Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estima...Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.展开更多
To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades(WTB),this paper proposes a technique that combines morpho...To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades(WTB),this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm.First,mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image.The algorithm employs entropy as the objective function to guide the iteration process of image enhancement,selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations,effectively enhancing the detail features of defect regions and improving the contrast between defects and background.Afterwards,grayscale inversion is performed on the enhanced infrared defect image to better adapt to the improved Otsu algorithm.Finally,by introducing a parameter K to adjust the calculation of inter-class variance in the Otsu method,the weight of the target pixels is increased.Combined with the adaptive iterative threshold algorithm,the threshold selection process is further fine-tuned.Experimental results show that compared to traditional Otsu algorithms and other improvements,the proposed method has significant advantages in terms of defect detection accuracy and reducing false positive rates.The average defect detection rate approaches 1,and the average Hausdorff distance decreases to 0.825,indicating strong robustness and accuracy of the method.展开更多
To further improve the boiler ash ratio detection methods and resource utilization, through image processing technology for boiler ash ratio analysis, the article first studied the one-dimensional Otsu algorithm, and ...To further improve the boiler ash ratio detection methods and resource utilization, through image processing technology for boiler ash ratio analysis, the article first studied the one-dimensional Otsu algorithm, and then for the one-dimensional Otsu algorithm, in order to improve the accuracy of the algorithm, then it puts forward a two-dimensional Otsu algorithm. Finally the two-dimensional Otsu algorithm combined with the one-dimensional Otsu algorithm and the improved Otsu algorithm. By analyzing the improved Otsu algorithm, this paper considers the pixel gray value, neighborhood information, excluding light, noise and the relative efficiency of one-dimensional Otsu algorithm higher accuracy. The relative dimensional Otsu algorithm operating efficiency has been greatly improved. Improved Otsu algorithm in dealing with boiler ash ratio detection has played a very good part in the ecological environment, economic development and some other important aspects.展开更多
The bat algorithm(BA)is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness,which can be used to find the globally opti...The bat algorithm(BA)is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness,which can be used to find the globally optimal solutions for various optimisation problems.Knowing the recent criticises of the originality of equations,the principle of BA is concise and easy to implement,and its mathematical structure can be seen as a hybrid particle swarm with simulated annealing.In this research,the authors focus on the performance optimisation of BA as a solver rather than discussing its originality issues.In terms of operation effect,BA has an acceptable convergence speed.However,due to the low proportion of time used to explore the search space,it is easy to converge prematurely and fall into the local optima.The authors propose an adaptive multi-stage bat algorithm(AMSBA).By tuning the algorithm's focus at three different stages of the search process,AMSBA can achieve a better balance between exploration and exploitation and improve its exploration ability by enhancing its performance in escaping local optima as well as maintaining a certain convergence speed.Therefore,AMSBA can achieve solutions with better quality.A convergence analysis was conducted to demonstrate the global convergence of AMSBA.The authors also perform simulation experiments on 30 benchmark functions from IEEE CEC 2017 as the objective functions and compare AMSBA with some original and improved swarm-based algorithms.The results verify the effectiveness and superiority of AMSBA.AMSBA is also compared with eight representative optimisation algorithms on 10 benchmark functions derived from IEEE CEC 2020,while this experiment is carried out on five different dimensions of the objective functions respectively.A balance and diversity analysis was performed on AMSBA to demonstrate its improvement over the original BA in terms of balance.AMSBA was also applied to the multi-threshold image segmentation of Citrus Macular disease,which is a bacterial infection that causes lesions on citrus trees.The segmentation results were analysed by comparing each comparative algorithm's peak signal-to-noise ratio,structural similarity index and feature similarity index.The results show that the proposed BA-based algorithm has apparent advantages,and it can effectively segment the disease spots from citrus leaves when the segmentation threshold is at a low level.Based on a comprehensive study,the authors think the proposed optimiser has mitigated the main drawbacks of the BA,and it can be utilised as an effective optimisation tool.展开更多
基金supported by the National Natural Science Foundation of China (61302188)the Nanjing University of Science and Technology Research Foundation (2010ZDJH05)
文摘Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.
基金supported by Natural Science Foundation of Jilin Province(YDZJ202401352ZYTS).
文摘To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades(WTB),this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm.First,mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image.The algorithm employs entropy as the objective function to guide the iteration process of image enhancement,selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations,effectively enhancing the detail features of defect regions and improving the contrast between defects and background.Afterwards,grayscale inversion is performed on the enhanced infrared defect image to better adapt to the improved Otsu algorithm.Finally,by introducing a parameter K to adjust the calculation of inter-class variance in the Otsu method,the weight of the target pixels is increased.Combined with the adaptive iterative threshold algorithm,the threshold selection process is further fine-tuned.Experimental results show that compared to traditional Otsu algorithms and other improvements,the proposed method has significant advantages in terms of defect detection accuracy and reducing false positive rates.The average defect detection rate approaches 1,and the average Hausdorff distance decreases to 0.825,indicating strong robustness and accuracy of the method.
文摘To further improve the boiler ash ratio detection methods and resource utilization, through image processing technology for boiler ash ratio analysis, the article first studied the one-dimensional Otsu algorithm, and then for the one-dimensional Otsu algorithm, in order to improve the accuracy of the algorithm, then it puts forward a two-dimensional Otsu algorithm. Finally the two-dimensional Otsu algorithm combined with the one-dimensional Otsu algorithm and the improved Otsu algorithm. By analyzing the improved Otsu algorithm, this paper considers the pixel gray value, neighborhood information, excluding light, noise and the relative efficiency of one-dimensional Otsu algorithm higher accuracy. The relative dimensional Otsu algorithm operating efficiency has been greatly improved. Improved Otsu algorithm in dealing with boiler ash ratio detection has played a very good part in the ecological environment, economic development and some other important aspects.
基金BBSRC,Grant/Award Number:RM32G0178B8National Natural Science Foundation of China,Grant/Award Numbers:U19A2061,U1809209,62076185+11 种基金Science and Technology Development Project of Jilin Province,Grant/Award Number:20190301024NYJilin Provincial Industrial Innovation Special Fund Project,Grant/Award Number:2018C039-3MRC,Grant/Award Number:MC_PC_17171Royal Society,Grant/Award Number:RP202G0230BHF,Grant/Award Number:AA/18/3/34220Hope Foundation for Cancer Research,Grant/Award Number:RM60G0680GCRF,Grant/Award Number:P202PF11Sino-UK Industrial Fund,Grant/Award Number:RP202G0289LIAS,Grant/Award Numbers:P202ED10,P202RE969Data Science Enhancement Fund,Grant/Award Number:P202RE237Fight for Sight,Grant/Award Number:24NN201Sino-UK Education Fund,Grant/Award Number:OP202006。
文摘The bat algorithm(BA)is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness,which can be used to find the globally optimal solutions for various optimisation problems.Knowing the recent criticises of the originality of equations,the principle of BA is concise and easy to implement,and its mathematical structure can be seen as a hybrid particle swarm with simulated annealing.In this research,the authors focus on the performance optimisation of BA as a solver rather than discussing its originality issues.In terms of operation effect,BA has an acceptable convergence speed.However,due to the low proportion of time used to explore the search space,it is easy to converge prematurely and fall into the local optima.The authors propose an adaptive multi-stage bat algorithm(AMSBA).By tuning the algorithm's focus at three different stages of the search process,AMSBA can achieve a better balance between exploration and exploitation and improve its exploration ability by enhancing its performance in escaping local optima as well as maintaining a certain convergence speed.Therefore,AMSBA can achieve solutions with better quality.A convergence analysis was conducted to demonstrate the global convergence of AMSBA.The authors also perform simulation experiments on 30 benchmark functions from IEEE CEC 2017 as the objective functions and compare AMSBA with some original and improved swarm-based algorithms.The results verify the effectiveness and superiority of AMSBA.AMSBA is also compared with eight representative optimisation algorithms on 10 benchmark functions derived from IEEE CEC 2020,while this experiment is carried out on five different dimensions of the objective functions respectively.A balance and diversity analysis was performed on AMSBA to demonstrate its improvement over the original BA in terms of balance.AMSBA was also applied to the multi-threshold image segmentation of Citrus Macular disease,which is a bacterial infection that causes lesions on citrus trees.The segmentation results were analysed by comparing each comparative algorithm's peak signal-to-noise ratio,structural similarity index and feature similarity index.The results show that the proposed BA-based algorithm has apparent advantages,and it can effectively segment the disease spots from citrus leaves when the segmentation threshold is at a low level.Based on a comprehensive study,the authors think the proposed optimiser has mitigated the main drawbacks of the BA,and it can be utilised as an effective optimisation tool.
