Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to f...Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to fall into local optimum and leads to slow convergence speed.The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms.In the present study,we propose a chaos-enhanced MFO(CMFO)by incorporating chaos maps into the MFO algorithm to enhance its performance.The chaotic map is utilized to initialize the moths’population,handle the boundary overstepping,and tune the distance parameter.The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one.The performance of the CMFO is also verified by using two real engineering problems.The statistical results clearly demonstrate that the appropriate chaotic map(singer map)embedded in the appropriate component of MFO can significantly improve the performance of MFO.展开更多
In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of ind...In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.展开更多
Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for t...Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.展开更多
An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the bucklin...An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the buckling load of laminated composite plates(LCPs)under uniaxial and biaxial compressions.In this method,a novel iterative adaptive Kriging model,which is structured using two training sample sets as active and adaptive points,is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process.The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples.The cell-based smoothed discrete shear gap method(CS-DSG3)is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets.The buckling load of the LCPs is maximized by utilizing the IPSO algorithm.To demonstrate the efficiency and accuracy of the proposed methodology,the LCPs with different layers(2,3,4,and 10 layers),boundary conditions,aspect ratios and load patterns(biaxial and uniaxial loads)are investigated.The results obtained by proposed method are in good agreement with the literature results,but with less computational burden.By applying adaptive radial Kriging model,the accurate optimal resultsebased predictions of the buckling load are obtained for the studied LCPs.展开更多
In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied...In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.展开更多
To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damagin...To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.展开更多
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK ...A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.展开更多
When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game ...When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.展开更多
Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the b...Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, all improved particie swarm algorithm （mO_PSO） is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.展开更多
This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell...This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.展开更多
To meet the requirements of modern air combat,an integrated fire/flight control(IFFC)system is designed to achieve automatic precision tracking and aiming for armed helicopters and release the pilot from heavy target ...To meet the requirements of modern air combat,an integrated fire/flight control(IFFC)system is designed to achieve automatic precision tracking and aiming for armed helicopters and release the pilot from heavy target burden.Considering the complex dynamic characteristics and the couplings of armed helicopters,an improved automatic attack system is con-structed to integrate the fire control system with the flight con-trol system into a unit.To obtain the optimal command signals,the algorithm is investigated to solve nonconvex optimization problems by the contracting Broyden Fletcher Goldfarb Shanno(C-BFGS)algorithm combined with the trust region method.To address the uncertainties in the automatic attack system,the memory nominal distribution and Wasserstein distance are introduced to accurately characterize the uncertainties,and the dual solvable problem is analyzed by using the duality the-ory,conjugate function,and dual norm.Simulation results verify the practicality and validity of the proposed method in solving the IFFC problem on the premise of satisfactory aiming accu-racy.展开更多
Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a ta...Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.展开更多
The azimuth resolution improvement problem is solved via a coherent combination of synthetic aperture radar （SAR） ima-ges with the quasi-non-overlapped Doppler bandwidth. Prior to the spectra combination, SAR images...The azimuth resolution improvement problem is solved via a coherent combination of synthetic aperture radar （SAR） ima-ges with the quasi-non-overlapped Doppler bandwidth. Prior to the spectra combination, SAR images should be coregistered, while phase biases induced by topography, atmospheric propagation delays and baseline measurement errors should be calibrated. However, the coregistration accuracy suffers from large Doppler decorrelation caused by the quasi-non-overlapped Doppler band-width. Furthermore, the method used to estimate phase biases from interferogram of azimuth prefiltered SAR image pairs wil fail when there is no overlapped spectrum. The fringe simulation and maximum sharpness optimization are adopted to deal with the problems. Accordingly, a novel algorithm to coherently synthesize SAR images is presented. The experiment with the Terra SAR X-band （TerraSAR-X） satel ite data validates the performance of the presented method.展开更多
In a commercialized, fully artificial plant factory, artificial luminaire is arranged in a unified way using a general illumination theory, an actual measurement, or an empirical methodology. However, with these metho...In a commercialized, fully artificial plant factory, artificial luminaire is arranged in a unified way using a general illumination theory, an actual measurement, or an empirical methodology. However, with these methods, lightings are implemented without considering specific optical characteristics of lighting or material characteristics of each component that constructs a cultivation system, resulting in an amount of light that becomes irregular. The amount of lighting is closely related with the growth and quality of crops, and the deviation between points where cultivated crops are located causes quality difference in the produced crops, thus impairing the economic feasibility of a plant factory. In this regard, a simulation to figure out an optimum lighting layout was performed. Arrangements based on the spectrum distribution of light source and reflector materials were implemented to ascertain the distance between lighting and height of lighting and gather information in the pre-treatment process to improve the uniformity of light in the plant cultivation system. Improvement of around 15% in light uniformity is achieved compared with the existing system after the simulation is carried out. This result would reduce the deviation in crop growth to make uniform quality crop production possible.展开更多
基金supported by the Military Science Project of the National Social Science Foundation of China(15GJ003-141)
文摘Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to fall into local optimum and leads to slow convergence speed.The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms.In the present study,we propose a chaos-enhanced MFO(CMFO)by incorporating chaos maps into the MFO algorithm to enhance its performance.The chaotic map is utilized to initialize the moths’population,handle the boundary overstepping,and tune the distance parameter.The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one.The performance of the CMFO is also verified by using two real engineering problems.The statistical results clearly demonstrate that the appropriate chaotic map(singer map)embedded in the appropriate component of MFO can significantly improve the performance of MFO.
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
基金the National Natural Science Foundation of China(Grant No.62101579).
文摘Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.
基金Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant number 107.02-2019.330.
文摘An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the buckling load of laminated composite plates(LCPs)under uniaxial and biaxial compressions.In this method,a novel iterative adaptive Kriging model,which is structured using two training sample sets as active and adaptive points,is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process.The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples.The cell-based smoothed discrete shear gap method(CS-DSG3)is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets.The buckling load of the LCPs is maximized by utilizing the IPSO algorithm.To demonstrate the efficiency and accuracy of the proposed methodology,the LCPs with different layers(2,3,4,and 10 layers),boundary conditions,aspect ratios and load patterns(biaxial and uniaxial loads)are investigated.The results obtained by proposed method are in good agreement with the literature results,but with less computational burden.By applying adaptive radial Kriging model,the accurate optimal resultsebased predictions of the buckling load are obtained for the studied LCPs.
基金Projects(61203020,61403190)supported by the National Natural Science Foundation of ChinaProject(BK20141461)supported by the Jiangsu Province Natural Science Foundation,China
文摘In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.
基金Sponsored by Beijing Priority Laboratory Fund of China(SYS10070522)
文摘To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.
文摘A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
基金National Natural Science Foundation of China(NSFC61773142,NSFC62303136)。
文摘When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
基金the National Natural Science Foundation of China (60573159)
文摘Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, all improved particie swarm algorithm （mO_PSO） is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.
基金supported by the National Natural Science Foundation of China(7127106671171065+1 种基金71202168)the Natural Science Foundation of Heilongjiang Province(GC13D506)
文摘This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.
基金supported by the National Natural Science Foundation of China(62373187)Forward-looking Layout Special Projects(ILA220591A22).
文摘To meet the requirements of modern air combat,an integrated fire/flight control(IFFC)system is designed to achieve automatic precision tracking and aiming for armed helicopters and release the pilot from heavy target burden.Considering the complex dynamic characteristics and the couplings of armed helicopters,an improved automatic attack system is con-structed to integrate the fire control system with the flight con-trol system into a unit.To obtain the optimal command signals,the algorithm is investigated to solve nonconvex optimization problems by the contracting Broyden Fletcher Goldfarb Shanno(C-BFGS)algorithm combined with the trust region method.To address the uncertainties in the automatic attack system,the memory nominal distribution and Wasserstein distance are introduced to accurately characterize the uncertainties,and the dual solvable problem is analyzed by using the duality the-ory,conjugate function,and dual norm.Simulation results verify the practicality and validity of the proposed method in solving the IFFC problem on the premise of satisfactory aiming accu-racy.
文摘Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.
基金supported by the National Natural Science Foundationof China(41001282)
文摘The azimuth resolution improvement problem is solved via a coherent combination of synthetic aperture radar （SAR） ima-ges with the quasi-non-overlapped Doppler bandwidth. Prior to the spectra combination, SAR images should be coregistered, while phase biases induced by topography, atmospheric propagation delays and baseline measurement errors should be calibrated. However, the coregistration accuracy suffers from large Doppler decorrelation caused by the quasi-non-overlapped Doppler band-width. Furthermore, the method used to estimate phase biases from interferogram of azimuth prefiltered SAR image pairs wil fail when there is no overlapped spectrum. The fringe simulation and maximum sharpness optimization are adopted to deal with the problems. Accordingly, a novel algorithm to coherently synthesize SAR images is presented. The experiment with the Terra SAR X-band （TerraSAR-X） satel ite data validates the performance of the presented method.
基金financially supported by the Ministry of Education, Science, and Technology (MEST)the National Research Foundation of Korea (NRF) through the Human Resource Training Project for Regional Innovationsupported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (No.20114010203040) grant funded by the Korean government’s Ministry of Knowledge Economy
文摘In a commercialized, fully artificial plant factory, artificial luminaire is arranged in a unified way using a general illumination theory, an actual measurement, or an empirical methodology. However, with these methods, lightings are implemented without considering specific optical characteristics of lighting or material characteristics of each component that constructs a cultivation system, resulting in an amount of light that becomes irregular. The amount of lighting is closely related with the growth and quality of crops, and the deviation between points where cultivated crops are located causes quality difference in the produced crops, thus impairing the economic feasibility of a plant factory. In this regard, a simulation to figure out an optimum lighting layout was performed. Arrangements based on the spectrum distribution of light source and reflector materials were implemented to ascertain the distance between lighting and height of lighting and gather information in the pre-treatment process to improve the uniformity of light in the plant cultivation system. Improvement of around 15% in light uniformity is achieved compared with the existing system after the simulation is carried out. This result would reduce the deviation in crop growth to make uniform quality crop production possible.
