Suction caissons can readily penetrate into the seabed under the combination of the self-weight and suction resulted from the encased water being increasingly pumped out. During suction-assisted penetration, the equiv...Suction caissons can readily penetrate into the seabed under the combination of the self-weight and suction resulted from the encased water being increasingly pumped out. During suction-assisted penetration, the equivalent overburden at the skirt-tip level outside the caisson is generally higher than that inside because the vertical stress within the soil plug is reduced by the exerted suction. This may result in a uniform shear stress developing over the base of the skirt-tip as the soil below the skirt-tip tends to move into the caisson, which leads to an asymmetric failure wedge existing below the base of the skirt-tip. Besides, different adhesion factors along the inside(αi) and outside(αo) of the skirt wall will cause asymmetric plastic zones inside and outside the caisson. Accordingly, an asymmetric failure mechanism is therefore proposed to calculate the penetration resistance of the skirt-tip. The proposed failure mechanism is the first to consider the effect of different adhesion factors(αi) and(αo) on the failure mechanism at the skirt-tip, and involves the contribution from the weighted average of equivalent overburdens inside and outside caisson at the skirt-tip level. The required suction pressure can be obtained in terms of force equilibrium of the caisson in a vertical direction. Finally, the asymmetric failure mechanism at the skirt-tip is validated with the FE calculations. By comparing with the measured data, the predictions of the required suction pressure are found to be in good agreement with the experimental results.展开更多
Covering-based rough sets process data organized by a covering of the universe. A soft set is a parameterized family of subsets of the universe. Both theories can deal with the uncertainties of data. Soft sets have no...Covering-based rough sets process data organized by a covering of the universe. A soft set is a parameterized family of subsets of the universe. Both theories can deal with the uncertainties of data. Soft sets have not any restrictions on the approximate description of the object,and they might form a covering of the universe. From this viewpoint,we establish a connection between these two theories. Specifically,we propose a complementary parameter for this purpose. With this parameter,the soft covering approximation space is established and the two theories are bridged. Furthermore,we study some relations between the covering and the soft covering approximation space and obtain some significant results. Finally,we define a notion of combine parameter which can help us to simplify the set of parameters and reduce the storage requirement of a soft covering approximation space.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
Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computin...Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.展开更多
In our study, the Dominance-based Rough Set Approach (DRSA) has been proposed to assist the Board of Directors of the Community Futures Development Corporations (CFDC), the sub-region of Abitibi-West (Quebec). The CFD...In our study, the Dominance-based Rough Set Approach (DRSA) has been proposed to assist the Board of Directors of the Community Futures Development Corporations (CFDC), the sub-region of Abitibi-West (Quebec). The CFDC needs a tool for decision support to select the projects that are proposed by the contractors and partners of its territory. In decision making, a balanced set of 22 indicators is considered. These indicators derive from five perspectives: economic, social, demographic, health and wellness. The DRSA proposal is suitable for the data processing with multiple indicators providing on many examples to infer decision rules related to the preference model. In this paper we show that decision rules developed with the use of rough set theory allow us to simplify the process of selecting a portfolio for sustainable development by reducing a number of redundant indicators and identifying the critical values of selected indicators.展开更多
In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result...In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.展开更多
The effects of 3 chairside polishing kits and mechanical brushing on the surface roughness of 3 different acrylic denture base resins were compared. Acrylic denture base resins （auto-polymerizing, heat-polymerizing, ...The effects of 3 chairside polishing kits and mechanical brushing on the surface roughness of 3 different acrylic denture base resins were compared. Acrylic denture base resins （auto-polymerizing, heat-polymerizing, injected heat-polymerizing resins） were examined after a tungsten carbide bur, and after chairside polishing using 3 polishing kits and pumice. The specimens were subjected to mechanical brushing using a wear tester to simulate 30 000 strokes of brushing. The surface roughness of the acrylic denture base resin specimens was measured using a contact pro-filometer. After the test, the random polished acrylic resins were evaluated by scanning electron mi-croscopy （SEM） and atomic force microscopy （AFM）. Acrylic denture base resins polished using the 3 types of polishing kits had a smoother surface than those finished with the tungsten carbide bur （p〈0.05）. The surface of the resin polished by a TC cutter exceeded the Ra of 0.2 μm （p〈0.05）. The auto-polymerizing resin showed a significantly higher surface roughness than the heat-polymerizing resin and injected heat-polymerizing resin （p〉0.05）. In the case of polishing step wise, there was almost no change in surface roughness after brushing （p〉0.05）.展开更多
This paper investigates a simplified method to determine the optimal stiffness of flexible connectors on a mobile offshore base(MOB) during the preliminary design stage. A three-module numerical model of an MOB was us...This paper investigates a simplified method to determine the optimal stiffness of flexible connectors on a mobile offshore base(MOB) during the preliminary design stage. A three-module numerical model of an MOB was used as a case study. Numerous constraint forces and relative displacements for the connectors at rough sea states with different wave angles were utilized to determine the optimized stiffness of the flexible connectors. The range of optimal stiffnesses for the connectors was obtained based on the combination and intersection of the optimized stiffness results, and the implementation steps were elaborated in detail. The percentage reductions of the optimized and optimal stiffness of the flexible connector were determined to quantitatively evaluate the decreases of the constraint force and relative displacement of the connectors compared with those calculated by using the original range of the connector stiffnesses. The results indicate the accuracy and feasibility of this method for determining the optimal stiffness of the flexible connectors and demonstrate the rationality and practicability of the optimal stiffness results. The research ideas, calculation process, and solutions for the optimal stiffness of the flexible connectors of an MOB in this paper can provide valuable technical support for the design of the connectors in similar semisubmersible floating structures.展开更多
The problem considered in this paper is how to detect the degree of similarity in the content of digital images useful in image retrieval,i.e.,to what extent is the content of a query image similar to content of other...The problem considered in this paper is how to detect the degree of similarity in the content of digital images useful in image retrieval,i.e.,to what extent is the content of a query image similar to content of other images.The solution to this problem results from the detection of subsets that are rough sets contained in covers of digital images determined by perceptual tolerance relations(PTRs).Such relations are defined within the context of perceptual representative spaces that hearken back to work by J.H.Poincare on representative spaces as models of physical continua.Classes determined by a PTR provide content useful in content-based image retrieval(CBIR).In addition,tolerance classes provide a means of determining when subsets of image covers are tolerance rough sets(TRSs).It is the nearness of TRSs present in image tolerance spaces that provide a promising approach to CBIR,especially in cases such as satellite images or aircraft identification where there are subtle differences between pairs of digital images,making it difficult to quantify the similarities between such images.The contribution of this article is the introduction of the nearness of tolerance rough sets as an effective means of measuring digital image similarities and,as a significant consequence,successfully carrying out CBIR.展开更多
Feature selection（FS） aims to determine a minimal feature（attribute） subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory（RST） has been us...Feature selection（FS） aims to determine a minimal feature（attribute） subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory（RST） has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone,requiring no additional information. This paper describes the fundamental ideas behind RST-based approaches,reviews related FS methods built on these ideas,and analyses more frequently used RST-based traditional FS algorithms such as Quickreduct algorithm,entropy based reduct algorithm,and relative reduct algorithm. It is found that some of the drawbacks in the existing algorithms and our proposed improved algorithms can overcome these drawbacks. The experimental analyses have been carried out in order to achieve the efficiency of the proposed algorithms.展开更多
基金financially supported by the National Natural Science Foundation of China (Grant Nos. 51639002 and 51879044)the SDUST Research Fund (Grant No. 2015KYJH104)。
文摘Suction caissons can readily penetrate into the seabed under the combination of the self-weight and suction resulted from the encased water being increasingly pumped out. During suction-assisted penetration, the equivalent overburden at the skirt-tip level outside the caisson is generally higher than that inside because the vertical stress within the soil plug is reduced by the exerted suction. This may result in a uniform shear stress developing over the base of the skirt-tip as the soil below the skirt-tip tends to move into the caisson, which leads to an asymmetric failure wedge existing below the base of the skirt-tip. Besides, different adhesion factors along the inside(αi) and outside(αo) of the skirt wall will cause asymmetric plastic zones inside and outside the caisson. Accordingly, an asymmetric failure mechanism is therefore proposed to calculate the penetration resistance of the skirt-tip. The proposed failure mechanism is the first to consider the effect of different adhesion factors(αi) and(αo) on the failure mechanism at the skirt-tip, and involves the contribution from the weighted average of equivalent overburdens inside and outside caisson at the skirt-tip level. The required suction pressure can be obtained in terms of force equilibrium of the caisson in a vertical direction. Finally, the asymmetric failure mechanism at the skirt-tip is validated with the FE calculations. By comparing with the measured data, the predictions of the required suction pressure are found to be in good agreement with the experimental results.
基金supported by National Natural Science Foundation of China under Grant No. 60873077/F020107the Science Research Project of Zhangzhou Normal University under Grant No. SK09002
文摘Covering-based rough sets process data organized by a covering of the universe. A soft set is a parameterized family of subsets of the universe. Both theories can deal with the uncertainties of data. Soft sets have not any restrictions on the approximate description of the object,and they might form a covering of the universe. From this viewpoint,we establish a connection between these two theories. Specifically,we propose a complementary parameter for this purpose. With this parameter,the soft covering approximation space is established and the two theories are bridged. Furthermore,we study some relations between the covering and the soft covering approximation space and obtain some significant results. Finally,we define a notion of combine parameter which can help us to simplify the set of parameters and reduce the storage requirement of a soft covering approximation space.
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.
文摘Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.
文摘In our study, the Dominance-based Rough Set Approach (DRSA) has been proposed to assist the Board of Directors of the Community Futures Development Corporations (CFDC), the sub-region of Abitibi-West (Quebec). The CFDC needs a tool for decision support to select the projects that are proposed by the contractors and partners of its territory. In decision making, a balanced set of 22 indicators is considered. These indicators derive from five perspectives: economic, social, demographic, health and wellness. The DRSA proposal is suitable for the data processing with multiple indicators providing on many examples to infer decision rules related to the preference model. In this paper we show that decision rules developed with the use of rough set theory allow us to simplify the process of selecting a portfolio for sustainable development by reducing a number of redundant indicators and identifying the critical values of selected indicators.
基金National Natural Science Foundation of China(No.51175077)
文摘In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.
文摘The effects of 3 chairside polishing kits and mechanical brushing on the surface roughness of 3 different acrylic denture base resins were compared. Acrylic denture base resins （auto-polymerizing, heat-polymerizing, injected heat-polymerizing resins） were examined after a tungsten carbide bur, and after chairside polishing using 3 polishing kits and pumice. The specimens were subjected to mechanical brushing using a wear tester to simulate 30 000 strokes of brushing. The surface roughness of the acrylic denture base resin specimens was measured using a contact pro-filometer. After the test, the random polished acrylic resins were evaluated by scanning electron mi-croscopy （SEM） and atomic force microscopy （AFM）. Acrylic denture base resins polished using the 3 types of polishing kits had a smoother surface than those finished with the tungsten carbide bur （p〈0.05）. The surface of the resin polished by a TC cutter exceeded the Ra of 0.2 μm （p〈0.05）. The auto-polymerizing resin showed a significantly higher surface roughness than the heat-polymerizing resin and injected heat-polymerizing resin （p〉0.05）. In the case of polishing step wise, there was almost no change in surface roughness after brushing （p〉0.05）.
基金financially supported by the National Key Research and Development Program of China(Grant Nos.2016YFC0802204and 2016YFC0802201)the National Natural Science Foundation of China(Grant No.51679166)+2 种基金the National Natural Science Fund for Innovative Research Groups Science Foundation(Grant No.51321065)the Construction Science and Technology Project of the Ministry of Transport of the People’s Republic of China(Grant No.2014328224040)the Innovative Research Program for Graduate Students at Chongqing Jiaotong University(Grant No.20140104)
文摘This paper investigates a simplified method to determine the optimal stiffness of flexible connectors on a mobile offshore base(MOB) during the preliminary design stage. A three-module numerical model of an MOB was used as a case study. Numerous constraint forces and relative displacements for the connectors at rough sea states with different wave angles were utilized to determine the optimized stiffness of the flexible connectors. The range of optimal stiffnesses for the connectors was obtained based on the combination and intersection of the optimized stiffness results, and the implementation steps were elaborated in detail. The percentage reductions of the optimized and optimal stiffness of the flexible connector were determined to quantitatively evaluate the decreases of the constraint force and relative displacement of the connectors compared with those calculated by using the original range of the connector stiffnesses. The results indicate the accuracy and feasibility of this method for determining the optimal stiffness of the flexible connectors and demonstrate the rationality and practicability of the optimal stiffness results. The research ideas, calculation process, and solutions for the optimal stiffness of the flexible connectors of an MOB in this paper can provide valuable technical support for the design of the connectors in similar semisubmersible floating structures.
基金supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) research grants 194376 and 185986Manitoba Centre of Excellence Fund(MCEF) grant and Canadian Network Centre of Excellence(NCE) and Canadian Arthritis Network(CAN) grant SRI-BIO-05.
文摘The problem considered in this paper is how to detect the degree of similarity in the content of digital images useful in image retrieval,i.e.,to what extent is the content of a query image similar to content of other images.The solution to this problem results from the detection of subsets that are rough sets contained in covers of digital images determined by perceptual tolerance relations(PTRs).Such relations are defined within the context of perceptual representative spaces that hearken back to work by J.H.Poincare on representative spaces as models of physical continua.Classes determined by a PTR provide content useful in content-based image retrieval(CBIR).In addition,tolerance classes provide a means of determining when subsets of image covers are tolerance rough sets(TRSs).It is the nearness of TRSs present in image tolerance spaces that provide a promising approach to CBIR,especially in cases such as satellite images or aircraft identification where there are subtle differences between pairs of digital images,making it difficult to quantify the similarities between such images.The contribution of this article is the introduction of the nearness of tolerance rough sets as an effective means of measuring digital image similarities and,as a significant consequence,successfully carrying out CBIR.
基金supported by the UGC, SERO, Hyderabad under FDP during XI plan period, and the UGC, New Delhi for financial assistance under major research project Grant No. F-34-105/2008
文摘Feature selection（FS） aims to determine a minimal feature（attribute） subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory（RST） has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone,requiring no additional information. This paper describes the fundamental ideas behind RST-based approaches,reviews related FS methods built on these ideas,and analyses more frequently used RST-based traditional FS algorithms such as Quickreduct algorithm,entropy based reduct algorithm,and relative reduct algorithm. It is found that some of the drawbacks in the existing algorithms and our proposed improved algorithms can overcome these drawbacks. The experimental analyses have been carried out in order to achieve the efficiency of the proposed algorithms.
