Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when deal...Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.展开更多
This article focuses on the relationship between mathematical morphology operations and rough sets,mainly based on the context of image retrieval and the basic image correspondence problem.Mathematical morphological p...This article focuses on the relationship between mathematical morphology operations and rough sets,mainly based on the context of image retrieval and the basic image correspondence problem.Mathematical morphological procedures and set approximations in rough set theory have some clear parallels.Numerous initiatives have been made to connect rough sets with mathematical morphology.Numerous significant publications have been written in this field.Others attempt to show a direct connection between mathematical morphology and rough sets through relations,a pair of dual operations,and neighborhood systems.Rough sets are used to suggest a strategy to approximatemathematicalmorphology within the general paradigm of soft computing.A single framework is defined using a different technique that incorporates the key ideas of both rough sets and mathematical morphology.This paper examines rough set theory from the viewpoint of mathematical morphology to derive rough forms of themorphological structures of dilation,erosion,opening,and closing.These newly defined structures are applied to develop algorithm for the differential analysis of chest X-ray images from a COVID-19 patient with acute pneumonia and a health subject.The algorithm and rough morphological operations show promise for the delineation of lung occlusion in COVID-19 patients from chest X-rays.The foundations of mathematical morphology are covered in this article.After that,rough set theory ideas are taken into account,and their connections are examined.Finally,a suggested image retrieval application of the concepts from these two fields is provided.展开更多
In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction...In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction. The examples of boiler flame image processing show that the detected skeletons can present the geometric shape of flame images well.展开更多
Voids are one of the major defects in ball grid array （BGA） solder joints due to a large amount of outgassing flux that gets entrapped during reflow. X-ray nondestructive machines are used to make voids visible ...Voids are one of the major defects in ball grid array （BGA） solder joints due to a large amount of outgassing flux that gets entrapped during reflow. X-ray nondestructive machines are used to make voids visible as lighter areas inside the solder joints in X-ray images for detection However, it has always been difficult to analyze this problem automatically because of some challenges such as noise, inconsistent lighting and void-like artifacts. This study realized accurate extraction and automatic a-nalysis of void defects in solder joints by adopting a technical proposal, in which Otsu algorithm was used to segment solder balls and void defects were extracted through opening and closing operations and top-hat transformation in mathematical mor-phology. Experimental results show that the technical proposal mentioned here has good robustness and can be applied in the detection of voids in BGA solder joints.展开更多
A novel algorithm for image edge detection is presented. This algorithm combines the nonsubsampled contourlet transform and the mathematical morphology. First, the source image is decomposed by the nonsubsampled conto...A novel algorithm for image edge detection is presented. This algorithm combines the nonsubsampled contourlet transform and the mathematical morphology. First, the source image is decomposed by the nonsubsampled contourlet transform into multi-scale and multi-directional subbands. Then the edges in the high-frequency and low-frequency sub-bands are respectively extracted by the dualthreshold modulus maxima method and the mathematical morphology operator. Finally, the edges from the high- frequency and low-frequency sub-bands are integrated to the edges of the source image, which are refined, and isolated points are excluded to achieve the edges of the source image. The simulation results show that the proposed algorithm can effectively suppress noise, eliminate pseudo-edges and overcome the adverse effects caused by uneven illumination to a certain extent. Compared with the traditional methods such as LoG, Sobel, and Carmy operators and the modulus maxima algorithm, the proposed method can maintain sufficient positioning accuracy and edge details, and it can also make an improvement in the completeness, smoothness and clearness of the outline.展开更多
This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the ...This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise & remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.展开更多
Until now, understanding of polymer flocculation has remained restricted within the qualitative explanations of the bridge unite theory and the electricity neutralization theory, because people not only lacked the sys...Until now, understanding of polymer flocculation has remained restricted within the qualitative explanations of the bridge unite theory and the electricity neutralization theory, because people not only lacked the systemic knowl- edge of the polymer flocculation mechanism, the flocculation dynamic process study and the flocculation effect esti- mate, but also could not penetrate within the flocculation process microscopic field to obtain the structural character parameters such as floccule structure, the frame bridge models and so on. In this paper, not only coal slurry flocculation images were photographed by using the transmission electron microscope, but also the basic theory of the mathematical morphology was applied to the coal slurry flocculation image processing. The steps and methods of the mathematical morphology were expounded in detail. The micro-structural parameters such as the flocculate size and the bridge length were obtained, which combined the microscopic flocculation grain configuration observations with the macroscopic flocculation effect, so as to get the maximum amount of veracious information to describe and explain the whole floc- culation course by rule and line. On this basis, not only the flocculation models of polymers in the coal slurry are sug- gested, but the quantitative study on flocculation mechanism has been achieved.展开更多
By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used bas...By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used based on neural network. A modified Hopfield network was presented for image restoration. The greed algorithm with n-simultaneous updates and apartially asynchronous algorithm were combined, im- proving convergence and avoiding synchronization penalties. Mathematical morphology was widely applicated in digital image processing. The basic idea of mathematical mor- phology is to use construction element measure image morphology for solving under- stand problem. Presented advanced Cellular neural network that forms MMCNN equa- tion to be suit for mathematical morphology filter. It gave the theory of MMCNN dynamic extent and stable state. It was evidenced that arrived mathematical morphology filter through steady of dynamic precess in definite condition. The results of implementation were applied in detecting undergroug bin level.展开更多
Wood identification is a basic technique of wood science and industry. Pore features are among the most important identification features for hardwoods. We have used a method based on an analysis of quantitative pore ...Wood identification is a basic technique of wood science and industry. Pore features are among the most important identification features for hardwoods. We have used a method based on an analysis of quantitative pore feature, which differs from traditional qualitative methods. We applies mathematical morphology methods such as dilation and erosion, open and close transforma- tion of wood cross-sections, image repairing, noise filtering and edge detection to segment the pores from their background. Then the mean square errors （MSE） of pores were computed to describe the distribution of pores. Our experiment shows that it is easy to classify the pore features into three basic types, just as in traditional qualitative methods, but with the use of MSE of pores. This quantitative method improves wood identification considerably.展开更多
Gear vibration analysis and gear fault diagnosis are related to the multi-objective decision-making process of machinery equipment production, in which a large amount of data and information should be collected, and t...Gear vibration analysis and gear fault diagnosis are related to the multi-objective decision-making process of machinery equipment production, in which a large amount of data and information should be collected, and the relationship between supply/demand needs and available resources, between production and labor, and between enterprise benefit and social benefit should be balanced generally. Thus, the gear fault diagnosis technologies as well as the professional quality and technical quality are required to be very high. To conform to the forward development of mathematical modeling technology, it is urgent to implement safety product management with computer by using gear vibration analysis and gear fault diagnosis as methods for aiding the research and development of machinery gear fault diagnosis system. 7展开更多
This paper suggests a combined novel control strategy for DFIG based wind power systems(WPS)under both nonlinear and unbalanced load conditions.The combined control approach is designed by coordinating the machine sid...This paper suggests a combined novel control strategy for DFIG based wind power systems(WPS)under both nonlinear and unbalanced load conditions.The combined control approach is designed by coordinating the machine side converter(MSC)and the load side converter(LSC)control approaches.The proposed MSC control approach is designed by using a model predictive control(MPC)approach to generate appropriate real and reactive power.The MSC controller selects an appropriate rotor voltage vector by using a minimized optimization cost function for the converter operation.It shows its superiority by eliminating the requirement of transformation,switching table,and the PWM techniques.The proposed MSC reduces the cost,complexity,and computational burden of the WPS.On the other hand,the LSC control approach is designed by using a mathematical morphological technique(MMT)for appropriate DC component extraction.Due to the appropriate DC-component extraction,the WPS can compensate the harmonics during both steady and dynamic states.Further,the LSC controller also provides active power filter operation even under the shutdown of WPS condition.To verify the applicability of coordinated control operation,the WPS-based microgrid system is tested under various test conditions.The proposed WPS is designed by using a MATLAB/Simulink software.展开更多
In this paper, a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed, which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Wa...In this paper, a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed, which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Walsh-MM method, a scheme for power quality disturbances detection and classification is developed, which involves three steps: denoising, feature extraction and morphological clustering. First, various evolution rules of Walsh function are used to generate groups of SEs for the multiscale Walsh-ordered morphological operation, so the original signal can be denoised. Next, the fundamental wave of the denoised signal is suppressed by Hadamard matrix;thus, disturbances can be extracted. Finally, the Walsh power spectrum of the waveform extracted in the previous step is calculated, and the parameters of which are taken by morphological clustering to classify the disturbances. Simulation results reveal the proposed scheme can effectively detect and classify disturbances, and the Walsh-MM method is less affected by noise and only involves simple calculation, which has a potential to be implemented in hardware and more suitable for real-time application.展开更多
This paper proposes a new model,which consists of a mathematical morphology(MM)decomposer and two long short term memory(LSTM)networks,to perform ultra-short term wind speed forecast.The MM decomposer is developed in ...This paper proposes a new model,which consists of a mathematical morphology(MM)decomposer and two long short term memory(LSTM)networks,to perform ultra-short term wind speed forecast.The MM decomposer is developed in order to improve the forecast accuracy,which separates the wind speed into two parts:a stationary long-term baseline and a nonstationary short-term residue.Afterwards,two LSTM networks are implemented to forecast the baseline and residue,respectively.Besides,this paper makes an integrated forecast that takes into account multiple climate factors,such as temperature and air pressure.The baseline,temperature and air pressure are used as the inputs of baseline network for training and prediction,and the baseline,residue,temperature and air pressure are used as the inputs of residue network for training and prediction.The performance of the proposed model has been validated using data collected from the Australian Meteorological Station,which is compared with least squares-support vector machine(LS-SVM),back-propagation artificial neural network(BPNN),LSTM,MM-LS-SVM,and MM-BPNN.The results demonstrate that the proposed model is more suitable to solve non-stationary time-series forecast,and achieves higher accuracy than the other models under various conditions.展开更多
Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature ex...Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.展开更多
The strom identification, tracking, and forecasting method is one of the important nowcasting techniques. Accurate storm identification is a prerequisite for successful storm tracking and forecasting. Storm identifica...The strom identification, tracking, and forecasting method is one of the important nowcasting techniques. Accurate storm identification is a prerequisite for successful storm tracking and forecasting. Storm identification faces two difficulties： one is false merger and the other is failure to isolate adjacent storms within a cluster of storms. The TITAN （Thunderstorm Identification, Tracking, Analysis, and Nowcasting） algorithm is apt to identify adjacent storm cells as one storm because it uses a single reflectivity threshold. The SCIT （Storm Cell Identification and Tracking） algorithm uses seven reflectivity thresholds and therefore is capable of isolating adjacent storm cells, but it discards the results identified by the lower threshold, leading to the loss of the internal structure information of storms. Both TITAN and SCIT have the problem of failing to satisfactorily identify false merger. To overcome these shortcomings, this paper proposes a novel approach based on mathematical morphology. The approach first applies the single threshold identification followed by implementing an erosion process to mitigate the false merger problem. During multi-threshold identification stages, dilation operation is performed against the storm cells which are just obtained by the higher threshold identification, until the storm edges touch each other or touch the edges of the previous storms identified by the lower threshold. The results of experiment show that by combining the strengths of the dilation and erosion operations, this approach is able to mitigate the false merger problem as well as maintain the internal structure of sub-storms when isolating storms within a cluster of storms.展开更多
Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis.These data-intensive applications have high requirements during ...Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis.These data-intensive applications have high requirements during hardware implementation that are challenging for conventional hardware platforms such as central processing units(CPUs)and graphics processing units(GPUs).Computation-in-memory(CIM)provides a possible solution for highly efficient morphology operations.In this study,we demonstrate the application of morphology operation with a novel memristor-based auto-detection architecture and demonstrate non-neuromoq)hic computation on a multi-array-based memristor system.Pixel-by-pixel logic computations with low parallelism are converted to parallel operations using memristors.Moreover,hardware-implemented computer-integrated manufacturing was used to experimentally demonstrate typical defect detection tasks in integrated circuit(IC)manufacturing and medical image analysis.In addition,we developed a new implementation scheme employing a four-layer network to realize small-object detection with high parallelism.The system benchmark based on the hardware measurement results showed significant improvement in the energy efficiency by approximately 358 times and 32 times more than when a CPU and GPU were employed,respectively,exhibiting the advantage of the proposed memristor-based morphology operation.展开更多
This paper proposes a motion-based region growing segmentation scheme, which incorporatesluminance and motion information simultaneously and uses morphological tools such as open-close byreconstruction and the region-...This paper proposes a motion-based region growing segmentation scheme, which incorporatesluminance and motion information simultaneously and uses morphological tools such as open-close byreconstruction and the region-growing version of the watershed algorithm. The main advantage of this scheme is thatthe resultant objects ore characterized by a coherent motion and foe moving object boundaries are precisely located.Simulation results demonstrate the effiency of the Proposed scheme.展开更多
Skeleton is an important topological descriptor of an image and is widely used in the fields of image analysis. In this paper we present an algorithm based on morphological operations for extracting skeleton from a bi...Skeleton is an important topological descriptor of an image and is widely used in the fields of image analysis. In this paper we present an algorithm based on morphological operations for extracting skeleton from a binary image. Since the original image can be partially or completely reconstructed from the skeleton, this algorithm which works in both analogUe and digital space is useful in image coding and feature description. A fast algorithm for skeletonizing and reconstrUcting digital images and the results of the fast algorithm are also given.展开更多
Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of ...Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.展开更多
文摘Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.
文摘This article focuses on the relationship between mathematical morphology operations and rough sets,mainly based on the context of image retrieval and the basic image correspondence problem.Mathematical morphological procedures and set approximations in rough set theory have some clear parallels.Numerous initiatives have been made to connect rough sets with mathematical morphology.Numerous significant publications have been written in this field.Others attempt to show a direct connection between mathematical morphology and rough sets through relations,a pair of dual operations,and neighborhood systems.Rough sets are used to suggest a strategy to approximatemathematicalmorphology within the general paradigm of soft computing.A single framework is defined using a different technique that incorporates the key ideas of both rough sets and mathematical morphology.This paper examines rough set theory from the viewpoint of mathematical morphology to derive rough forms of themorphological structures of dilation,erosion,opening,and closing.These newly defined structures are applied to develop algorithm for the differential analysis of chest X-ray images from a COVID-19 patient with acute pneumonia and a health subject.The algorithm and rough morphological operations show promise for the delineation of lung occlusion in COVID-19 patients from chest X-rays.The foundations of mathematical morphology are covered in this article.After that,rough set theory ideas are taken into account,and their connections are examined.Finally,a suggested image retrieval application of the concepts from these two fields is provided.
文摘In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction. The examples of boiler flame image processing show that the detected skeletons can present the geometric shape of flame images well.
基金National Science and Technology Major Project of the Ministry of Science And Technology of China(No.2013YQ240803)Shanxi Programs for Science and Technology Development(Nos.20140321010-02,201603D121040-1)Scientific and Technological Innovation Programs of Higher Education Institutions of Shanxi Province(No.2013063)
文摘Voids are one of the major defects in ball grid array （BGA） solder joints due to a large amount of outgassing flux that gets entrapped during reflow. X-ray nondestructive machines are used to make voids visible as lighter areas inside the solder joints in X-ray images for detection However, it has always been difficult to analyze this problem automatically because of some challenges such as noise, inconsistent lighting and void-like artifacts. This study realized accurate extraction and automatic a-nalysis of void defects in solder joints by adopting a technical proposal, in which Otsu algorithm was used to segment solder balls and void defects were extracted through opening and closing operations and top-hat transformation in mathematical mor-phology. Experimental results show that the technical proposal mentioned here has good robustness and can be applied in the detection of voids in BGA solder joints.
基金The National Key Technologies R&D Program during the 12th Five-Year Period of China(No.2012BAJ23B02)Science and Technology Support Program of Jiangsu Province(No.BE2010606)
文摘A novel algorithm for image edge detection is presented. This algorithm combines the nonsubsampled contourlet transform and the mathematical morphology. First, the source image is decomposed by the nonsubsampled contourlet transform into multi-scale and multi-directional subbands. Then the edges in the high-frequency and low-frequency sub-bands are respectively extracted by the dualthreshold modulus maxima method and the mathematical morphology operator. Finally, the edges from the high- frequency and low-frequency sub-bands are integrated to the edges of the source image, which are refined, and isolated points are excluded to achieve the edges of the source image. The simulation results show that the proposed algorithm can effectively suppress noise, eliminate pseudo-edges and overcome the adverse effects caused by uneven illumination to a certain extent. Compared with the traditional methods such as LoG, Sobel, and Carmy operators and the modulus maxima algorithm, the proposed method can maintain sufficient positioning accuracy and edge details, and it can also make an improvement in the completeness, smoothness and clearness of the outline.
基金Foundation item: Under the auspices of the National Natural Science Foundation of China (No. 49971055
文摘This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise & remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.
文摘Until now, understanding of polymer flocculation has remained restricted within the qualitative explanations of the bridge unite theory and the electricity neutralization theory, because people not only lacked the systemic knowl- edge of the polymer flocculation mechanism, the flocculation dynamic process study and the flocculation effect esti- mate, but also could not penetrate within the flocculation process microscopic field to obtain the structural character parameters such as floccule structure, the frame bridge models and so on. In this paper, not only coal slurry flocculation images were photographed by using the transmission electron microscope, but also the basic theory of the mathematical morphology was applied to the coal slurry flocculation image processing. The steps and methods of the mathematical morphology were expounded in detail. The micro-structural parameters such as the flocculate size and the bridge length were obtained, which combined the microscopic flocculation grain configuration observations with the macroscopic flocculation effect, so as to get the maximum amount of veracious information to describe and explain the whole floc- culation course by rule and line. On this basis, not only the flocculation models of polymers in the coal slurry are sug- gested, but the quantitative study on flocculation mechanism has been achieved.
文摘By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used based on neural network. A modified Hopfield network was presented for image restoration. The greed algorithm with n-simultaneous updates and apartially asynchronous algorithm were combined, im- proving convergence and avoiding synchronization penalties. Mathematical morphology was widely applicated in digital image processing. The basic idea of mathematical mor- phology is to use construction element measure image morphology for solving under- stand problem. Presented advanced Cellular neural network that forms MMCNN equa- tion to be suit for mathematical morphology filter. It gave the theory of MMCNN dynamic extent and stable state. It was evidenced that arrived mathematical morphology filter through steady of dynamic precess in definite condition. The results of implementation were applied in detecting undergroug bin level.
文摘Wood identification is a basic technique of wood science and industry. Pore features are among the most important identification features for hardwoods. We have used a method based on an analysis of quantitative pore feature, which differs from traditional qualitative methods. We applies mathematical morphology methods such as dilation and erosion, open and close transforma- tion of wood cross-sections, image repairing, noise filtering and edge detection to segment the pores from their background. Then the mean square errors （MSE） of pores were computed to describe the distribution of pores. Our experiment shows that it is easy to classify the pore features into three basic types, just as in traditional qualitative methods, but with the use of MSE of pores. This quantitative method improves wood identification considerably.
文摘Gear vibration analysis and gear fault diagnosis are related to the multi-objective decision-making process of machinery equipment production, in which a large amount of data and information should be collected, and the relationship between supply/demand needs and available resources, between production and labor, and between enterprise benefit and social benefit should be balanced generally. Thus, the gear fault diagnosis technologies as well as the professional quality and technical quality are required to be very high. To conform to the forward development of mathematical modeling technology, it is urgent to implement safety product management with computer by using gear vibration analysis and gear fault diagnosis as methods for aiding the research and development of machinery gear fault diagnosis system. 7
基金Assistance provided by Council of scientific and industrial research(CSIR),Government of India,under the acknowledgment number 143460/2K19/1(File:09/969(0013)/2K20-EMR-I)and Siksha O Anusandhan(Deemed to be University).
文摘This paper suggests a combined novel control strategy for DFIG based wind power systems(WPS)under both nonlinear and unbalanced load conditions.The combined control approach is designed by coordinating the machine side converter(MSC)and the load side converter(LSC)control approaches.The proposed MSC control approach is designed by using a model predictive control(MPC)approach to generate appropriate real and reactive power.The MSC controller selects an appropriate rotor voltage vector by using a minimized optimization cost function for the converter operation.It shows its superiority by eliminating the requirement of transformation,switching table,and the PWM techniques.The proposed MSC reduces the cost,complexity,and computational burden of the WPS.On the other hand,the LSC control approach is designed by using a mathematical morphological technique(MMT)for appropriate DC component extraction.Due to the appropriate DC-component extraction,the WPS can compensate the harmonics during both steady and dynamic states.Further,the LSC controller also provides active power filter operation even under the shutdown of WPS condition.To verify the applicability of coordinated control operation,the WPS-based microgrid system is tested under various test conditions.The proposed WPS is designed by using a MATLAB/Simulink software.
基金supported by the National Natural Science Foundation of China(52077081)。
文摘In this paper, a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed, which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Walsh-MM method, a scheme for power quality disturbances detection and classification is developed, which involves three steps: denoising, feature extraction and morphological clustering. First, various evolution rules of Walsh function are used to generate groups of SEs for the multiscale Walsh-ordered morphological operation, so the original signal can be denoised. Next, the fundamental wave of the denoised signal is suppressed by Hadamard matrix;thus, disturbances can be extracted. Finally, the Walsh power spectrum of the waveform extracted in the previous step is calculated, and the parameters of which are taken by morphological clustering to classify the disturbances. Simulation results reveal the proposed scheme can effectively detect and classify disturbances, and the Walsh-MM method is less affected by noise and only involves simple calculation, which has a potential to be implemented in hardware and more suitable for real-time application.
基金This work was supported by Fundamental Research Funds for Central Universities,(No.2019MS014)Natural Science Foundation of Guangdong Province(No.2018A030313822).
文摘This paper proposes a new model,which consists of a mathematical morphology(MM)decomposer and two long short term memory(LSTM)networks,to perform ultra-short term wind speed forecast.The MM decomposer is developed in order to improve the forecast accuracy,which separates the wind speed into two parts:a stationary long-term baseline and a nonstationary short-term residue.Afterwards,two LSTM networks are implemented to forecast the baseline and residue,respectively.Besides,this paper makes an integrated forecast that takes into account multiple climate factors,such as temperature and air pressure.The baseline,temperature and air pressure are used as the inputs of baseline network for training and prediction,and the baseline,residue,temperature and air pressure are used as the inputs of residue network for training and prediction.The performance of the proposed model has been validated using data collected from the Australian Meteorological Station,which is compared with least squares-support vector machine(LS-SVM),back-propagation artificial neural network(BPNN),LSTM,MM-LS-SVM,and MM-BPNN.The results demonstrate that the proposed model is more suitable to solve non-stationary time-series forecast,and achieves higher accuracy than the other models under various conditions.
基金supported by National Natural Science Foundation of China (No. 61763037)Inner Mongolia Autonomous Region Natural Science Foundation of China(No. 2019LH06007)Science and Technology Plan Project of Inner Mongolia (No. 2019,2020GG028)。
文摘Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.
基金Supported by National Basic Research Program of China(2004CB418300)Ph.D.Programs Foundation of the Ministry of Education of China(20040001008).
文摘The strom identification, tracking, and forecasting method is one of the important nowcasting techniques. Accurate storm identification is a prerequisite for successful storm tracking and forecasting. Storm identification faces two difficulties： one is false merger and the other is failure to isolate adjacent storms within a cluster of storms. The TITAN （Thunderstorm Identification, Tracking, Analysis, and Nowcasting） algorithm is apt to identify adjacent storm cells as one storm because it uses a single reflectivity threshold. The SCIT （Storm Cell Identification and Tracking） algorithm uses seven reflectivity thresholds and therefore is capable of isolating adjacent storm cells, but it discards the results identified by the lower threshold, leading to the loss of the internal structure information of storms. Both TITAN and SCIT have the problem of failing to satisfactorily identify false merger. To overcome these shortcomings, this paper proposes a novel approach based on mathematical morphology. The approach first applies the single threshold identification followed by implementing an erosion process to mitigate the false merger problem. During multi-threshold identification stages, dilation operation is performed against the storm cells which are just obtained by the higher threshold identification, until the storm edges touch each other or touch the edges of the previous storms identified by the lower threshold. The results of experiment show that by combining the strengths of the dilation and erosion operations, this approach is able to mitigate the false merger problem as well as maintain the internal structure of sub-storms when isolating storms within a cluster of storms.
基金the National Natural Science Foundation of China(Grants No.92064001,61851404,and 61874169)the IoT Intelligent Microsystem Center of Tsinghua University-China Mobile Joint Research Institute.
文摘Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis.These data-intensive applications have high requirements during hardware implementation that are challenging for conventional hardware platforms such as central processing units(CPUs)and graphics processing units(GPUs).Computation-in-memory(CIM)provides a possible solution for highly efficient morphology operations.In this study,we demonstrate the application of morphology operation with a novel memristor-based auto-detection architecture and demonstrate non-neuromoq)hic computation on a multi-array-based memristor system.Pixel-by-pixel logic computations with low parallelism are converted to parallel operations using memristors.Moreover,hardware-implemented computer-integrated manufacturing was used to experimentally demonstrate typical defect detection tasks in integrated circuit(IC)manufacturing and medical image analysis.In addition,we developed a new implementation scheme employing a four-layer network to realize small-object detection with high parallelism.The system benchmark based on the hardware measurement results showed significant improvement in the energy efficiency by approximately 358 times and 32 times more than when a CPU and GPU were employed,respectively,exhibiting the advantage of the proposed memristor-based morphology operation.
文摘This paper proposes a motion-based region growing segmentation scheme, which incorporatesluminance and motion information simultaneously and uses morphological tools such as open-close byreconstruction and the region-growing version of the watershed algorithm. The main advantage of this scheme is thatthe resultant objects ore characterized by a coherent motion and foe moving object boundaries are precisely located.Simulation results demonstrate the effiency of the Proposed scheme.
文摘Skeleton is an important topological descriptor of an image and is widely used in the fields of image analysis. In this paper we present an algorithm based on morphological operations for extracting skeleton from a binary image. Since the original image can be partially or completely reconstructed from the skeleton, this algorithm which works in both analogUe and digital space is useful in image coding and feature description. A fast algorithm for skeletonizing and reconstrUcting digital images and the results of the fast algorithm are also given.
基金supported by the National Natural Science Foundation of China(Project No.51767018)Natural Science Foundation of Gansu Province(Project No.23JRRA836).
文摘Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.
