Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extracti...Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extraction of numerous repetitive features,thereby reducing the accuracy of image retrieval.This article proposes an indoor visual localization method based on semantic segmentation and adaptive weight fusion to address the issue of ground texture interference with retrieval results.During the positioning process,an indoor semantic segmentation model is established.Semantic segmentation technology is applied to accurately delineate the ground portion of the images.Fea-ture extraction is performed on both the original database and the ground-segmented database.The vector of locally aggregated descriptors(VLAD)algorithm is then used to convert image features into a fixed-length feature representation,which improves the efficiency of image retrieval.Simul-taneously,a method for adaptive weight optimization in similarity calculation is proposed,using a-daptive weights to compute similarity for different regional features,thereby improving the accuracy of image retrieval.The experimental results indicate that this method significantly reduces ground interference and effectively utilizes ground information,thereby improving the accuracy of image retrieval.展开更多
For intercepting modern high maneuverable targets, a novel adaptive weighted differential game guidance law based on the game theory of mixed strategy is proposed, combining two guidance laws which are derived from th...For intercepting modern high maneuverable targets, a novel adaptive weighted differential game guidance law based on the game theory of mixed strategy is proposed, combining two guidance laws which are derived from the perfect and imperfect in- formation pattern, respectively. The weights vary according to the estimated error of the target＇s acceleration, the guidance law is generated by directly using the estimation of target＇s acceleration when the estimated error is small, and a differential game guidance law with adaptive penalty coefficient is implemented when the estimated error is large. The adaptive penalty coeffi- cients are not constants and they can be adjusted with current target maneuverability. The superior homing performance of the new guidance law is verified by computer simulations.展开更多
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
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
In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted av...In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.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
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
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
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
Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine.The latest research demonstrates that Subspace-based I...Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine.The latest research demonstrates that Subspace-based Improved Model Predictive Control(SIMPC)can overcome the difficulty in solving the predictive model in MPC/NMPC applications.However,applying constant design parameters cannot maintain consistent control effects in all states.Meanwhile,the designed system relies too much on sensor-measured data,and thus it is difficult to thoroughly validate the safety of the system because of its high complexity.This means that any potential hardware/software faults will endanger the engine.Therefore,this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle(PLA)and real-time relative tracking error.Thus,without introducing additional design parameters,an Adaptive Tracking Weight-based SIMPC(ATW-SIMPC)controller is designed to improve the control performance in all operating states effectively.Then,a Primary/Backup Hybrid Control(PBHC)strategy with the ATW-SIMPC controller as the primary system and the traditional speed(Nf)controller as the backup system is proposed to ensure safety.The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems,so as to avoid bump transition.The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults;otherwise,the ATW-SIMPC controller keeps acting on the engine.The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship,compared with the existing SIMPC controller,uplifts the dynamic control performance by 32%and reduces overshoots to an allowable limit,resulting in a better control effect in full state.The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults,such as sensor/onboard adaptive model faults.The approach proposed is applicable to the design of a model-based engine intelligent control system.展开更多
Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose signifi...Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data leakage.Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security concerns.However,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across clients.This heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse distributions.To address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated Learning.The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics.Additionally,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates.Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.展开更多
Studies show that Graph Neural Networks(GNNs)are susceptible to minor perturbations.Therefore,analyzing adversarial attacks on GNNs is crucial in current research.Previous studies used Generative Adversarial Networks ...Studies show that Graph Neural Networks(GNNs)are susceptible to minor perturbations.Therefore,analyzing adversarial attacks on GNNs is crucial in current research.Previous studies used Generative Adversarial Networks to generate a set of fake nodes,injecting them into a clean GNNs to poison the graph structure and evaluate the robustness of GNNs.In the attack process,the computation of new node connections and the attack loss are independent,which affects the attack on the GNN.To improve this,a Fake Node Camouflage Attack based on Mutual Information(FNCAMI)algorithm is proposed.By incorporating Mutual Information(MI)loss,the distribution of nodes injected into the GNNs become more similar to the original nodes,achieving better attack results.Since the loss ratios of GNNs and MI affect performance,we also design an adaptive weighting method.By adjusting the loss weights in real-time through rate changes,larger loss values are obtained,eliminating local optima.The feasibility,effectiveness,and stealthiness of this algorithm are validated on four real datasets.Additionally,we use both global and targeted attacks to test the algorithm’s performance.Comparisons with baseline attack algorithms and ablation experiments demonstrate the efficiency of the FNCAMI algorithm.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
The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A a...The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.展开更多
In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept...In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
Aiming to address the limitations of the standard Chimp Optimization Algorithm(ChOA),such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle(UAV)path planning,this paper propose...Aiming to address the limitations of the standard Chimp Optimization Algorithm(ChOA),such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle(UAV)path planning,this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm(IChOA).First,this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints,transforming the path planning problem into an optimization problem with multiple constraints.Second,this paper enhances the diversity of the chimpanzee population by applying the Sine chaos mapping strategy and introduces a nonlinear convergence factor to improve the algorithm’s search accuracy and convergence speed.Finally,this paper proposes a dynamic adjustment strategy for the number of chimpanzee advance echelons,which effectively balances global exploration and local exploitation,significantly optimizing the algorithm’s search performance.To validate the effectiveness of the IChOA algorithm,this paper conducts experimental comparisons with eight different intelligent algorithms.The experimental results demonstrate that the IChOA outperforms the selected comparison algorithms in terms of practicality and robustness in UAV 3D path planning.It effectively solves the issues of efficiency in finding the shortest path and ensures high stability during execution.展开更多
This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow an...This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.展开更多
Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a chall...Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate segmentation.In this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue.Using an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding block.The MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale features.Additionally,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions.The proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed method.It achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.展开更多
As one of the most classical scheduling problems,flexible job shop scheduling problems(FJSP)find widespread applications in modern intelligent manufacturing systems.However,the majority of meta-heuristic methods for s...As one of the most classical scheduling problems,flexible job shop scheduling problems(FJSP)find widespread applications in modern intelligent manufacturing systems.However,the majority of meta-heuristic methods for solving FJSP in the literature are population-based evolutionary algorithms,which are complex and time-consuming.In this paper,we propose a fast effective singlesolution based local search algorithm with an innovative adaptive weighting-based local search(AWLS)technique for solving FJSP.The adaptive weighting technique assigns weights to each operation and adaptively updates them during the exploration.AWLS integrates a Tabu Search strategy and the adaptive weighting technique to smooth the landscape of the search space and enhance the exploration diversity.Computational experiments on 313 well-known benchmark instances demonstrate that AWLS is highly competitive with state-of-the-art algorithms in terms of both solution quality and computational efficiency,despite of its simplicity.Specifically,AWLS improves the previous best-known results in the literature on 33 instances and match the best-known results on the remaining ones except for only one under the same time limit of up to 300 s.As a strongly non-deterministic polynomia(NP)-hard problem which has been extensively studied for nearly half a century,breaking the records on these classic instances is an arduous task.Nevertheless,AWLS establishes new records on 8 challenging instances whose previous best records were established by a state-of-the-art meta-heuristic algorithm and a famous industrial solver.展开更多
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024F025)+2 种基金the Open Research Fund of National Mobile Communications Research Laboratory Southeast University(No.2023D07)the Outstanding Youth Program of Natural Science Foundation of Heilongjiang Province(No.YQ2020F012)the Funda-mental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extraction of numerous repetitive features,thereby reducing the accuracy of image retrieval.This article proposes an indoor visual localization method based on semantic segmentation and adaptive weight fusion to address the issue of ground texture interference with retrieval results.During the positioning process,an indoor semantic segmentation model is established.Semantic segmentation technology is applied to accurately delineate the ground portion of the images.Fea-ture extraction is performed on both the original database and the ground-segmented database.The vector of locally aggregated descriptors(VLAD)algorithm is then used to convert image features into a fixed-length feature representation,which improves the efficiency of image retrieval.Simul-taneously,a method for adaptive weight optimization in similarity calculation is proposed,using a-daptive weights to compute similarity for different regional features,thereby improving the accuracy of image retrieval.The experimental results indicate that this method significantly reduces ground interference and effectively utilizes ground information,thereby improving the accuracy of image retrieval.
基金National Natural Science Foundation of China (60874040)
文摘For intercepting modern high maneuverable targets, a novel adaptive weighted differential game guidance law based on the game theory of mixed strategy is proposed, combining two guidance laws which are derived from the perfect and imperfect in- formation pattern, respectively. The weights vary according to the estimated error of the target＇s acceleration, the guidance law is generated by directly using the estimation of target＇s acceleration when the estimated error is small, and a differential game guidance law with adaptive penalty coefficient is implemented when the estimated error is large. The adaptive penalty coeffi- cients are not constants and they can be adjusted with current target maneuverability. The superior homing performance of the new guidance law is verified by computer simulations.
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 National Natural Science Foundation of China (No.30500129)
文摘In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.
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 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.
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 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National Natural Science Foundation of China (Nos. 52176009, 51906103) for financial support
文摘Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine.The latest research demonstrates that Subspace-based Improved Model Predictive Control(SIMPC)can overcome the difficulty in solving the predictive model in MPC/NMPC applications.However,applying constant design parameters cannot maintain consistent control effects in all states.Meanwhile,the designed system relies too much on sensor-measured data,and thus it is difficult to thoroughly validate the safety of the system because of its high complexity.This means that any potential hardware/software faults will endanger the engine.Therefore,this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle(PLA)and real-time relative tracking error.Thus,without introducing additional design parameters,an Adaptive Tracking Weight-based SIMPC(ATW-SIMPC)controller is designed to improve the control performance in all operating states effectively.Then,a Primary/Backup Hybrid Control(PBHC)strategy with the ATW-SIMPC controller as the primary system and the traditional speed(Nf)controller as the backup system is proposed to ensure safety.The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems,so as to avoid bump transition.The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults;otherwise,the ATW-SIMPC controller keeps acting on the engine.The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship,compared with the existing SIMPC controller,uplifts the dynamic control performance by 32%and reduces overshoots to an allowable limit,resulting in a better control effect in full state.The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults,such as sensor/onboard adaptive model faults.The approach proposed is applicable to the design of a model-based engine intelligent control system.
基金supported by the National Natural Science Foundation of China(Nos.62002100,61902237)Key Research and Promotion Projects of Henan Province(Nos.232102240023,232102210063,222102210040).
文摘Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data leakage.Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security concerns.However,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across clients.This heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse distributions.To address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated Learning.The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics.Additionally,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates.Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.
基金supported by the Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2022JM-381,2017JQ6070)National Natural Science Foundation of China(Grant No.61703256),Foundation of State Key Laboratory of Public Big Data(No.PBD2022-08)the Fundamental Research Funds for the Central Universities,China(Program No.GK202201014,GK202202003,GK201803020).
文摘Studies show that Graph Neural Networks(GNNs)are susceptible to minor perturbations.Therefore,analyzing adversarial attacks on GNNs is crucial in current research.Previous studies used Generative Adversarial Networks to generate a set of fake nodes,injecting them into a clean GNNs to poison the graph structure and evaluate the robustness of GNNs.In the attack process,the computation of new node connections and the attack loss are independent,which affects the attack on the GNN.To improve this,a Fake Node Camouflage Attack based on Mutual Information(FNCAMI)algorithm is proposed.By incorporating Mutual Information(MI)loss,the distribution of nodes injected into the GNNs become more similar to the original nodes,achieving better attack results.Since the loss ratios of GNNs and MI affect performance,we also design an adaptive weighting method.By adjusting the loss weights in real-time through rate changes,larger loss values are obtained,eliminating local optima.The feasibility,effectiveness,and stealthiness of this algorithm are validated on four real datasets.Additionally,we use both global and targeted attacks to test the algorithm’s performance.Comparisons with baseline attack algorithms and ablation experiments demonstrate the efficiency of the FNCAMI algorithm.
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 the National Natural Science Foundation of China(Nos.62265010,62061024)Gansu Province Science and Technology Plan(No.23YFGA0062)Gansu Province Innovation Fund(No.2022A-215)。
文摘The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.
基金Project(08SK1002) supported by the Major Project of Science and Technology Department of Hunan Province,China
文摘In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the Shaanxi Province Natural Science Basic Research Program Project(2024JC-YBMS-572)partially funded by Yan’an University Graduate Education Innovation Program Project(YCX2023032,YCX2023033,YCX2024094,YCX2024097)the“14th Five Year Plan Medium and Long Term Major Scientific Research Project”(2021ZCQ015)of Yan’an University.
文摘Aiming to address the limitations of the standard Chimp Optimization Algorithm(ChOA),such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle(UAV)path planning,this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm(IChOA).First,this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints,transforming the path planning problem into an optimization problem with multiple constraints.Second,this paper enhances the diversity of the chimpanzee population by applying the Sine chaos mapping strategy and introduces a nonlinear convergence factor to improve the algorithm’s search accuracy and convergence speed.Finally,this paper proposes a dynamic adjustment strategy for the number of chimpanzee advance echelons,which effectively balances global exploration and local exploitation,significantly optimizing the algorithm’s search performance.To validate the effectiveness of the IChOA algorithm,this paper conducts experimental comparisons with eight different intelligent algorithms.The experimental results demonstrate that the IChOA outperforms the selected comparison algorithms in terms of practicality and robustness in UAV 3D path planning.It effectively solves the issues of efficiency in finding the shortest path and ensures high stability during execution.
文摘This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.
基金supported by the STI2030-Major-Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate segmentation.In this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue.Using an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding block.The MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale features.Additionally,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions.The proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed method.It achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.
基金supported in part by the National Natural Science Foundation of China(NSFC)(62202192 and 72101094)the National Science Fund for Distinguished Young Scholars of China(51825502).
文摘As one of the most classical scheduling problems,flexible job shop scheduling problems(FJSP)find widespread applications in modern intelligent manufacturing systems.However,the majority of meta-heuristic methods for solving FJSP in the literature are population-based evolutionary algorithms,which are complex and time-consuming.In this paper,we propose a fast effective singlesolution based local search algorithm with an innovative adaptive weighting-based local search(AWLS)technique for solving FJSP.The adaptive weighting technique assigns weights to each operation and adaptively updates them during the exploration.AWLS integrates a Tabu Search strategy and the adaptive weighting technique to smooth the landscape of the search space and enhance the exploration diversity.Computational experiments on 313 well-known benchmark instances demonstrate that AWLS is highly competitive with state-of-the-art algorithms in terms of both solution quality and computational efficiency,despite of its simplicity.Specifically,AWLS improves the previous best-known results in the literature on 33 instances and match the best-known results on the remaining ones except for only one under the same time limit of up to 300 s.As a strongly non-deterministic polynomia(NP)-hard problem which has been extensively studied for nearly half a century,breaking the records on these classic instances is an arduous task.Nevertheless,AWLS establishes new records on 8 challenging instances whose previous best records were established by a state-of-the-art meta-heuristic algorithm and a famous industrial solver.
