Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data mu...Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.展开更多
The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search sta...The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.展开更多
The physical principle of infrared imaging leads to the low contrast of the whole image,the blurring of contour and edge details,and it is also sensitive to noise.To improve the quality of infrared image and visual ef...The physical principle of infrared imaging leads to the low contrast of the whole image,the blurring of contour and edge details,and it is also sensitive to noise.To improve the quality of infrared image and visual effect,an adaptive weighted guided filter(AWGF) for infrared image enhancement algorithm was proposed.The core idea of AWGF algorithm is to propose an adaptive strategy to update the weights of guided filter(GF) parameters,which not only improves the accuracy of regularization parameter estimation in GF theory,but also achieves the purpose of removing infrared image noise and improving its detail contrast.A large number of real infrared images were used to verify AWGF algorithm,and good experimental results were obtained.Compared with other guided filtering algorithms,the halo phenomenon at the edge of infrared images processed by the AWGF algorithm is significantly avoided,and the evaluation parameter values of information entropy(IE),average gradient(AG),and moment of inertia(MI)are relatively high.This shows that the quality of infrared image processed by the AWGF algorithm is better.展开更多
Risk management is an important aspect of financial research because correlations among financial data are essential in evaluating portfolio risk.Among various correlations,spatiotemporal correlations involve economic...Risk management is an important aspect of financial research because correlations among financial data are essential in evaluating portfolio risk.Among various correlations,spatiotemporal correlations involve economic entity attributes and are interrelated in space and time.Such correlations have therefore drawn increasing attention in financial risk management.However,classical correlation measurements are typically based on either time series correlations or spatial dependence;they cannot be directly applied to financial data with spatiotemporal correlations.The spatiotemporal correlation coefficient model with adaptive weight proposed in this paper can(1)address the absolute quantity,dynamic quantity,and dynamic development of financial data and(2)be used for risk grading,financial risk evaluation,and portfolio management.To verify the validity and superiority of this model,cluster analysis results and portfolio performance are compared with a classical model with time series correlation or spatial correlation,respectively.Empirical findings show that the proposed coefficient is highly effective and convenient compared to others.Overall,our method provides a highly efficient financial risk management method with valuable implications for investors and financial institutions.展开更多
Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learnin...Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.展开更多
In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be direc...In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.展开更多
The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have ...The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.展开更多
Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-...Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.展开更多
Due to the rapid development of electronic countermeasures(ECMs),the corresponding means of electronic counter countermeasures(ECCMs)are urgently needed.In this paper,an act-ive anti-jamming method based on frequency ...Due to the rapid development of electronic countermeasures(ECMs),the corresponding means of electronic counter countermeasures(ECCMs)are urgently needed.In this paper,an act-ive anti-jamming method based on frequency diverse array radar is proposed.By deriving the closed form of the phase center in a uniform line array FDA,we establish a model of the FDA signal based on adaptive weights and derive the effect of active anti-jamming in this regime.The pro-posed active anti-jamming method makes it difficult for jammers to detect or locate our radar.Fur-thermore,the effectiveness of the two frequency increment schemes in terms of anti-jamming is ana-lyzed by comparing the deviation of phase center.Finally,the simulation results verify the effective-ness and superiority of the proposed method.展开更多
Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia ar...Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.展开更多
The performance of a strapdown inertial navigation system(SINS)largely depends on the accuracy and rapidness of the initial alignment.A novel anti-interference self-alignment algorithm by attitude optimization estimat...The performance of a strapdown inertial navigation system(SINS)largely depends on the accuracy and rapidness of the initial alignment.A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper.The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions,the SINS alignment is heuristically established as an optimiza-tion problem of finding the minimum eigenvector.In order to further improve the alignment precision,an adaptive recursive weighted least squares(ARWLS)curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics.Simulation studies and experimental results favorably demonstrate its rapidness,accuracy and robustness.展开更多
The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved p...The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.展开更多
Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We intr...Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural network.The ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm optimization.The final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is 0.9577.We find that the maximum deposition level chosen as four can obtain the best result.Experiments demonstrate the effectiveness of both AIWF and RA.Finally,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models.Our model will be distributed to the cloud computing environment.展开更多
In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requi...In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.展开更多
Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations,an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations...Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations,an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations.Some hydrodynamic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm.By using adaptive weight method to determine the weight and target function,the multi-objective optimization could be translated into single-objective optimization.For a certain kind of submarine,three typical maneuvers were chosen to be the objects of study:overshoot maneuver in horizontal plane,overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane.From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrodynamic coefficient,the efficiency of proposed method is proved.展开更多
Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and computation.The services are affected by active and/or passive...Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and computation.The services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and content-sharing.These issues lead to computation and communication overhead of servers and computation nodes.To address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and computation.First,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead.Second,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge devices.The simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.展开更多
This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using A...This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.展开更多
In the daily application of an iris-recognition-at-a-distance(IAAD)system,many ocular images of low quality are acquired.As the iris part of these images is often not qualified for the recognition requirements,the mor...In the daily application of an iris-recognition-at-a-distance(IAAD)system,many ocular images of low quality are acquired.As the iris part of these images is often not qualified for the recognition requirements,the more accessible periocular regions are a good complement for recognition.To further boost the performance of IAAD systems,a novel end-to-end framework for multi-modal ocular recognition is proposed.The proposed framework mainly consists of iris/periocular feature extraction and matching,unsupervised iris quality assessment,and a score-level adaptive weighted fusion strategy.First,ocular feature reconstruction(OFR)is proposed to sparsely reconstruct each probe image by high-quality gallery images based on proper feature maps.Next,a brand new unsupervised iris quality assessment method based on random multiscale embedding robustness is proposed.Different from the existing iris quality assess-ment methods,the quality of an iris image is measured by its robustness in the embedding space.At last,the fusion strategy exploits the iris quality score as the fusion weight to coalesce the complementary information from the iris and periocular regions.Extensive experi-mental results on ocular datasets prove that the proposed method is obviously better than unimodal biometrics,and the fusion strategy can significantly improve therecognition performance.展开更多
Segmentation of images with intensity inhomogeneity is a significant task in the field of image processing,especially in medical image processing and analysis.Some local region-based models work well on handling inten...Segmentation of images with intensity inhomogeneity is a significant task in the field of image processing,especially in medical image processing and analysis.Some local region-based models work well on handling intensity inhomogeneity,but they are always sensitive to contour initialization and high noise.In this paper,we present an adaptive segmentation model for images with intensity inhomogeneity in the form of partial differential equation.Firstly,a global intensity fitting term and a local intensity fitting term are constructed by employing the global and local image information,respectively.Secondly,a tradeoff function is defined to adjust adaptively the weight between two fitting terms,which is based on the neighborhood contrast of image pixel.Finally,a weighted regularization term related to local entropy is used to ensure the smoothness of evolution curve.Meanwhile,a distance regularization term is added for stable level set evolution.Experimental results show that the proposed model without initial contour can segment inhomogeneous images stably and effectively,which thereby avoiding the influence of contour initialization on segmentation results.Besides,the proposed model works better on noise images comparing with two relevant segmentation models.展开更多
It is difficult to comprehensively extract device status information for CNNs under a single source high-frequency timing signal,and CNNs cannot effec-tively achieve precise identification and classification based on the...It is difficult to comprehensively extract device status information for CNNs under a single source high-frequency timing signal,and CNNs cannot effec-tively achieve precise identification and classification based on the importance of multichannel features.This article proposes a CNN fault diagnosis method based on multi-channel weight adaptation.This methodfirst normalizes different data sources as input as different channels of CNN,and uses the characteristics of convolutional networks to achieve the characteristics of different data sources.Fusion and extraction.Then,the SNET module is embedded into the CNN net-work,adapted to the weight of each channel,and the accuracy of classification is improved.Finally,through comparative experiments,this method can further improve the accuracy of fault recognition.展开更多
基金This study was supported by National Key Research and Development Project(Project No.2017YFD0301506)National Social Science Foundation(Project No.71774052)+1 种基金Hunan Education Department Scientific Research Project(Project No.17K04417A092).
文摘Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.
基金Project of Key Science and Technology of the Henan Province (No.202102310259)Henan Province University Scientific and Technological Innovation Team (No.18IRTSTHN009).
文摘The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.
基金supported by the National Natural Science Foundation of China (61673017,61905285)the Shaanxi Provincial Department of Science and Technology Key Project in the Field of Industry (2018ZDXM-GY-039)。
文摘The physical principle of infrared imaging leads to the low contrast of the whole image,the blurring of contour and edge details,and it is also sensitive to noise.To improve the quality of infrared image and visual effect,an adaptive weighted guided filter(AWGF) for infrared image enhancement algorithm was proposed.The core idea of AWGF algorithm is to propose an adaptive strategy to update the weights of guided filter(GF) parameters,which not only improves the accuracy of regularization parameter estimation in GF theory,but also achieves the purpose of removing infrared image noise and improving its detail contrast.A large number of real infrared images were used to verify AWGF algorithm,and good experimental results were obtained.Compared with other guided filtering algorithms,the halo phenomenon at the edge of infrared images processed by the AWGF algorithm is significantly avoided,and the evaluation parameter values of information entropy(IE),average gradient(AG),and moment of inertia(MI)are relatively high.This shows that the quality of infrared image processed by the AWGF algorithm is better.
基金supported by International(Regional)Cooperation and Exchange Project(71720107002)the National Natural Science Foundation of China(Nos.72161001 and 71963001)+2 种基金Guangxi Natural Science Fund(2018GXNSFBA050012)Key Research Base of Humanities and Social Sciences in Guangxi Universities Guangxi Development Research Strategy Institute(2021GDSIYB04,2022GDSIYB08)Project of Guangzhou Financial Service Innovation and Risk Management Research Base(No.PTJS202204).
文摘Risk management is an important aspect of financial research because correlations among financial data are essential in evaluating portfolio risk.Among various correlations,spatiotemporal correlations involve economic entity attributes and are interrelated in space and time.Such correlations have therefore drawn increasing attention in financial risk management.However,classical correlation measurements are typically based on either time series correlations or spatial dependence;they cannot be directly applied to financial data with spatiotemporal correlations.The spatiotemporal correlation coefficient model with adaptive weight proposed in this paper can(1)address the absolute quantity,dynamic quantity,and dynamic development of financial data and(2)be used for risk grading,financial risk evaluation,and portfolio management.To verify the validity and superiority of this model,cluster analysis results and portfolio performance are compared with a classical model with time series correlation or spatial correlation,respectively.Empirical findings show that the proposed coefficient is highly effective and convenient compared to others.Overall,our method provides a highly efficient financial risk management method with valuable implications for investors and financial institutions.
基金supported,in part,by the National Nature Science Foundation of China under grant numbers 62272236in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)fund.
文摘Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.
文摘In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.
基金supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B186 and No.2022D01B05)。
文摘The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.
文摘Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.
基金the National Natural Science Foundation of China(No.61971438)the Natural Science Founda-tion of Shaanxi Province(No.2019JM-155).
文摘Due to the rapid development of electronic countermeasures(ECMs),the corresponding means of electronic counter countermeasures(ECCMs)are urgently needed.In this paper,an act-ive anti-jamming method based on frequency diverse array radar is proposed.By deriving the closed form of the phase center in a uniform line array FDA,we establish a model of the FDA signal based on adaptive weights and derive the effect of active anti-jamming in this regime.The pro-posed active anti-jamming method makes it difficult for jammers to detect or locate our radar.Fur-thermore,the effectiveness of the two frequency increment schemes in terms of anti-jamming is ana-lyzed by comparing the deviation of phase center.Finally,the simulation results verify the effective-ness and superiority of the proposed method.
基金supported by the Natural Science Foundation of China under contact(61233007)
文摘Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.
基金supported by the National Natural Science Foundation of China(41174162).
文摘The performance of a strapdown inertial navigation system(SINS)largely depends on the accuracy and rapidness of the initial alignment.A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper.The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions,the SINS alignment is heuristically established as an optimiza-tion problem of finding the minimum eigenvector.In order to further improve the alignment precision,an adaptive recursive weighted least squares(ARWLS)curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics.Simulation studies and experimental results favorably demonstrate its rapidness,accuracy and robustness.
基金This work was supported by Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.,China(J2022114,Risk Assessment and Coordinated Operation of Coastal Wind Power Multi-Point Pooling Access System under Extreme Weather).
文摘The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.
基金This paper is partially supported by Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)Royal Society International Exchanges Cost Share Award,UK(RP202G0230)+5 种基金British Heart Foundation Accelerator Award,UK(AA/18/3/34220)Hope Foundation for Cancer Research,UK(RM60G0680)Global Challenges Research Fund(GCRF),UK(P202PF11)Sino-UK Industrial Fund,UK(RP202G0289)LIAS Pioneering Partnerships award,UK(P202ED10)Data Science Enhancement Fund,UK(P202RE237).
文摘Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural network.The ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm optimization.The final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is 0.9577.We find that the maximum deposition level chosen as four can obtain the best result.Experiments demonstrate the effectiveness of both AIWF and RA.Finally,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models.Our model will be distributed to the cloud computing environment.
基金supported by the National Natural Science Foundation of China(61473202)。
文摘In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.
文摘Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations,an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations.Some hydrodynamic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm.By using adaptive weight method to determine the weight and target function,the multi-objective optimization could be translated into single-objective optimization.For a certain kind of submarine,three typical maneuvers were chosen to be the objects of study:overshoot maneuver in horizontal plane,overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane.From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrodynamic coefficient,the efficiency of proposed method is proved.
基金supported in part by Basic Science Research Programs of the Ministry of Education(NRF-2018R1A2B6005105)in part by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2019R1A5A8080290).
文摘Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and computation.The services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and content-sharing.These issues lead to computation and communication overhead of servers and computation nodes.To address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and computation.First,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead.Second,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge devices.The simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.
基金Supported by the National Natural Science Foundation of China (No. 60872105)the Program for Science & Technology Innovative Research Team of Qing Lan Project in Higher Educational Institutions of Jiangsuthe Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.
基金This work was supported by National Natural Science Foundation of China(Nos.62006225,61906199 and 62071468)the Strategic Priority Research Program of Chinese Academy of Sciences(CAS),China(No.XDA 27040700)sponsored by The Beijing Nova Program,China(Nos.Z201100006820050 and Z211100002121010).
文摘In the daily application of an iris-recognition-at-a-distance(IAAD)system,many ocular images of low quality are acquired.As the iris part of these images is often not qualified for the recognition requirements,the more accessible periocular regions are a good complement for recognition.To further boost the performance of IAAD systems,a novel end-to-end framework for multi-modal ocular recognition is proposed.The proposed framework mainly consists of iris/periocular feature extraction and matching,unsupervised iris quality assessment,and a score-level adaptive weighted fusion strategy.First,ocular feature reconstruction(OFR)is proposed to sparsely reconstruct each probe image by high-quality gallery images based on proper feature maps.Next,a brand new unsupervised iris quality assessment method based on random multiscale embedding robustness is proposed.Different from the existing iris quality assess-ment methods,the quality of an iris image is measured by its robustness in the embedding space.At last,the fusion strategy exploits the iris quality score as the fusion weight to coalesce the complementary information from the iris and periocular regions.Extensive experi-mental results on ocular datasets prove that the proposed method is obviously better than unimodal biometrics,and the fusion strategy can significantly improve therecognition performance.
基金partially supported by National Natural Science Foundation of China(No.11901071 and 31971113)the Natural Science Foundation Project of CQ CSTC(No.cstc2019jcyj-msxmX0219)+2 种基金the E-ducational Commission Foundation of Chongqing of China(No.KJQN201800537)the China Postdoctoral Science Foundation(No.2020M671685)the Selective Funding for Postdoctoral Research Projects in Zhejiang Province(No.514000-X81902).
文摘Segmentation of images with intensity inhomogeneity is a significant task in the field of image processing,especially in medical image processing and analysis.Some local region-based models work well on handling intensity inhomogeneity,but they are always sensitive to contour initialization and high noise.In this paper,we present an adaptive segmentation model for images with intensity inhomogeneity in the form of partial differential equation.Firstly,a global intensity fitting term and a local intensity fitting term are constructed by employing the global and local image information,respectively.Secondly,a tradeoff function is defined to adjust adaptively the weight between two fitting terms,which is based on the neighborhood contrast of image pixel.Finally,a weighted regularization term related to local entropy is used to ensure the smoothness of evolution curve.Meanwhile,a distance regularization term is added for stable level set evolution.Experimental results show that the proposed model without initial contour can segment inhomogeneous images stably and effectively,which thereby avoiding the influence of contour initialization on segmentation results.Besides,the proposed model works better on noise images comparing with two relevant segmentation models.
基金This work is financially supported by:The National Key R&D Program of China(No.2020YFB1712600)The Fundamental Research Funds for Central University(No.3072022QBZ0601)The National Natural Science Foundation of China(No.62272126).
文摘It is difficult to comprehensively extract device status information for CNNs under a single source high-frequency timing signal,and CNNs cannot effec-tively achieve precise identification and classification based on the importance of multichannel features.This article proposes a CNN fault diagnosis method based on multi-channel weight adaptation.This methodfirst normalizes different data sources as input as different channels of CNN,and uses the characteristics of convolutional networks to achieve the characteristics of different data sources.Fusion and extraction.Then,the SNET module is embedded into the CNN net-work,adapted to the weight of each channel,and the accuracy of classification is improved.Finally,through comparative experiments,this method can further improve the accuracy of fault recognition.
