A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance m...A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance method.Firstly,an altitude-energy profile is designed,and the bank angle is derived analytically as the initial iteration value for the predictor-corrector method.The predictor-corrector guidance method has been improved by deriving an analytical form for predicting the range-to-go error,which greatly accelerates the iterative speed.Then,a segmented guidance algorithm is proposed.The above analytically predictor-corrector guidance method is adopted when the energy exceeds an energy threshold.When the energy is less than the threshold,the equidistant test method is used to calculate the bank angle command,which ensures guidance accuracy as well as computational efficiency.Additionally,an adaptive guidance cycle strategy is applied to reduce the computational time of the reentry guidance trajectory.Finally,the accuracy and robustness of the proposed method are verified through a series of simulations and Monte-Carlo experiments.Compared with the traditional integral method,the proposed method requires 75%less computation time on average and achieves a lower landing error.展开更多
The fatigue resistance of casting polyurethane(CPU)is crucial in various sectors,such as construction,healthcare,and the automotive industry.Despite its importance,no studies have reported on the fatigue threshold of ...The fatigue resistance of casting polyurethane(CPU)is crucial in various sectors,such as construction,healthcare,and the automotive industry.Despite its importance,no studies have reported on the fatigue threshold of CPU.This study employed an advanced Intrinsic Strength Analyzer(ISA)to evaluate the fatigue threshold of CPUs,systematically exploring the effects of three types of isocyanates(PPDI,NDI,TDI)that contribute to hard segment structures based on the cutting method.Employing multiple advanced characterization techniques(XRD,TEM,DSC,AFM),the results indicate that PPDI-based polyurethane exhibits the highest fatigue threshold(182.89 J/m^(2))due to a highest phase separation and a densely packed spherulitic structure,although the hydrogen bonding degree is the lowest(48.3%).Conversely,NDI-based polyurethane,despite having the high hydrogen bonding degree(53.6%),exhibits moderate fatigue performance(122.52 J/m^(2)),likely due to a more scattered microstructure.TDI-based polyurethane,with the highest hydrogen bonding degree(59.1%)but absence of spherulitic structure,shows the lowest fatigue threshold(46.43 J/m^(2)).Compared to common rubbers(NR,NBR,EPDM,BR),the superior fatigue performance of CPU is attributed to its well-organized microstructure,polyurethane possesses a higher fatigue threshold due to its high phase separation degree and orderly and dense spherulitic structure which enhances energy dissipation and reduces crack propagation.展开更多
To segment the tumor region precisely is a prerequisite for ultrasound navigation and treatment. In this paper, a normalized cut method to segment tumor ultrasound image is proposed by means of simple linear iterative...To segment the tumor region precisely is a prerequisite for ultrasound navigation and treatment. In this paper, a normalized cut method to segment tumor ultrasound image is proposed by means of simple linear iterative clustering for presegmentation procedure. The first step, we use simple linear iterative clustering algorithm to divide the image into a number of homogeneous over-segmented regions. Then, these regions are regarded as nodes, and a similarity matrix is constructed by comparing the histograms of each two regions. Finally, we apply the Ncut method to merging the over-segmented regions, then the image segmentation process is completed. The results show that the proposed segmentation scheme handles the strong speckle noise, low contrast, and weak edges well in ultrasound image. Our method has high segmentation precision and computation efficiency than the pixel-based Ncut method.展开更多
In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating ...In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating the reliability of equipment with a single model may lead to severer errors.However, if lifetime is divided into several different intervals according to the characteristics of its failure rate, piecewise fitting can more accurately approximate the failure rate of equipment. Therefore, in this paper, failure rate is regarded as a piecewise function, and two kinds of segmented distribution are put forward to evaluate reliability. In order to estimate parameters in the segmented reliability function, Bayesian estimation and maximum likelihood estimation（MLE） of the segmented distribution are discussed in this paper. Since traditional information criterion is not suitable for the segmented distribution, an improved information criterion is proposed to test and evaluate the segmented reliability model in this paper. After a great deal of testing and verification,the segmented reliability model and its estimation methods presented in this paper are proven more efficient and accurate than the traditional non-segmented single model, especially when the change of the failure rate is time-phased or multimodal. The significant performance of the segmented reliability model in evaluating reliability of proximity sensors of leading-edge flap in civil aircraft indicates that the segmented distribution and its estimation method in this paper could be useful and accurate.展开更多
This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind o...This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind of image segmentation is still a challenging task.The proposed method uses both the region and boundary information to achieve accurate segmentation results.The region information can help to identify rough region of interest and prevent the boundary leakage problem.It makes use of normalized nonlocal comparisons between pairs of patches in each region,and a heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation.The boundary information can help to detect the precise location of the target object,it makes use of the geodesic active contour model to obtain the target boundary.The corresponding variational segmentation problem is implemented by a level set formulation.We use an internal energy term for geometric active contours to penalize the deviation of the level set function from a signed distance function.At last,experimental results on synthetic images and real images are shown in the paper with promising results.展开更多
From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Consi...From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Considering the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become crucial.Identification through the use of Computed Tomography(CT)images is an efficient and quick means.Therefore,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images.In this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of COVID-19.Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem.This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum.In addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population.Two experiments were carried out to test the performance of the proposed algorithm.First,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions.Secondly,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics.It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics.Furthermore,the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO.展开更多
In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back proj...In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back projection analysis.Data in two frequency bands(0.5-2 Hz and 1-3 Hz)are used in the imaging processes.The results show that the rupture of the first event extends about 200 km to the northeast and about 150 km to the southwest,lasting~90 s in total.The southwestern rupture is triggered by the northeastern rupture,demonstrating a sequential bidirectional unilateral rupture pattern.The rupture of the second event extends approximately 80 km in both northeast and west directions,lasting~35 s in total and demonstrates a typical bilateral rupture feature.The cascading ruptures on both sides also reflect the occurrence of selective rupture behaviors on bifurcated faults.In addition,we observe super-shear ruptures on certain fault sections with relatively straight fault structures and sparse aftershocks.展开更多
A new segmentation method has been developed for PET fast imaging. The technique automatically segments the transmission images into different anatomical regions, it efficiently reduced the PET transmission scan time....A new segmentation method has been developed for PET fast imaging. The technique automatically segments the transmission images into different anatomical regions, it efficiently reduced the PET transmission scan time. The result shows that this method gives only 3 min-scan time which is perfect for attenuation correction of the PET images instead of the original 15-30 min-scan time. This approach has been successfully tested both on phantom and clinical data.展开更多
Effective and efficient SAR image segmentation has a significant role in coastal zone interpretation. In this paper, a coastal zone segmentation model is proposed based on Potts model. By introducing edge self-adaptio...Effective and efficient SAR image segmentation has a significant role in coastal zone interpretation. In this paper, a coastal zone segmentation model is proposed based on Potts model. By introducing edge self-adaption parameter and modifying noisy data term, the proposed variational model provides a good solution for the coastal zone SAR image with common characteristics of inherent speckle noise and complicated geometrical details. However, the proposed model is difficult to solve due to to its nonlinear, non-convex and non-smooth characteristics. Followed by curve evolution theory and operator splitting method, the minimization problem is reformulated as a constrained minimization problem. A fast alternating minimization iterative scheme is designed to implement coastal zone segmentation. Finally, various two-stage and multiphase experimental results illustrate the advantage of the proposed segmentation model, and indicate the high computation efficiency of designed numerical approximation algorithm.展开更多
Based on the multi-rigid body discretization model, namely, finite segment model,a chain multi-rigid-body-hinge-spring system model of a beam was presented, then a nonlinear parametrically exacted vibration equation o...Based on the multi-rigid body discretization model, namely, finite segment model,a chain multi-rigid-body-hinge-spring system model of a beam was presented, then a nonlinear parametrically exacted vibration equation of multi-degrees of freedom system was established using the coordination transformation method, and its resonance fields were derived by the restriction parameter method, that is, the dynamical buckling analysis of the beam. Because the deformation of a beam is not restricted by the discrete model and dynamic equation, the post buckling analysis can be done in above math model. The numerical solutions of a few examples were obtained by direct integrated method, which shows that the mechanical and math model gotten is correct.展开更多
In biology ferment engineering,accurate statistics of the quantity of bacteria is one of the most important subjects. In this paper,the quantity of bacteria which was observed traditionally manuauy can be detected aut...In biology ferment engineering,accurate statistics of the quantity of bacteria is one of the most important subjects. In this paper,the quantity of bacteria which was observed traditionally manuauy can be detected automatically. Image acquisition and processing system is designed to accomplish image preprocessing,image segmentation and statistics of the quantity of bacteria. Segmentation of bacteria images is successfully realized by means of a region-based level set method and then the quantity of bacteria is computed precisely,which plays an important role in optimizing the growth conditions of bacteria.展开更多
On the basis of the recent geological surveys and the relevant studies of the Xianshuihe fault zone, this paper analyzes the seismogenic mechanism of some faults and characteristic morphology on the fault zone by the ...On the basis of the recent geological surveys and the relevant studies of the Xianshuihe fault zone, this paper analyzes the seismogenic mechanism of some faults and characteristic morphology on the fault zone by the boundary element method and discusses the fault segmentation and the related distribution of the earthquake ruptures. The main conclusions are: For the first order segmentation, the Xianshuihe fault zone can be divided into three major segments (the northwestern Luhuo-Qianning segment, the middle linking fracture region and the southeastern Kangding segment). Among them, the differences are not only in geometry and structure, but also in mechanical property and dynamics. Some of the characteristic morphologies on the Xianshuihe fault zone reflect the effects in cumulative deformation due to long-term fault movement, and reveal the fault segmentation in different orders. Such morphologies control, to some extent, the developments and the distributions of the earthquake ruptures on the fault zone.展开更多
There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning met...There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate enough.Both of them cannot generate entire segmentation information.In order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is proposed.This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances.It includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different objects.Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and quantitatively.The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method.This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.展开更多
The principle and method of flexible multibody system dynamics is presented. The dynamic equation have been developed by means of Huston's method based on Kane's equation. In which the flexible members with g...The principle and method of flexible multibody system dynamics is presented. The dynamic equation have been developed by means of Huston's method based on Kane's equation. In which the flexible members with general cross-section characters were divided into finite segment models under the assumption of small strain. In order to decrease the degrees of freedom of the system and increase the efficiency of numerical calculation. the mode transformation has been introduced. A typical example is presented. and the foregoing method has been perfectly verified.展开更多
The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions a...The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.展开更多
Using traditional particle tracking velocimetry based on optical flow for measuring areas with large velocity gradient changes may cause oversmoothing,resulting in significant measurement errors.To address this proble...Using traditional particle tracking velocimetry based on optical flow for measuring areas with large velocity gradient changes may cause oversmoothing,resulting in significant measurement errors.To address this problem,the traditional particle tracking velocimetry method based on an optical flow was improved.The level set segmentation algorithm was used to obtain the boundary contour of the region with large velocity gradient changes,and the non-uniform flow field was divided into regions according to the boundary contour to obtain sub-regions with uniform velocity distribution.The particle tracking velocimetry method based on optical flow was used to measure the granular flow velocity in each sub-region,thus avoiding the problem of granular flow distribution.The simulation results show that the measurement accuracy of this method is approximately 10%higher than that of traditional methods.The method was applied to a velocity measurement experiment on dense granular flow in silos,and the velocity distribution of the granular flow was obtained,verifying the practicality of the method in granular flow fields.展开更多
One of the evolving hand biometric features considered so far is finger knuckle printing,because of its ability towards unique identification of individuals.Despite many attempts have been made in this area of researc...One of the evolving hand biometric features considered so far is finger knuckle printing,because of its ability towards unique identification of individuals.Despite many attempts have been made in this area of research,the accuracy of the recognition model remains a major issue.To overcome this problem,a novel biometric-based method,named fingerknuckle-print(FKP),has been developed for individual verification.The proposed system carries key steps such as preprocessing,segmentation,feature extraction and classification.Initially input FKP image is fed into the preprocessing stage where colour images are converted to gray scale image for augmenting the system performance.Afterwards,segmentation process is carried out with the help of CROI(Circular Region of Interest)and Morphological operation.Then,feature extraction stage is carried out using Gabor-Derivative line approach for extracting intrinsic features.Finally,DCNN(Deep Convolutional Neural Network)is trained for the processed knuckle images to recognize imposter and genuine individuals.Extensive experiments on standard FKP database demonstrates that the proposed method attains considerable improvement compared with state-of-the-art methods.The overall accuracy attained for the proposed methodology is 95.6%which is achieved better than the existing techniques.展开更多
Water-soil leakage due to the longitudinal dislocation opening of tunnel segments in high-permeable soil strata is crucial for ensuring the longevity of underground tunnel infrastructures.This research delves into thi...Water-soil leakage due to the longitudinal dislocation opening of tunnel segments in high-permeable soil strata is crucial for ensuring the longevity of underground tunnel infrastructures.This research delves into this complex phenomenon employing coupled computational fluiddynamics(CFD),discrete element method(DEM),and finiteelement method(FEM),considering varied tunnel buried depths and dislocation opening sizes.Two critical areas susceptible to water-soil leakage have been identified,including an‘ellipsoid’shaped area at the tunnel top and a soil sliding area perpendicular to the tunneling direction.With a narrow segment opening(3 d_(50)),the fineloss remains below 2%across various buried depths,whereas it escalates to 7.4%-30%with increasing buried depth under a slightly wider opening(3.8d_(50)).The proposed three-dimensional(3D)ellipsoid model is used to delineate the leakage region and quantify over 98%ground soil loss due to dislocation opening.Furthermore,the research reveals that soil sliding induced by water-soil leakage significantly decreases the structural shear stress on the waists and inverts of the tunnel segment,while the soil arching at the top of the tunnel would mitigate the stress release,particularly at the lower dislocated tunnel segment.展开更多
Two dimensional(2 D) entropy method has to pay the price of time when applied to image segmentation. So the genetic algorithm is introduced to improve the computational efficiency of the 2 D entropy method. The pro...Two dimensional(2 D) entropy method has to pay the price of time when applied to image segmentation. So the genetic algorithm is introduced to improve the computational efficiency of the 2 D entropy method. The proposed method uses both the gray value of a pixel and the local average gray value of an image. At the same time, the simple genetic algorithm is improved by using better reproduction and crossover operators. Thus the proposed method makes up the 2 D entropy method’s drawback of being time consuming, and yields satisfactory segmentation results. Experimental results show that the proposed method can save computational time when it provides good quality segmentation.展开更多
基金National Natural Science Foundation of China(Nos.61773387 and 62022061).
文摘A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance method.Firstly,an altitude-energy profile is designed,and the bank angle is derived analytically as the initial iteration value for the predictor-corrector method.The predictor-corrector guidance method has been improved by deriving an analytical form for predicting the range-to-go error,which greatly accelerates the iterative speed.Then,a segmented guidance algorithm is proposed.The above analytically predictor-corrector guidance method is adopted when the energy exceeds an energy threshold.When the energy is less than the threshold,the equidistant test method is used to calculate the bank angle command,which ensures guidance accuracy as well as computational efficiency.Additionally,an adaptive guidance cycle strategy is applied to reduce the computational time of the reentry guidance trajectory.Finally,the accuracy and robustness of the proposed method are verified through a series of simulations and Monte-Carlo experiments.Compared with the traditional integral method,the proposed method requires 75%less computation time on average and achieves a lower landing error.
基金financially supported by the National Natural Science Foundation of China(No.52473228).
文摘The fatigue resistance of casting polyurethane(CPU)is crucial in various sectors,such as construction,healthcare,and the automotive industry.Despite its importance,no studies have reported on the fatigue threshold of CPU.This study employed an advanced Intrinsic Strength Analyzer(ISA)to evaluate the fatigue threshold of CPUs,systematically exploring the effects of three types of isocyanates(PPDI,NDI,TDI)that contribute to hard segment structures based on the cutting method.Employing multiple advanced characterization techniques(XRD,TEM,DSC,AFM),the results indicate that PPDI-based polyurethane exhibits the highest fatigue threshold(182.89 J/m^(2))due to a highest phase separation and a densely packed spherulitic structure,although the hydrogen bonding degree is the lowest(48.3%).Conversely,NDI-based polyurethane,despite having the high hydrogen bonding degree(53.6%),exhibits moderate fatigue performance(122.52 J/m^(2)),likely due to a more scattered microstructure.TDI-based polyurethane,with the highest hydrogen bonding degree(59.1%)but absence of spherulitic structure,shows the lowest fatigue threshold(46.43 J/m^(2)).Compared to common rubbers(NR,NBR,EPDM,BR),the superior fatigue performance of CPU is attributed to its well-organized microstructure,polyurethane possesses a higher fatigue threshold due to its high phase separation degree and orderly and dense spherulitic structure which enhances energy dissipation and reduces crack propagation.
基金Supported by the National Basic Research Program ofChina(2011CB707900)
文摘To segment the tumor region precisely is a prerequisite for ultrasound navigation and treatment. In this paper, a normalized cut method to segment tumor ultrasound image is proposed by means of simple linear iterative clustering for presegmentation procedure. The first step, we use simple linear iterative clustering algorithm to divide the image into a number of homogeneous over-segmented regions. Then, these regions are regarded as nodes, and a similarity matrix is constructed by comparing the histograms of each two regions. Finally, we apply the Ncut method to merging the over-segmented regions, then the image segmentation process is completed. The results show that the proposed segmentation scheme handles the strong speckle noise, low contrast, and weak edges well in ultrasound image. Our method has high segmentation precision and computation efficiency than the pixel-based Ncut method.
基金supported by the National Natural Science Foundation of China (Nos. 60672164, 60939003, 61079013, 60879001, 90000871)the Special Project about Humanities and Social Sciences in Ministry of Education of China (No. 16JDGC008)+2 种基金National Natural Science Funds and Civil Aviation Mutual Funds (Nos. U1533128 and U1233114)Study On Reusing Sketch User Interface Oriented Design Knowledge (No. 16KJA520003)Six Talent Peaks Project In Jiangsu Province (No. 2016-XYDXXJS-088)
文摘In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating the reliability of equipment with a single model may lead to severer errors.However, if lifetime is divided into several different intervals according to the characteristics of its failure rate, piecewise fitting can more accurately approximate the failure rate of equipment. Therefore, in this paper, failure rate is regarded as a piecewise function, and two kinds of segmented distribution are put forward to evaluate reliability. In order to estimate parameters in the segmented reliability function, Bayesian estimation and maximum likelihood estimation（MLE） of the segmented distribution are discussed in this paper. Since traditional information criterion is not suitable for the segmented distribution, an improved information criterion is proposed to test and evaluate the segmented reliability model in this paper. After a great deal of testing and verification,the segmented reliability model and its estimation methods presented in this paper are proven more efficient and accurate than the traditional non-segmented single model, especially when the change of the failure rate is time-phased or multimodal. The significant performance of the segmented reliability model in evaluating reliability of proximity sensors of leading-edge flap in civil aircraft indicates that the segmented distribution and its estimation method in this paper could be useful and accurate.
基金supported in part by the National Natural Science Foundation of China(11626214,11571309)the General Research Project of Zhejiang Provincial Department of Education(Y201635378)+3 种基金the Zhejiang Provincial Natural Science Foundation of China(LY17F020011)J.Peng is supported by the National Natural Science Foundation of China(11771160)the Research Promotion Program of Huaqiao University(ZQN-PY411)Natural Science Foundation of Fujian Province(2015J01254)
文摘This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind of image segmentation is still a challenging task.The proposed method uses both the region and boundary information to achieve accurate segmentation results.The region information can help to identify rough region of interest and prevent the boundary leakage problem.It makes use of normalized nonlocal comparisons between pairs of patches in each region,and a heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation.The boundary information can help to detect the precise location of the target object,it makes use of the geodesic active contour model to obtain the target boundary.The corresponding variational segmentation problem is implemented by a level set formulation.We use an internal energy term for geometric active contours to penalize the deviation of the level set function from a signed distance function.At last,experimental results on synthetic images and real images are shown in the paper with promising results.
基金supported by the National Natural Science Foundation of China[grant numbers 21466008]the Guangxi Natural Science Foundation,China[grant numbers 2019GXNSFAA185017]+1 种基金the Scientific Research Project of Guangxi Minzu University[grant numbers 2021MDKJ004]the Innovation Project of Guangxi Graduate Education[grant numbers YCSW2022255].
文摘From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Considering the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become crucial.Identification through the use of Computed Tomography(CT)images is an efficient and quick means.Therefore,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images.In this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of COVID-19.Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem.This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum.In addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population.Two experiments were carried out to test the performance of the proposed algorithm.First,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions.Secondly,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics.It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics.Furthermore,the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO.
基金supported by the National Key R&D Program of China(No.2022YFF0800601)National Scientific Foundation of China(Nos.41930103 and 41774047).
文摘In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back projection analysis.Data in two frequency bands(0.5-2 Hz and 1-3 Hz)are used in the imaging processes.The results show that the rupture of the first event extends about 200 km to the northeast and about 150 km to the southwest,lasting~90 s in total.The southwestern rupture is triggered by the northeastern rupture,demonstrating a sequential bidirectional unilateral rupture pattern.The rupture of the second event extends approximately 80 km in both northeast and west directions,lasting~35 s in total and demonstrates a typical bilateral rupture feature.The cascading ruptures on both sides also reflect the occurrence of selective rupture behaviors on bifurcated faults.In addition,we observe super-shear ruptures on certain fault sections with relatively straight fault structures and sparse aftershocks.
文摘A new segmentation method has been developed for PET fast imaging. The technique automatically segments the transmission images into different anatomical regions, it efficiently reduced the PET transmission scan time. The result shows that this method gives only 3 min-scan time which is perfect for attenuation correction of the PET images instead of the original 15-30 min-scan time. This approach has been successfully tested both on phantom and clinical data.
基金supported by the China Postdoctoral Science Foundation under Grant No.2015M571993the Shandong Provincial Natural Science Foundation,China under Grant No.ZR2017MD004+1 种基金the National Natural Science Foundation of China under Grant No.61602269Qingdao Postdoctoral Application Research Funded Project
文摘Effective and efficient SAR image segmentation has a significant role in coastal zone interpretation. In this paper, a coastal zone segmentation model is proposed based on Potts model. By introducing edge self-adaption parameter and modifying noisy data term, the proposed variational model provides a good solution for the coastal zone SAR image with common characteristics of inherent speckle noise and complicated geometrical details. However, the proposed model is difficult to solve due to to its nonlinear, non-convex and non-smooth characteristics. Followed by curve evolution theory and operator splitting method, the minimization problem is reformulated as a constrained minimization problem. A fast alternating minimization iterative scheme is designed to implement coastal zone segmentation. Finally, various two-stage and multiphase experimental results illustrate the advantage of the proposed segmentation model, and indicate the high computation efficiency of designed numerical approximation algorithm.
文摘Based on the multi-rigid body discretization model, namely, finite segment model,a chain multi-rigid-body-hinge-spring system model of a beam was presented, then a nonlinear parametrically exacted vibration equation of multi-degrees of freedom system was established using the coordination transformation method, and its resonance fields were derived by the restriction parameter method, that is, the dynamical buckling analysis of the beam. Because the deformation of a beam is not restricted by the discrete model and dynamic equation, the post buckling analysis can be done in above math model. The numerical solutions of a few examples were obtained by direct integrated method, which shows that the mechanical and math model gotten is correct.
基金863 Programgrant number:2007AA02Z211+3 种基金Jiangsu Science and Technology Departmentgrant number:BE2008399Education of Jiangsu Provincegrant number:08KJA530002
文摘In biology ferment engineering,accurate statistics of the quantity of bacteria is one of the most important subjects. In this paper,the quantity of bacteria which was observed traditionally manuauy can be detected automatically. Image acquisition and processing system is designed to accomplish image preprocessing,image segmentation and statistics of the quantity of bacteria. Segmentation of bacteria images is successfully realized by means of a region-based level set method and then the quantity of bacteria is computed precisely,which plays an important role in optimizing the growth conditions of bacteria.
文摘On the basis of the recent geological surveys and the relevant studies of the Xianshuihe fault zone, this paper analyzes the seismogenic mechanism of some faults and characteristic morphology on the fault zone by the boundary element method and discusses the fault segmentation and the related distribution of the earthquake ruptures. The main conclusions are: For the first order segmentation, the Xianshuihe fault zone can be divided into three major segments (the northwestern Luhuo-Qianning segment, the middle linking fracture region and the southeastern Kangding segment). Among them, the differences are not only in geometry and structure, but also in mechanical property and dynamics. Some of the characteristic morphologies on the Xianshuihe fault zone reflect the effects in cumulative deformation due to long-term fault movement, and reveal the fault segmentation in different orders. Such morphologies control, to some extent, the developments and the distributions of the earthquake ruptures on the fault zone.
基金funded in part by the Equipment Pre-Research Foundation of China,Grant No.61400010203in part by the Independent Project of the State Key Laboratory of Virtual Reality Technology and Systems.
文摘There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate enough.Both of them cannot generate entire segmentation information.In order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is proposed.This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances.It includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different objects.Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and quantitatively.The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method.This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.
文摘The principle and method of flexible multibody system dynamics is presented. The dynamic equation have been developed by means of Huston's method based on Kane's equation. In which the flexible members with general cross-section characters were divided into finite segment models under the assumption of small strain. In order to decrease the degrees of freedom of the system and increase the efficiency of numerical calculation. the mode transformation has been introduced. A typical example is presented. and the foregoing method has been perfectly verified.
基金supported by the National Natural Science(No.U19A2063)the Jilin Provincial Development Program of Science and Technology (No.20230201080GX)the Jilin Province Education Department Scientific Research Project (No.JJKH20230851KJ)。
文摘The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.
文摘Using traditional particle tracking velocimetry based on optical flow for measuring areas with large velocity gradient changes may cause oversmoothing,resulting in significant measurement errors.To address this problem,the traditional particle tracking velocimetry method based on an optical flow was improved.The level set segmentation algorithm was used to obtain the boundary contour of the region with large velocity gradient changes,and the non-uniform flow field was divided into regions according to the boundary contour to obtain sub-regions with uniform velocity distribution.The particle tracking velocimetry method based on optical flow was used to measure the granular flow velocity in each sub-region,thus avoiding the problem of granular flow distribution.The simulation results show that the measurement accuracy of this method is approximately 10%higher than that of traditional methods.The method was applied to a velocity measurement experiment on dense granular flow in silos,and the velocity distribution of the granular flow was obtained,verifying the practicality of the method in granular flow fields.
基金RUSA PHASE 2.0, University of Alagappa, Karaikudi has supported this research projectThe UGC-NFSC fellowship has helped support this research
文摘One of the evolving hand biometric features considered so far is finger knuckle printing,because of its ability towards unique identification of individuals.Despite many attempts have been made in this area of research,the accuracy of the recognition model remains a major issue.To overcome this problem,a novel biometric-based method,named fingerknuckle-print(FKP),has been developed for individual verification.The proposed system carries key steps such as preprocessing,segmentation,feature extraction and classification.Initially input FKP image is fed into the preprocessing stage where colour images are converted to gray scale image for augmenting the system performance.Afterwards,segmentation process is carried out with the help of CROI(Circular Region of Interest)and Morphological operation.Then,feature extraction stage is carried out using Gabor-Derivative line approach for extracting intrinsic features.Finally,DCNN(Deep Convolutional Neural Network)is trained for the processed knuckle images to recognize imposter and genuine individuals.Extensive experiments on standard FKP database demonstrates that the proposed method attains considerable improvement compared with state-of-the-art methods.The overall accuracy attained for the proposed methodology is 95.6%which is achieved better than the existing techniques.
基金supported by the National Natural Science Foundation of China(Grant Nos.52090084 and 52208354)the Shenzhen Science and Technology Program(Grant No.KQTD20221101093555006).
文摘Water-soil leakage due to the longitudinal dislocation opening of tunnel segments in high-permeable soil strata is crucial for ensuring the longevity of underground tunnel infrastructures.This research delves into this complex phenomenon employing coupled computational fluiddynamics(CFD),discrete element method(DEM),and finiteelement method(FEM),considering varied tunnel buried depths and dislocation opening sizes.Two critical areas susceptible to water-soil leakage have been identified,including an‘ellipsoid’shaped area at the tunnel top and a soil sliding area perpendicular to the tunneling direction.With a narrow segment opening(3 d_(50)),the fineloss remains below 2%across various buried depths,whereas it escalates to 7.4%-30%with increasing buried depth under a slightly wider opening(3.8d_(50)).The proposed three-dimensional(3D)ellipsoid model is used to delineate the leakage region and quantify over 98%ground soil loss due to dislocation opening.Furthermore,the research reveals that soil sliding induced by water-soil leakage significantly decreases the structural shear stress on the waists and inverts of the tunnel segment,while the soil arching at the top of the tunnel would mitigate the stress release,particularly at the lower dislocated tunnel segment.
文摘Two dimensional(2 D) entropy method has to pay the price of time when applied to image segmentation. So the genetic algorithm is introduced to improve the computational efficiency of the 2 D entropy method. The proposed method uses both the gray value of a pixel and the local average gray value of an image. At the same time, the simple genetic algorithm is improved by using better reproduction and crossover operators. Thus the proposed method makes up the 2 D entropy method’s drawback of being time consuming, and yields satisfactory segmentation results. Experimental results show that the proposed method can save computational time when it provides good quality segmentation.
