Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults.Therefore,an improved spectral amplit...Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults.Therefore,an improved spectral amplitude modulation(ISAM)based on sparse feature adaptive convolution(SFAC)is proposed to enhance the fault features under variable speed conditions.First,an optimal bi-damped wavelet construction method is proposed to learn signal impulse features,which selects the optimal bi-damped wavelet parameters with correlation criterion and particle swarm optimization.Second,a convolutional basis pursuit denoising model based on an optimal bi-damped wavelet is proposed for resolving sparse impulses.A model regularization parameter selection method based on weighted fault characteristic amplitude ratio assistance is proposed.Then,an ISAM method based on kurtosis threshold is proposed to further enhance the fault information of sparse signal.Finally,the type of variable speed faults is determined by order spectrum analysis.Various experimental results,such as spectral amplitude modulation and Morlet wavelet matching,verify the effectiveness and advantages of the ISAM-SFAC method.展开更多
To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,...To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,the evaluation member uses the determined linguistic variable to give the correlation strength evaluation matrix of customer requirements and engineering features.Secondly,the relative importance of the evaluation member and customer requirements are aggregated.Finally,the priority of engineering features is obtained by calculating the deviation.The feasibility and practicability of this method are proven by taking the design of a new product of a long bag low-pressure pulse dust collector as an example.展开更多
In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption...In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution （PWVD） and an extraction of symptom parameter （SP）. An extraction method for instantaneous feature spectrum is presented using the relative crossing information （RCI） and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping （LSM） technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.展开更多
Key variable identification for classifications is related to many trouble-shooting problems in process indus-tries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently i...Key variable identification for classifications is related to many trouble-shooting problems in process indus-tries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently in applica-tion for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diag-nosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee East-man process (TEP) simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE (A-SVM-RFE). A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis (PCA) and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.展开更多
This paper proposes a night-time vehicle detection method using variable Haar-like feature.The specific features of front vehicle cannot be obtained in road image at night-time because of light reflection and ambient ...This paper proposes a night-time vehicle detection method using variable Haar-like feature.The specific features of front vehicle cannot be obtained in road image at night-time because of light reflection and ambient light,and it is also difficult to define optimal brightness and color of rear lamp according to road conditions.In comparison,the difference of vehicle region and road surface is more robust for road illumination environment.Thus,we select the candidates of vehicles by analysing the difference,and verify the candidates using those brightness and complexity to detect vehicle correctly.The feature of brightness difference is detected using variable horizontal Haar-like mask according to vehicle size in the location of image.And the region occurring rapid change is selected as the candidate.The proposed method is evaluated by testing on the various real road conditions.展开更多
The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion,which leads to a false matching,with an...The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion,which leads to a false matching,with an inaccurate pose estimation or failed tracking.To address the challenge above,a new method of feature point matching is proposed in this paper,which combines the variable velocity model with the reverse optical flow method.First,the constant velocity model is extended to a new variable velocity model,and the expanded variable velocity model is used to provide the initial pixel shifting for the reverse optical flow method.Then the search range of feature points is accurately determined according to the results of the reverse optical flow method,thereby improving the accuracy and reliability of feature matching,with strengthened interframe tracking effects.Finally,we tested on TUM data set based on the RGB-D camera.Experimental results show that this method can reduce the probability of tracking failure and improve localization accuracy on SLAM(Simultaneous Localization and Mapping)systems.Compared with the traditional ORB-SLAM2,the test error of this method on each sequence in the TUM data set is significantly reduced,and the root mean square error is only 63.8%of the original system under the optimal condition.展开更多
Anatomical characteristics have been proven useful for extracting climatic signals. To examine the climatic signals recorded by tree-ring cell features in the Changbai Mountains, we measured cell number and cell lumen...Anatomical characteristics have been proven useful for extracting climatic signals. To examine the climatic signals recorded by tree-ring cell features in the Changbai Mountains, we measured cell number and cell lumen diameter, in addition to ring widths, of Korean pine （Pinus koraiensis） tree rings at sites of varied elevation, and we developed chronologies of cell number （CN）, mean lumen diameter （MLD）, maximum lumen diameter （MAXLD） and tree-ring width （TRW）. The chronologies were correlated with climatic factors monthly mean tem- perature and the sum of precipitation. As shown by our analysis, the cell parameter chronologies were suitable for dendroclimatology studies. CN and TRW shared relatively similar climatic signals which differed from MLD and MAXLD, and growth-climate relationships were elevation- dependent, as shown by the following findings： （1） at each elevation, MLD and MAXLD recorded different monthly climatic signals from those recorded by TRW for the same climatic factors; and （2） MLD and MAXLD recorded cli- matic factors that were absent middle elevations. Cell lumen effective archive for improving for this study area. from TRW at lower and diameter proved to be an the climate reconstruction展开更多
With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectiv...With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.展开更多
To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage ...To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage and mesoscopic information for coal under compression was then analysed. The shape and distribution of damage were comprehensively considered in a defined damage variable, which was based on the texture characteristic. An elastic-brittle damage model based on the mesostructure information of coal was established. As a result, the damage model can appropriately and reliably replicate the processes of initiation, expansion, cut-through and eventual destruction of microscopic damage to coal under compression. After comparison, it was proved that the predicted overall stress-strain response of the model was comparable to the experimental result.展开更多
Feature Model (FM) became an important role in Software Product Line Engineering (SPLE) field. Many approaches have been introduced since the original FM came up with Feature Oriented Domain Analysis (FODA) introduced...Feature Model (FM) became an important role in Software Product Line Engineering (SPLE) field. Many approaches have been introduced since the original FM came up with Feature Oriented Domain Analysis (FODA) introduced by Kang in 1990. The main purpose of FM is used for commonality and variability analysis in domain engineering, to optimize the reusable aspect of software features or components. Cardinality-based Feature Model (CBFM) is one extension of original FM, which integrates several notations of other extensions. In CBFM, feature model defined as hierarchy of feature, with each of feature has a cardinality. The other notation to express variability within SPLE is Orthogonal Variability Model (OVM). At the other hand, OMG as standard organization makes an effort to build standard generic language to express the commonality and variability in SPL field, by initiate Common Variability Language (CVL). This paper reports the comparison and mapping of FODA, CBFM and OVM to CVL where need to be explored first to define meta model mapping of these several approaches. Furthermore, the comparison and mapping of those approaches are discussed in term of R3ST (read as “REST”) software feature model as the case study.展开更多
Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability information.Self-adaptive system...Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability information.Self-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts.However,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources.The process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and error-prone.Nevertheless,systems’complexity can be reduced while the development process of systems by reusing elements or components.FMs are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system components.While current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been considered.In this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse process.Evaluation results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.展开更多
AIM： To characterize the clinical, serologic and virologic features of hepatitis B virus （HBV） infection in Iranian patients with different stages of liver disease.METHODS： Sixty two patients comprising of 12 inac...AIM： To characterize the clinical, serologic and virologic features of hepatitis B virus （HBV） infection in Iranian patients with different stages of liver disease.METHODS： Sixty two patients comprising of 12 inactive carriers, 30 chronic hepatitis patients, 13 patients with liver cirrhosis and 7 patients with hepatocellular carcinoma （HCC） were enrolled in the study. The HBV S, C and basal core promoter （BCP） regions were amplified and sequenced, and the clinical, serologic, phylogenetic and virologic characteristics were investigated.RESULTS： The study group consisted of 16 HBeAgpositive and 46 HBeAg-negative patients. Anti-HBepositive patients were older and had higher levels of ALT, ASL and bilirubin compared to HBeAg-positive patients. Phylogenetic analysis revealed that all patients were infected with genotype D （mostly ayw2）. The G1896A precore （PC） mutant was detected in 58.1% patients. HBeAg-negative patients showed a higher rate of PC mutant compared to HBeAg-positive patients （2,2 = 9.682, P = 0.003）. The majority of patients with HCC were HBeAg-negative and were infected with PC mutant variants. There was no significant difference in the occurrence of BCP mutation between the two groups, while the rate of BCP plus PC mutants was higher in HBeAg-negative patients （2,2 = 4.308, P = 0.04）. In the HBV S region, the genetic variability was low, and the marked substitution was P120T/S, with a rate of 9.7% （n = 6）.CONCLUSION： In conclusion, HBV/D is the predominant genotype in Iran, and the nucleotide variability in the BCP and PC regions may play a role in HBV disease outcome in HBeAg-negative patients.展开更多
Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common an...Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints(CTC).CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things(IoT)devices because different Internet devices and protocols are communicated.Therefore,managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex,time-consuming,and hard.However,the CTC problem needs to be considered in previously proposed approaches such as Commonality VariabilityModeling of Features(COVAMOF)andGenarch+tool;therefore,invalid products are generated.This research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints(BOFS-CTC),to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the featuremodel of SPL.BOFS-CTC removes the invalid products at the early stage of feature selection for the product configuration.Furthermore,this research developed the BOFS-CTC algorithm and applied it to,IoT-based feature models.The findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product configurations.The accuracy of BOFS-CTC is measured by the integration sampling technique,where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100%correct.Using BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.展开更多
This study explores the bioconvective behavior of a Reiner-Rivlin nanofluid,accounting for spatially varying thermal properties.The flow is considered over a porous,stretching surface with mass suction effects incorpo...This study explores the bioconvective behavior of a Reiner-Rivlin nanofluid,accounting for spatially varying thermal properties.The flow is considered over a porous,stretching surface with mass suction effects incorporated into the transport analysis.The Reiner-Rivlin nanofluid model includes variable thermal conductivity,mass diffusivity,and motile microorganism density to accurately reflect realistic biological conditions.Radiative heat transfer and internal heat generation are considered in the thermal energy equation,while the Cattaneo-Christov theory is employed to model non-Fourier heat and mass fluxes.The governing equations are non-dimensionalized to reduce complexity,and a numerical solution is obtained using a shooting method.Parametric studies are conducted to examine the influence of key dimensionless parameters on velocity,temperature,concentration,and motile microorganism profiles.The results are presented through a series of graphs,offering insight into the dynamic interplay between physical mechanisms affecting heat and mass transfer in non-Newtonian bioconvective nanofluid systems.展开更多
Changeful and complex rural family structure and climatic features of transitional areas in China make the application of variable strategy in energy-saving rural residence designs possible.Aiming at the low cost,seve...Changeful and complex rural family structure and climatic features of transitional areas in China make the application of variable strategy in energy-saving rural residence designs possible.Aiming at the low cost,several effective and reasonable variable strategies were proposed for the design of interior spaces,main bedroom,sunshine room,staircase,west wall,door and window design to satisfy changing structure of a family during different periods and their different thermo-technical requirements in winter and summer.In this way,thermal comfort of rural indoor spaces will be improved,more energy saved,useful experience and thoughts provided for the energy-saving residence design in cold regions and regions hot in summer and cold in winter.展开更多
基金funded by the National Natural Science Foundation of China(grant nos.52475084 and 52375076)the Postdoctoral Fellowship Program of CPSF(grant no.GZC20230202).
文摘Difficulty in extracting nonlinear sparse impulse features due to variable speed conditions and redundant noise interference leads to challenges in diagnosing variable speed faults.Therefore,an improved spectral amplitude modulation(ISAM)based on sparse feature adaptive convolution(SFAC)is proposed to enhance the fault features under variable speed conditions.First,an optimal bi-damped wavelet construction method is proposed to learn signal impulse features,which selects the optimal bi-damped wavelet parameters with correlation criterion and particle swarm optimization.Second,a convolutional basis pursuit denoising model based on an optimal bi-damped wavelet is proposed for resolving sparse impulses.A model regularization parameter selection method based on weighted fault characteristic amplitude ratio assistance is proposed.Then,an ISAM method based on kurtosis threshold is proposed to further enhance the fault information of sparse signal.Finally,the type of variable speed faults is determined by order spectrum analysis.Various experimental results,such as spectral amplitude modulation and Morlet wavelet matching,verify the effectiveness and advantages of the ISAM-SFAC method.
文摘To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,the evaluation member uses the determined linguistic variable to give the correlation strength evaluation matrix of customer requirements and engineering features.Secondly,the relative importance of the evaluation member and customer requirements are aggregated.Finally,the priority of engineering features is obtained by calculating the deviation.The feasibility and practicability of this method are proven by taking the design of a new product of a long bag low-pressure pulse dust collector as an example.
基金supported by National Natural Science Foundation of China (Grant No. 50875016, 51075023)Fundamental Research Funds for the Central Universities of China (Grant No. JD0903, JD0904)
文摘In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution （PWVD） and an extraction of symptom parameter （SP）. An extraction method for instantaneous feature spectrum is presented using the relative crossing information （RCI） and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping （LSM） technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.
基金Supported by China 973 Program (No.2002CB312200), the National Natural Science Foundation of China (No.60574019 and No.60474045), the Key Technologies R&D Program of Zhejiang Province (No.2005C21087) and the Academician Foundation of Zhejiang Province (No.2005A1001-13).
文摘Key variable identification for classifications is related to many trouble-shooting problems in process indus-tries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently in applica-tion for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diag-nosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee East-man process (TEP) simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE (A-SVM-RFE). A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis (PCA) and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.
基金supported by the MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Infor mation Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency)(NIPA-2011-C1090-1121-0010)by the Brain Korea 21 Project in2011
文摘This paper proposes a night-time vehicle detection method using variable Haar-like feature.The specific features of front vehicle cannot be obtained in road image at night-time because of light reflection and ambient light,and it is also difficult to define optimal brightness and color of rear lamp according to road conditions.In comparison,the difference of vehicle region and road surface is more robust for road illumination environment.Thus,we select the candidates of vehicles by analysing the difference,and verify the candidates using those brightness and complexity to detect vehicle correctly.The feature of brightness difference is detected using variable horizontal Haar-like mask according to vehicle size in the location of image.And the region occurring rapid change is selected as the candidate.The proposed method is evaluated by testing on the various real road conditions.
基金This work was supported by The National Natural Science Foundation of China under Grant No.61304205 and NO.61502240The Natural Science Foundation of Jiangsu Province under Grant No.BK20191401 and No.BK20201136Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.SJCX21_0364 and No.SJCX21_0363.
文摘The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion,which leads to a false matching,with an inaccurate pose estimation or failed tracking.To address the challenge above,a new method of feature point matching is proposed in this paper,which combines the variable velocity model with the reverse optical flow method.First,the constant velocity model is extended to a new variable velocity model,and the expanded variable velocity model is used to provide the initial pixel shifting for the reverse optical flow method.Then the search range of feature points is accurately determined according to the results of the reverse optical flow method,thereby improving the accuracy and reliability of feature matching,with strengthened interframe tracking effects.Finally,we tested on TUM data set based on the RGB-D camera.Experimental results show that this method can reduce the probability of tracking failure and improve localization accuracy on SLAM(Simultaneous Localization and Mapping)systems.Compared with the traditional ORB-SLAM2,the test error of this method on each sequence in the TUM data set is significantly reduced,and the root mean square error is only 63.8%of the original system under the optimal condition.
基金supported by the National Science-Technology Support Plan Projects(2012BAC19B02)the Beijing Natural Science Foundation Projects(8154046)
文摘Anatomical characteristics have been proven useful for extracting climatic signals. To examine the climatic signals recorded by tree-ring cell features in the Changbai Mountains, we measured cell number and cell lumen diameter, in addition to ring widths, of Korean pine （Pinus koraiensis） tree rings at sites of varied elevation, and we developed chronologies of cell number （CN）, mean lumen diameter （MLD）, maximum lumen diameter （MAXLD） and tree-ring width （TRW）. The chronologies were correlated with climatic factors monthly mean tem- perature and the sum of precipitation. As shown by our analysis, the cell parameter chronologies were suitable for dendroclimatology studies. CN and TRW shared relatively similar climatic signals which differed from MLD and MAXLD, and growth-climate relationships were elevation- dependent, as shown by the following findings： （1） at each elevation, MLD and MAXLD recorded different monthly climatic signals from those recorded by TRW for the same climatic factors; and （2） MLD and MAXLD recorded cli- matic factors that were absent middle elevations. Cell lumen effective archive for improving for this study area. from TRW at lower and diameter proved to be an the climate reconstruction
基金supported by the National Natural Science Foundation of China(62033008,61873143)。
文摘With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.
基金funding by the National Natural Science Foundation of China(Nos.51474039 and 51404046)the Project of Shanxi Provincial Federation of Coalbed Methane Research(No.2013012010)the Science Foundation of North University of China(No.XJJ2016033)
文摘To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage and mesoscopic information for coal under compression was then analysed. The shape and distribution of damage were comprehensively considered in a defined damage variable, which was based on the texture characteristic. An elastic-brittle damage model based on the mesostructure information of coal was established. As a result, the damage model can appropriately and reliably replicate the processes of initiation, expansion, cut-through and eventual destruction of microscopic damage to coal under compression. After comparison, it was proved that the predicted overall stress-strain response of the model was comparable to the experimental result.
文摘Feature Model (FM) became an important role in Software Product Line Engineering (SPLE) field. Many approaches have been introduced since the original FM came up with Feature Oriented Domain Analysis (FODA) introduced by Kang in 1990. The main purpose of FM is used for commonality and variability analysis in domain engineering, to optimize the reusable aspect of software features or components. Cardinality-based Feature Model (CBFM) is one extension of original FM, which integrates several notations of other extensions. In CBFM, feature model defined as hierarchy of feature, with each of feature has a cardinality. The other notation to express variability within SPLE is Orthogonal Variability Model (OVM). At the other hand, OMG as standard organization makes an effort to build standard generic language to express the commonality and variability in SPL field, by initiate Common Variability Language (CVL). This paper reports the comparison and mapping of FODA, CBFM and OVM to CVL where need to be explored first to define meta model mapping of these several approaches. Furthermore, the comparison and mapping of those approaches are discussed in term of R3ST (read as “REST”) software feature model as the case study.
文摘Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability information.Self-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts.However,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources.The process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and error-prone.Nevertheless,systems’complexity can be reduced while the development process of systems by reusing elements or components.FMs are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system components.While current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been considered.In this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse process.Evaluation results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.
基金A grant from the Nanotechnology committee of the Ministry of Science, Research and Technology, Iran, No. 31.1895 on 05.03.2004 to Majid Sadeghizadeh
文摘AIM： To characterize the clinical, serologic and virologic features of hepatitis B virus （HBV） infection in Iranian patients with different stages of liver disease.METHODS： Sixty two patients comprising of 12 inactive carriers, 30 chronic hepatitis patients, 13 patients with liver cirrhosis and 7 patients with hepatocellular carcinoma （HCC） were enrolled in the study. The HBV S, C and basal core promoter （BCP） regions were amplified and sequenced, and the clinical, serologic, phylogenetic and virologic characteristics were investigated.RESULTS： The study group consisted of 16 HBeAgpositive and 46 HBeAg-negative patients. Anti-HBepositive patients were older and had higher levels of ALT, ASL and bilirubin compared to HBeAg-positive patients. Phylogenetic analysis revealed that all patients were infected with genotype D （mostly ayw2）. The G1896A precore （PC） mutant was detected in 58.1% patients. HBeAg-negative patients showed a higher rate of PC mutant compared to HBeAg-positive patients （2,2 = 9.682, P = 0.003）. The majority of patients with HCC were HBeAg-negative and were infected with PC mutant variants. There was no significant difference in the occurrence of BCP mutation between the two groups, while the rate of BCP plus PC mutants was higher in HBeAg-negative patients （2,2 = 4.308, P = 0.04）. In the HBV S region, the genetic variability was low, and the marked substitution was P120T/S, with a rate of 9.7% （n = 6）.CONCLUSION： In conclusion, HBV/D is the predominant genotype in Iran, and the nucleotide variability in the BCP and PC regions may play a role in HBV disease outcome in HBeAg-negative patients.
文摘Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints(CTC).CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things(IoT)devices because different Internet devices and protocols are communicated.Therefore,managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex,time-consuming,and hard.However,the CTC problem needs to be considered in previously proposed approaches such as Commonality VariabilityModeling of Features(COVAMOF)andGenarch+tool;therefore,invalid products are generated.This research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints(BOFS-CTC),to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the featuremodel of SPL.BOFS-CTC removes the invalid products at the early stage of feature selection for the product configuration.Furthermore,this research developed the BOFS-CTC algorithm and applied it to,IoT-based feature models.The findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product configurations.The accuracy of BOFS-CTC is measured by the integration sampling technique,where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100%correct.Using BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.
文摘This study explores the bioconvective behavior of a Reiner-Rivlin nanofluid,accounting for spatially varying thermal properties.The flow is considered over a porous,stretching surface with mass suction effects incorporated into the transport analysis.The Reiner-Rivlin nanofluid model includes variable thermal conductivity,mass diffusivity,and motile microorganism density to accurately reflect realistic biological conditions.Radiative heat transfer and internal heat generation are considered in the thermal energy equation,while the Cattaneo-Christov theory is employed to model non-Fourier heat and mass fluxes.The governing equations are non-dimensionalized to reduce complexity,and a numerical solution is obtained using a shooting method.Parametric studies are conducted to examine the influence of key dimensionless parameters on velocity,temperature,concentration,and motile microorganism profiles.The results are presented through a series of graphs,offering insight into the dynamic interplay between physical mechanisms affecting heat and mass transfer in non-Newtonian bioconvective nanofluid systems.
基金Supported by 2009 Scientific and Technological Program of Zhengzhou Provincial Department of Science and Technology:Study on the Optimal Energy-Conservation Design of Low-cost Rural Residences in Henan Province2011 Undergraduates' Innovative Program of North China University of Water Resources and Electric Power:Study on the Energy-Conservation Design of Regional Rural Residences in Henan Province
文摘Changeful and complex rural family structure and climatic features of transitional areas in China make the application of variable strategy in energy-saving rural residence designs possible.Aiming at the low cost,several effective and reasonable variable strategies were proposed for the design of interior spaces,main bedroom,sunshine room,staircase,west wall,door and window design to satisfy changing structure of a family during different periods and their different thermo-technical requirements in winter and summer.In this way,thermal comfort of rural indoor spaces will be improved,more energy saved,useful experience and thoughts provided for the energy-saving residence design in cold regions and regions hot in summer and cold in winter.
