This paper develops a variational model for image noise removal using total curvature(TC), which is a high-order regularizer. The TC has the advantage of preserving image feature. Unfortunately, it also has the charac...This paper develops a variational model for image noise removal using total curvature(TC), which is a high-order regularizer. The TC has the advantage of preserving image feature. Unfortunately, it also has the characteristics of nonlinear, non-convex and non-smooth. Consequently, the numerical computation with the curvature regularization is difficult. In order to conquer the computation problem, the proposed model is transformed into an alternating optimization problem by importing auxiliary variables. Furthermore, based on alternating direction method of multipliers, we design a fast numerical approximation iterative scheme for proposed model. Finally, numerous experiments are implemented to indicate the advantages of the proposed model in image edge preserving, image contrast and corners preserving. Meanwhile, the high computational efficiency of the designed model is verified by comparing with traditional models, including the total variation(TV) and total Laplace(TL) model.展开更多
Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts...Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.展开更多
In this paper,a new mission model,called a multi-debris active removal mission with partial debris capture strategy,is proposed.The model assumes that a platform only captures part of the scheduled debris at a time an...In this paper,a new mission model,called a multi-debris active removal mission with partial debris capture strategy,is proposed.The model assumes that a platform only captures part of the scheduled debris at a time and then releases these debris pieces to a disposal orbit.This process is then repeated until all of the scheduled debris is removed.A genetic algorithm with a multiparameter concatenated coding method is designed to optimize the plan of a multi-debris active removal mission with a partial debris capture strategy.A set of six pieces of debris and a set of 10 pieces of debris are selected to demonstrate the proposed planning method.The result confirms the effectiveness of the genetic algorithm with the multi-parameter concatenated coding method.The new mission model provides a more comprehensive decision-making framework than the existing mission models and makes it possible to further decrease mission costs.展开更多
Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain regions.The Active Debris Removal(ADR)mission,to mitigate the spac...Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain regions.The Active Debris Removal(ADR)mission,to mitigate the space debris density and stabilize the space debris environment,has been considered as a most effective method.In this paper,a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed,which includes the low-level and high-level optimization process.To improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions,the ADR mission is seen as a Time-Dependant Traveling Salesman Problem(TDTSP)with two objective functions to minimize the total mission duration and the total propellant consumption.The problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital maneuvers.Two optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission,and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II(NSGA-II).Two test cases are presented,which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.展开更多
In order to rapidly respond to the complex and mutational market, a new facility layout plan based on cellular manufacturing is proposed, which gives consideration to high efficiency and flexibility. The plan designs ...In order to rapidly respond to the complex and mutational market, a new facility layout plan based on cellular manufacturing is proposed, which gives consideration to high efficiency and flexibility. The plan designs two phases of integrated cell layout, i.e., cell construction and cell system layout, on the condition of adding/removing machines. First, in view of the costs of logics and machine-relocation, the cell construction based on the alternative processing routes and intra-cell layout are integrated as a whole, which achieves cell formation, process planning and the intra-cell layout in a single step. Secondly, an approach of a continuous optimized multi-line layout for solving the cell system layout problem is proposed, which eliminates the coupling relationship from the machine-relocation and realizes an integrated design of the two phases of the cell layout. An application based on real factory data is optimally solved by the Matlab 7.0 software to validate and verify the models.展开更多
Some depression cells with heights lower than their surrounding cells may often be found in Grid-based digital elevation models (DEM) dataset due to sampling errors.The depression-filling algorithm presented by Planch...Some depression cells with heights lower than their surrounding cells may often be found in Grid-based digital elevation models (DEM) dataset due to sampling errors.The depression-filling algorithm presented by Planchon and Darboux works very quickly compared to other published methods.Despite its simplicity and deli-cacy,this algorithm remains difficult to understand due to its three complex subroutines and its recursive execution.Another fast algorithm is presented in this article.The main idea of this new algorithm is as follows:first,the DEM dataset is viewed as an island and the outer space as an ocean;when the ocean level increases,the DEM cells on the island's boundary will be inundated;when a cell is inundated for the first time,its elevation is increased to the ocean level at that moment;after the ocean has inun-dated the entire DEM,all of the depressions are filled.The depression-removing processing is performed using a priority queue.Theoretically,this new algorithm is a fast algorithm despite the fact that it runs more slowly than Planchon and Darboux's method.Its time-complexity in both the worst case and in an average case is O(8nlog 2 (m)),which is close to O(n).The running speed of this algorithm depends mainly on the insertion operation of the priority queue.As shown by the tests,the depres-sion-filling effects of this algorithm are correct and valid,and the overall time consumption of this algorithm is less than twice the time consumed by Planchon & Darboux's method for handling a DEM smaller than 2500×2500 cells.More importantly,this new algorithm is simpler and easier to understand than Planchon and Darboux's method This advantage allows the correct program code to be written quickly.展开更多
Developing autonomous mobile robot system has been a hot topic in AI area. With recent advances in technology, autonomous robots are attracting more and more attention worldwide, and there are a lot of ongoing researc...Developing autonomous mobile robot system has been a hot topic in AI area. With recent advances in technology, autonomous robots are attracting more and more attention worldwide, and there are a lot of ongoing research and development activities in both industry and academia. In complex ground environment, obstacles positions are uncertain. Path finding for robots in such environment is very hot issues currently. In this paper, we present the design and implementation of a multi-sensor based object detecting and moving autonomous robot exploration system, 4RE, with the VEX robotics design system. With the goals of object detecting and removing in complex ground environment with different obstacles, a novel object detecting and removing algorithms is proposed and implemented. Experimental results indicate that our robot system with our object detecting and removing algorithm can effectively detect the obstacles on the path and remove them in complex ground environment and avoid collision with the obstacles.展开更多
Images are frequently affected because of blurring,and data loss occurred by sampling and noise occurrence.The images are getting blurred because of object movement in the scenario,atmospheric misrepresentations,and o...Images are frequently affected because of blurring,and data loss occurred by sampling and noise occurrence.The images are getting blurred because of object movement in the scenario,atmospheric misrepresentations,and optical aberrations.The main objective of image restoration is to evaluate the original image from the corrupted data.To overcome this issue,the multiobjective reptile search algorithm is proposed for performing an effective image deblurring and restoration(MORSA-IDR).The proposed MORSA is used in two different processes such as threshold and kernel parameter calculation.In that,threshold values are used for detecting and replacing the noisy pixel removal using deep residual network,and estimation of kernel is performed for deblurring the images.The main objective of the proposed MORSA-IDR is to enhance the process of deblurring for recovering low-level contextual information.The MORSA-IDR is evaluated using peak signal noise ratio(PSNR)and structural similarity index.The existing researches such as enhanced local maximum intensity(ELMI)prior and deep unrolling for blind deblurring(DUBLID)are used to evaluate the MORSA-IDR.The PSNR of MORSA-IDR for image 6 is 30.98 dB,which is high when compared with the ELMI and DUBLID.展开更多
基金supported by the National Natural Science Foundation of China(No.61602269)the China Postdoctoral Science Foundation(No.2015M571993)+1 种基金the Shandong Provincial Natural Science Foundation of China(No.ZR2017MD004)the Qingdao Postdoctoral Application Research Funded Project
文摘This paper develops a variational model for image noise removal using total curvature(TC), which is a high-order regularizer. The TC has the advantage of preserving image feature. Unfortunately, it also has the characteristics of nonlinear, non-convex and non-smooth. Consequently, the numerical computation with the curvature regularization is difficult. In order to conquer the computation problem, the proposed model is transformed into an alternating optimization problem by importing auxiliary variables. Furthermore, based on alternating direction method of multipliers, we design a fast numerical approximation iterative scheme for proposed model. Finally, numerous experiments are implemented to indicate the advantages of the proposed model in image edge preserving, image contrast and corners preserving. Meanwhile, the high computational efficiency of the designed model is verified by comparing with traditional models, including the total variation(TV) and total Laplace(TL) model.
基金funded by the Third Xinjiang Scientific Expedition Program(2021xjkk1400)the National Natural Science Foundation of China(42071049)+2 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01C022)the Xinjiang Uygur Autonomous Region Innovation Environment Construction Special Project&Science and Technology Innovation Base Construction Project(PT2107)the Tianshan Talent-Science and Technology Innovation Team(2022TSYCTD0006).
文摘Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.
基金co-supported by the Open Fund Project of Space Intelligent Control Technology Laboratory(No.HTKJ2021KL502010)the Research Project of Space Debris and Near-earth Asteroid Defense Grants,China(No.KJSP 2020010303)the National Natural Science Foundation of China(No.11802130).
文摘In this paper,a new mission model,called a multi-debris active removal mission with partial debris capture strategy,is proposed.The model assumes that a platform only captures part of the scheduled debris at a time and then releases these debris pieces to a disposal orbit.This process is then repeated until all of the scheduled debris is removed.A genetic algorithm with a multiparameter concatenated coding method is designed to optimize the plan of a multi-debris active removal mission with a partial debris capture strategy.A set of six pieces of debris and a set of 10 pieces of debris are selected to demonstrate the proposed planning method.The result confirms the effectiveness of the genetic algorithm with the multi-parameter concatenated coding method.The new mission model provides a more comprehensive decision-making framework than the existing mission models and makes it possible to further decrease mission costs.
基金the Open Research Foundation of Science and Technology in Aerospace Flight Dynamics Laboratory of China(GF2018005).
文摘Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain regions.The Active Debris Removal(ADR)mission,to mitigate the space debris density and stabilize the space debris environment,has been considered as a most effective method.In this paper,a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed,which includes the low-level and high-level optimization process.To improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions,the ADR mission is seen as a Time-Dependant Traveling Salesman Problem(TDTSP)with two objective functions to minimize the total mission duration and the total propellant consumption.The problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital maneuvers.Two optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission,and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II(NSGA-II).Two test cases are presented,which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.
文摘In order to rapidly respond to the complex and mutational market, a new facility layout plan based on cellular manufacturing is proposed, which gives consideration to high efficiency and flexibility. The plan designs two phases of integrated cell layout, i.e., cell construction and cell system layout, on the condition of adding/removing machines. First, in view of the costs of logics and machine-relocation, the cell construction based on the alternative processing routes and intra-cell layout are integrated as a whole, which achieves cell formation, process planning and the intra-cell layout in a single step. Secondly, an approach of a continuous optimized multi-line layout for solving the cell system layout problem is proposed, which eliminates the coupling relationship from the machine-relocation and realizes an integrated design of the two phases of the cell layout. An application based on real factory data is optimally solved by the Matlab 7.0 software to validate and verify the models.
基金financially supported by the National Basic Research Program of China (Grant No.2006CB400502)the Promotion of 100 Young Talent Scientist Project of the Chinese Acad-emy of Sciences (8-057493)the Special Meteorology Project(GYHY(QX)2007-6-1)
文摘Some depression cells with heights lower than their surrounding cells may often be found in Grid-based digital elevation models (DEM) dataset due to sampling errors.The depression-filling algorithm presented by Planchon and Darboux works very quickly compared to other published methods.Despite its simplicity and deli-cacy,this algorithm remains difficult to understand due to its three complex subroutines and its recursive execution.Another fast algorithm is presented in this article.The main idea of this new algorithm is as follows:first,the DEM dataset is viewed as an island and the outer space as an ocean;when the ocean level increases,the DEM cells on the island's boundary will be inundated;when a cell is inundated for the first time,its elevation is increased to the ocean level at that moment;after the ocean has inun-dated the entire DEM,all of the depressions are filled.The depression-removing processing is performed using a priority queue.Theoretically,this new algorithm is a fast algorithm despite the fact that it runs more slowly than Planchon and Darboux's method.Its time-complexity in both the worst case and in an average case is O(8nlog 2 (m)),which is close to O(n).The running speed of this algorithm depends mainly on the insertion operation of the priority queue.As shown by the tests,the depres-sion-filling effects of this algorithm are correct and valid,and the overall time consumption of this algorithm is less than twice the time consumed by Planchon & Darboux's method for handling a DEM smaller than 2500×2500 cells.More importantly,this new algorithm is simpler and easier to understand than Planchon and Darboux's method This advantage allows the correct program code to be written quickly.
文摘Developing autonomous mobile robot system has been a hot topic in AI area. With recent advances in technology, autonomous robots are attracting more and more attention worldwide, and there are a lot of ongoing research and development activities in both industry and academia. In complex ground environment, obstacles positions are uncertain. Path finding for robots in such environment is very hot issues currently. In this paper, we present the design and implementation of a multi-sensor based object detecting and moving autonomous robot exploration system, 4RE, with the VEX robotics design system. With the goals of object detecting and removing in complex ground environment with different obstacles, a novel object detecting and removing algorithms is proposed and implemented. Experimental results indicate that our robot system with our object detecting and removing algorithm can effectively detect the obstacles on the path and remove them in complex ground environment and avoid collision with the obstacles.
文摘Images are frequently affected because of blurring,and data loss occurred by sampling and noise occurrence.The images are getting blurred because of object movement in the scenario,atmospheric misrepresentations,and optical aberrations.The main objective of image restoration is to evaluate the original image from the corrupted data.To overcome this issue,the multiobjective reptile search algorithm is proposed for performing an effective image deblurring and restoration(MORSA-IDR).The proposed MORSA is used in two different processes such as threshold and kernel parameter calculation.In that,threshold values are used for detecting and replacing the noisy pixel removal using deep residual network,and estimation of kernel is performed for deblurring the images.The main objective of the proposed MORSA-IDR is to enhance the process of deblurring for recovering low-level contextual information.The MORSA-IDR is evaluated using peak signal noise ratio(PSNR)and structural similarity index.The existing researches such as enhanced local maximum intensity(ELMI)prior and deep unrolling for blind deblurring(DUBLID)are used to evaluate the MORSA-IDR.The PSNR of MORSA-IDR for image 6 is 30.98 dB,which is high when compared with the ELMI and DUBLID.
