demonstrate the implementation of the special case of above quantum multi-output teleportation scheme on a sixteenqubit quantum computer and a 32-qubit simulator provided by IBM quantum platform, then discuss it in four types of noisy environments, and calculate the fidelities of the output states.
文摘This paper for the first time improved a Robust Multi-Output machine learning regression model for seismic hazard zoning of Turkey, Iraq and Iran using constructed 3-D shear-wave velocity(Vs), seismic tomography dataset model for the crust and uppermost mantle beneath the study area. The focus of this paper’s opportunity is to develop a scientific framework leveraging machine learning that will ultimately provide the rapid and more complete characterization of earthquake properties. This work can be targeted at improving the seismic hazard zones system ability to detect and associate seismic signals, or at estimating other seismic characteristics(crust acceleration and crust energy) while traditionally, methods cannot monitor the earthquakes system. This work has derived some physical equations for extraction of many variables as inputs for our developed machine learning model based on a reliable understanding of the tomography data to physical variables by preparing huge dataset from different physical conditions of crust. We have extracted the velocity values of the shear waves from the original NETCDF file, which contains the S velocity values for every one km of the depths of the crust for the study area from one km down to the uppermost mantle beneath the Middle East. For the first time, this study calculated new seismic hazard parameter called Peak Crust Acceleration(PCA) for seismic hazard analysis by considering the transmitted initial seismic energy through the Earthy wrote in python language ’s crust layers from hypocenter. All machine learning algorithms in this studusing anaconda platform the open-source Individual Edition(Distribution).
基金supported by National Natural Science Foundation of China (No. 60874116)Natural Science Foundation of Hebei Province (No. F2009000857)
文摘A form of iterative learning control (ILC) is used to update the set-point for the local controller. It is referred to as set-point-related (SPR) indirect ILC. SPR indirect ILC has shown excellent performance: as a supervision module for the local controller, ILC can improve the tracking performance of the closed-loop system along the batch direction. In this study, an ILC-based P-type controller is proposed for multi-input multi-output (MIMO) linear batch processes, where a P-type controller is used to design the control signal directly and an ILC module is used to update the set-point for the P-type controller. Under the proposed ILC-based P-type controller, the closed-loop system can be transformed to a 2-dimensional (2D) Roesser s system. Based on the 2D system framework, a sufficient condition for asymptotic stability of the closed-loop system is derived in this paper. In terms of the average tracking error (ATE), the closed-loop control performance under the proposed algorithm can be improved from batch to batch, even though there are repetitive disturbances. A numerical example is used to validate the proposed results.
文摘Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom(DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.
文摘This paper presents the design of decentralized repetitive control （RC） for multi-input multi-output （MIMO） systems. An optimization method is used to obtain a RC compensator that ensures system stability and good tracking performance. The designed compensator is in the form of a stable, low order, and causal filter, in which the compensator can be implemented separately without being merged with the RC internal model. This will reduce complexity in the implementation. Simulation results and comparison study are given to demonstrate the effectiveness of the proposed design. The novelty of design is also verified in experiments on a 2 degrees of freedom （DOF） robot.
文摘In this paper,the application of extraction principle for logic function minimiza-tion to multi-output case is studied.A defect in original algorithm in dealing with multi-outputextrema is made up,and on this base,three kinds of less-than terms in different conditions aredefined.In addition,three kinds of generalized definition of less-than terms are given,so as to findout more efficiently the covers with minimal number of terms and irredundant function outputs.This work makes the multi-output extraction principle closer to perfection.An algorithm basedon the work is presented.
文摘Recently, there have been more debates on the methods of measuring efficiency. The main objective of this paper is to make a sensitivity analysis for different frontier models and compare the results obtained from the different methods of estimating multi-output frontier for a specific application. The methods include stochastic distance function frontier, stochastic ray frontier, and data envelopment analysis. The stochastic frontier regressions with and without the inefficiency effects model are also com-pared and tested. The results indicate that there are significant correlations between the results obtained from the alternative estimation methods.
基金Supported by the Opening Research Foundation of the State Key Laboratory of Information Security (2005-01-02)
文摘This paper discusses the best affine approach （BAA） of multi-output m-valued logical functions. First, it gives the spectra of rate of accordance between multi-output m-valued logical functions and their affine functions, then analyzes the BAA of multi-output m-valued logical functions and finally gives the spectral characteristics of BAA of multi-output m-valued logical functions.
基金Supported by the Major Research plan of the National Natural Science Foundation of China(91334101)the National Basic Research Program of China(2009CB219906)the National Natural Science Foundation of China(21276203)
文摘A double-input–multi-output linearized system is developed using the state-space method for dynamic analysis of methanation process of coke oven gas.The stability of reactor alone and reactor with feed-effluent heat exchanger is compared through the dominant poles of the system transfer functions.With single or double disturbance of temperature and CO concentration at the reactor inlet,typical dynamic behavior in the reactor,including fast concentration response,slow temperature response and inverse response,is revealed for further understanding of the counteraction and synergy effects caused by simultaneous variation of concentration and temperature.Analysis results show that the stability of the reactor loop is more sensitive than that of reactor alone due to the positive heat feedback.Remarkably,with the decrease of heat exchange efficiency,the reactor system may display limit cycle behavior for a pair of complex conjugate poles across the imaginary axis.
基金This project is supported by Program for New Century Excellent Talents in University,China(No.NCET-04-0325).
文摘The FRF estimator based on the errors-in-variables （EV） model of multi-input multi-output （MIMO） system is presented to reduce the bias error of FRF HI estimator. The FRF HI estimator is influenced by the noises in the inputs of the system and generates an under-estimation of the true FRF. The FRF estimator based on the EV model takes into account the errors in both the inputs and outputs of the system and would lead to more accurate FRF estimation. The FRF estimator based on the EV model is applied to the waveform replication on the 6-DOF （degree-of-freedom） hydraulic vibration table. The result shows that it is favorable to improve the control precision of the MIMO vibration control system.
