This work is concerned with the analysis of blood flow through inclined catheterized arteries having a balloon(angioplasty) with time-variant overlapping stenosis. The nature of blood in small arteries is analyzed mat...This work is concerned with the analysis of blood flow through inclined catheterized arteries having a balloon(angioplasty) with time-variant overlapping stenosis. The nature of blood in small arteries is analyzed mathematically by considering it as a Carreau nanofluid. The highly nonlinear momentum equations of nanofluid model are simplified by considering the mild stenosis case. The formulated problem is solved by a homotopy perturbation expansion in terms of a variant of the Weissenberg number to obtain explicit forms for the axial velocity, the stream function, the pressure gradient, the resistance impedance and the wall shear stress distribution. These solutions depend on the Brownian motion number, thermophoresis number, local temperature Grashof number G_r and local nanoparticle Grash of number B_r. The results were also studied for various values of the physical parameters, such as the Weissenberg number W_i, the power law index n, the taper angle φ, the maximum height of stenosis δ~*, the angle of inclination α, the maximum height of balloon σ~*, the axial displacement of the balloon z_d~*,the flow rate F and the Froud number Fr. The obtained results show that the transmission of axial velocity curves through a Newtonian fluid(Wi=0, n=1, Gr=0, Br=0, Nt=0, Nb≠0) is substantially lower than that through a Carreau nanofluid near the wall of balloon while the inverse occurs in the region between the balloon and stenosis. The streamlines have a clearly distinguished shifting toward the stenotic region and this shifting appears near the wall of the balloon, while it has almost disappeared near the stenotic wall and the trapping bolus in the case of horizontal arteries and Newtonian fluid(Wi=0, n=1, Gr=0, Br=0, Nt=0, Nb≠0) does not appear but for the case of Carreau nanofluid bolus appears.展开更多
Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clu...Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering algorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spatial clustering of applications with noise(DBSCAN)algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN(AFD)which works with the initialization of two parameters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically.The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model overcomes the problems of clustering such as morphology,overlapping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.展开更多
For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve th...For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping,one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps： fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach.展开更多
Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the comm...Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.展开更多
A novel weighted evolving network model based on the clique overlapping growth was proposed.The model shows different network characteristics under two different selection mechanisms that are preferential selection an...A novel weighted evolving network model based on the clique overlapping growth was proposed.The model shows different network characteristics under two different selection mechanisms that are preferential selection and random selection.On the basis of mean-field theory,this model under the two different selection mechanisms was analyzed.The analytic equations of distributions of the number of cliques that a vertex joins and the vertex strength of the model were given.It is proved that both distributions follow the scale-free power-law distribution in preferential selection mechanism and the exponential distribution in random selection mechanism,respectively.The analytic expressions of exponents of corresponding distributions were obtained.The agreement between the simulations and analytical results indicates the validity of the theoretical analysis.Finally,three real transport bus networks(BTNs) of Beijing,Shanghai and Hangzhou in China were studied.By analyzing their network properties,it is discovered that these real BTNs belong to a kind of weighted evolving network model with clique overlapping growth and random selection mechanism that was proposed in this context.展开更多
The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition...The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.展开更多
In order to accurately measure an object’s three-dimensional surface shape,the influence of sampling on it was studied.First,on the basis of deriving spectra expressions through the Fourier transform,the generation o...In order to accurately measure an object’s three-dimensional surface shape,the influence of sampling on it was studied.First,on the basis of deriving spectra expressions through the Fourier transform,the generation of CCD pixels was analyzed,and its expression was given.Then,based on the discrete expression of deformation fringes obtained after sampling,its Fourier spectrum expression was derived,resulting in an infinitely repeated"spectra island"in the frequency domain.Finally,on the basis of using a low-pass filter to remove high-order harmonic components and retaining only one fundamental frequency component,the inverse Fourier transform was used to reconstruct the signal strength.A method of reducing the sampling interval,i.e.,reducing the number of sampling points per fringe,was proposed to increase the ratio between the sampling frequency and the fundamental frequency of the grating.This was done to reconstruct the object’s surface shape more accurately under the condition of m>4.The basic principle was verified through simulation and experiment.In the simulation,the sampling intervals were 8 pixels,4 pixels,2 pixels,and 1 pixel,the maximum absolute error values obtained in the last three situations were 88.80%,38.38%,and 31.50%in the first situation,respectively,and the corresponding average absolute error values are 71.84%,43.27%,and 32.26%.It is demonstrated that the smaller the sampling interval,the better the recovery effect.Taking the same four sampling intervals in the experiment as in the simulation can also lead to the same conclusions.The simulated and experimental results show that reducing the sampling interval can improve the accuracy of object surface shape measurement and achieve better reconstruction results.展开更多
When the attributes of unknown targets are not just numerical attributes,but hybrid attributes containing linguistic attributes,the existing recognition methods are not effective.In addition,it is more difficult to id...When the attributes of unknown targets are not just numerical attributes,but hybrid attributes containing linguistic attributes,the existing recognition methods are not effective.In addition,it is more difficult to identify the unknown targets densely distributed in the feature space,especially when there is interval overlap between attribute measurements of different target classes.To address these problems,a novel method based on intuitionistic fuzzy comprehensive evaluation model(IFCEM)is proposed.For numerical attributes,targets in the database are divided into individual classes and overlapping classes,and for linguistic attributes,continuous interval-valued linguistic term set(CIVLTS)is used to describe target characteristic.A cloud modelbased method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively.An improved inverse weighted kernel fuzzy c-means(IWK-FCM)algorithm is proposed for solution of attribute weight vector.The possibility matrix is applied to determine the identity and category of query target.Finally,a case study composed of parameter sensitivity analysis,recognition accuracy analysis.and comparison with other methods,is taken to verify the superiority of the proposed method.展开更多
通过重叠延伸PCR(gene splicing by overlap extension,SOE-PCR)扩增黄曲霉寡聚-1,6-葡萄糖苷酶基因和酿酒酵母α-信号肽序列,定向重组到整合型表达载体pδRCMB中,并在CICC1346中实现分泌表达;然后通过同源建模、利用分子模拟软件Discov...通过重叠延伸PCR(gene splicing by overlap extension,SOE-PCR)扩增黄曲霉寡聚-1,6-葡萄糖苷酶基因和酿酒酵母α-信号肽序列,定向重组到整合型表达载体pδRCMB中,并在CICC1346中实现分泌表达;然后通过同源建模、利用分子模拟软件Discovery Studio 4.1分析其蛋白结构,以对硝基苯-α-D-葡萄糖吡喃苷(pNPG)为底物,建立并确定酶活反应条件,并进行纯化、酶学性质分析;将密码子优化后序列在CICC1346中实现分泌表达,利用淀粉进行共发酵实验。结果显示:重组酶突变后相对野生型蛋白结构无影响。SDS-PAGE分析重组重组酶大小约为70 kDa;优化后最高酶活达到0.69 U/m L,重组酶最适pH为6.5,在pH4.5.0~7.0维持90%以上的酶活;最适温度为40℃,在30~40℃维持接近100%的酶活;受Cu^(2+)和Mn^(2+)严格抑制;寡聚-1,6-葡萄糖苷酶与α-淀粉酶及糖化酶协同利用淀粉产乙醇,提高淀粉水解效率。这是首次报道黄曲霉的寡聚-1,6-葡萄糖苷酶基因在酿酒酵母整合型分泌表达。展开更多
目的比较反穿刺法与Overlap法在全腹腔镜全胃切除术(totally laparoscopic total gastrectomy,TLTG)食管空肠吻合中的应用效果。方法回顾性分析我院2017年1月~2018年12月75例TLTG的临床资料,依据食管空肠重建方式不同,分为反穿刺组(n=41...目的比较反穿刺法与Overlap法在全腹腔镜全胃切除术(totally laparoscopic total gastrectomy,TLTG)食管空肠吻合中的应用效果。方法回顾性分析我院2017年1月~2018年12月75例TLTG的临床资料,依据食管空肠重建方式不同,分为反穿刺组(n=41)和Overlap组(n=34)。结果75例均顺利完成全TLTG。与Overlap组比较,反穿刺组吻合时间[(46.4±6.3)min vs(52.9±4.6)min,t=-5.028,P=0.000]和术后住院时间[(11.0±1.3)d vs(11.6±1.2)d,t=-2.363,P=0.021]明显缩短,住院费用明显减少[(44720.7±3499.2)元vs.(48164.6±5536.5)元,t=-3.274,P=0.002],但肠道恢复时间显著延长[(3.8±1.1)d vs.(3.2±0.6)d,t=2.675,P=0.009]。2组总手术时间、术中出血量、清扫淋巴结数目差异无统计学意义(P>0.05)。结论全腹腔镜下食管空肠吻合术管型吻合的反穿刺法和线型吻合的Overlap法均安全可行,但反穿刺法在吻合时间和手术费用上更有优势。展开更多
文摘This work is concerned with the analysis of blood flow through inclined catheterized arteries having a balloon(angioplasty) with time-variant overlapping stenosis. The nature of blood in small arteries is analyzed mathematically by considering it as a Carreau nanofluid. The highly nonlinear momentum equations of nanofluid model are simplified by considering the mild stenosis case. The formulated problem is solved by a homotopy perturbation expansion in terms of a variant of the Weissenberg number to obtain explicit forms for the axial velocity, the stream function, the pressure gradient, the resistance impedance and the wall shear stress distribution. These solutions depend on the Brownian motion number, thermophoresis number, local temperature Grashof number G_r and local nanoparticle Grash of number B_r. The results were also studied for various values of the physical parameters, such as the Weissenberg number W_i, the power law index n, the taper angle φ, the maximum height of stenosis δ~*, the angle of inclination α, the maximum height of balloon σ~*, the axial displacement of the balloon z_d~*,the flow rate F and the Froud number Fr. The obtained results show that the transmission of axial velocity curves through a Newtonian fluid(Wi=0, n=1, Gr=0, Br=0, Nt=0, Nb≠0) is substantially lower than that through a Carreau nanofluid near the wall of balloon while the inverse occurs in the region between the balloon and stenosis. The streamlines have a clearly distinguished shifting toward the stenotic region and this shifting appears near the wall of the balloon, while it has almost disappeared near the stenotic wall and the trapping bolus in the case of horizontal arteries and Newtonian fluid(Wi=0, n=1, Gr=0, Br=0, Nt=0, Nb≠0) does not appear but for the case of Carreau nanofluid bolus appears.
文摘Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering algorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spatial clustering of applications with noise(DBSCAN)algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN(AFD)which works with the initialization of two parameters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically.The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model overcomes the problems of clustering such as morphology,overlapping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.
基金supported by the National Natural Science Foundation of China(61304218)
文摘For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping,one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps： fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach.
基金supported by the National Natural Science Foundation of China(71271018)
文摘Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.
基金Projects(60874080,60504027) supported by the National Natural Science Foundation of ChinaProject(20060401037) supported by the National Postdoctor Science Foundation of China
文摘A novel weighted evolving network model based on the clique overlapping growth was proposed.The model shows different network characteristics under two different selection mechanisms that are preferential selection and random selection.On the basis of mean-field theory,this model under the two different selection mechanisms was analyzed.The analytic equations of distributions of the number of cliques that a vertex joins and the vertex strength of the model were given.It is proved that both distributions follow the scale-free power-law distribution in preferential selection mechanism and the exponential distribution in random selection mechanism,respectively.The analytic expressions of exponents of corresponding distributions were obtained.The agreement between the simulations and analytical results indicates the validity of the theoretical analysis.Finally,three real transport bus networks(BTNs) of Beijing,Shanghai and Hangzhou in China were studied.By analyzing their network properties,it is discovered that these real BTNs belong to a kind of weighted evolving network model with clique overlapping growth and random selection mechanism that was proposed in this context.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(62325104).
文摘The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.
文摘In order to accurately measure an object’s three-dimensional surface shape,the influence of sampling on it was studied.First,on the basis of deriving spectra expressions through the Fourier transform,the generation of CCD pixels was analyzed,and its expression was given.Then,based on the discrete expression of deformation fringes obtained after sampling,its Fourier spectrum expression was derived,resulting in an infinitely repeated"spectra island"in the frequency domain.Finally,on the basis of using a low-pass filter to remove high-order harmonic components and retaining only one fundamental frequency component,the inverse Fourier transform was used to reconstruct the signal strength.A method of reducing the sampling interval,i.e.,reducing the number of sampling points per fringe,was proposed to increase the ratio between the sampling frequency and the fundamental frequency of the grating.This was done to reconstruct the object’s surface shape more accurately under the condition of m>4.The basic principle was verified through simulation and experiment.In the simulation,the sampling intervals were 8 pixels,4 pixels,2 pixels,and 1 pixel,the maximum absolute error values obtained in the last three situations were 88.80%,38.38%,and 31.50%in the first situation,respectively,and the corresponding average absolute error values are 71.84%,43.27%,and 32.26%.It is demonstrated that the smaller the sampling interval,the better the recovery effect.Taking the same four sampling intervals in the experiment as in the simulation can also lead to the same conclusions.The simulated and experimental results show that reducing the sampling interval can improve the accuracy of object surface shape measurement and achieve better reconstruction results.
基金supported by the Youth Foundation of the National Science Foundation of China(62001503)the Excellent Youth Scholar of the National Defense Science and Technology Foundation of China(2017-JCJQ-ZQ-003)the Special Fund for Taishan Scholar Project(ts201712072).
文摘When the attributes of unknown targets are not just numerical attributes,but hybrid attributes containing linguistic attributes,the existing recognition methods are not effective.In addition,it is more difficult to identify the unknown targets densely distributed in the feature space,especially when there is interval overlap between attribute measurements of different target classes.To address these problems,a novel method based on intuitionistic fuzzy comprehensive evaluation model(IFCEM)is proposed.For numerical attributes,targets in the database are divided into individual classes and overlapping classes,and for linguistic attributes,continuous interval-valued linguistic term set(CIVLTS)is used to describe target characteristic.A cloud modelbased method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively.An improved inverse weighted kernel fuzzy c-means(IWK-FCM)algorithm is proposed for solution of attribute weight vector.The possibility matrix is applied to determine the identity and category of query target.Finally,a case study composed of parameter sensitivity analysis,recognition accuracy analysis.and comparison with other methods,is taken to verify the superiority of the proposed method.
文摘通过重叠延伸PCR(gene splicing by overlap extension,SOE-PCR)扩增黄曲霉寡聚-1,6-葡萄糖苷酶基因和酿酒酵母α-信号肽序列,定向重组到整合型表达载体pδRCMB中,并在CICC1346中实现分泌表达;然后通过同源建模、利用分子模拟软件Discovery Studio 4.1分析其蛋白结构,以对硝基苯-α-D-葡萄糖吡喃苷(pNPG)为底物,建立并确定酶活反应条件,并进行纯化、酶学性质分析;将密码子优化后序列在CICC1346中实现分泌表达,利用淀粉进行共发酵实验。结果显示:重组酶突变后相对野生型蛋白结构无影响。SDS-PAGE分析重组重组酶大小约为70 kDa;优化后最高酶活达到0.69 U/m L,重组酶最适pH为6.5,在pH4.5.0~7.0维持90%以上的酶活;最适温度为40℃,在30~40℃维持接近100%的酶活;受Cu^(2+)和Mn^(2+)严格抑制;寡聚-1,6-葡萄糖苷酶与α-淀粉酶及糖化酶协同利用淀粉产乙醇,提高淀粉水解效率。这是首次报道黄曲霉的寡聚-1,6-葡萄糖苷酶基因在酿酒酵母整合型分泌表达。
