Because of the difficulty to obtain the traffic flow information of lanes at non-detector intersections in most metropolises of the world,based on the relationships between the lanes of signal-controlled intersections...Because of the difficulty to obtain the traffic flow information of lanes at non-detector intersections in most metropolises of the world,based on the relationships between the lanes of signal-controlled intersections,cluster analysis and stepwise regression are integrated to predict the traffic volume of lanes at non-detector isolated controlled intersections.First cluster analysis is used to cluster the lanes of non-detector isolated signal-controlled intersections and the lanes of all signal-controlled intersections with detectors.Then, by the results of cluster analysis,the traffic volume samples are selected randomly and stepwise regression is used to predict the traffic volume of lanes at non-detector isolated signal-controlled intersections.The method is tested by the traffic volume data of lanes of the road network of Nanjing city.The problem of predicting the traffic volume of lanes at non-detector isolated signal-controlled intersections was resolved and can be widely used in urban traffic flow guidance and urban traffic control in cities without enough intersections equipped with detectors.展开更多
In order to solve the problem of how to collaborate with foreign agents and ontologies, a restricted clustering integration approach is proposed. It differs from the traditional approaches in which web ontology langua...In order to solve the problem of how to collaborate with foreign agents and ontologies, a restricted clustering integration approach is proposed. It differs from the traditional approaches in which web ontology language （OWL） is extended by adding some new collaborative interfaces （ i. e., agent-link and ontology-link） to it instead of owl： import. Syntaxes of the interface for foreign ontologies and foreign agents, respectively, and a meta-method of clustering integrated collaboration are discussed. The approach focuses on taking advantage of OWL itself to solve the collaborative problems, and it is feasible to track the contexts of newadded knowledge concerning ontological collaboration.展开更多
A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Th...A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.展开更多
Traditional clustering algorithms generally have some problems, such as the sensitivity to initializing parameter, difficulty in finding out the optimization clustering result and the validity of clustering. In this p...Traditional clustering algorithms generally have some problems, such as the sensitivity to initializing parameter, difficulty in finding out the optimization clustering result and the validity of clustering. In this paper, a FSM and a mathematic model of a new-style clustering algorithm based on the swarm intelligence are provided. In this algorithm, the clustering main body moves in a three-dimensional space and has the abilities of memory, communication, analysis, judgment and coordinating information. Experimental results conform that this algorithm has many merits such as insensitive to the order of the data, capable of dealing with exceptional, high-dimension or complicated data. The algorithm can be used in the fields of Web mining, incremental clustering. economic analysis, oattern recognition, document classification and so on.展开更多
For a computer to perform intelligent information processing requires functions that can extract concepts from words, as humans do, and then associate those concepts with related concepts. In order to implement this a...For a computer to perform intelligent information processing requires functions that can extract concepts from words, as humans do, and then associate those concepts with related concepts. In order to implement this association function, it is necessary to quantify the degree of association between two concepts. In the present paper, we propose a method for quantifying degree of association focusing on the viewpoint that uses a concept base （a knowledge base that expresses concepts as a collection of pairs, each pair consisting of an attribute word used to describe the concept and a weighting that expresses the word＇s importance）. Here, ＂Viewpoint＂ is the perspective from which a concept is viewed; for example, consider the degree of association between ＂airplane＂ and ＂automobile＂, and the degree of association between ＂airplane＂ and ＂bird＂. From the viewpoint of ＂vehicle＂, ＂airplane＂ and ＂automobile＂ are highly related, while from the viewpoint of ＂flight＂, ＂airplane＂ and ＂bird＂ are highly related. We present herein a comparison of two methods for calculating degree of association focusing on the viewpoint, and demonstrate that the method involving modulation of attribute weightings based on viewpoint results in degree of association calculations that are closer to human senses.展开更多
Join-aggregate is an important and widely used operation in database system. However, it is time-consuming to process join-aggregate query in big data environment, especially on MapReduce framework. The main bottlenec...Join-aggregate is an important and widely used operation in database system. However, it is time-consuming to process join-aggregate query in big data environment, especially on MapReduce framework. The main bottlenecks contain two aspects： lots of I/O caused by temporary data and heavy communication overhead between different data nodes during query processing. To overcome such disadvantages, we design a data structure called Reference Primary Key table （RPK-table） which stores the relationship of primary key and foreign key between tables. Based on this structure, we propose an improved algorithm on MapReduce framework for join-aggregate query. Experi-ments on TPC-H dataset demonstrate that our algorithm outperforms existing methods in terms of communication cost and query response time.展开更多
A preliminary investigation of shape memory (SM) effects of SMPU (shape memory polyurethane) knitting fabric is presented in this paper. Three SMPU knitted fabrics series with different content of SMPU fibers: 100% SM...A preliminary investigation of shape memory (SM) effects of SMPU (shape memory polyurethane) knitting fabric is presented in this paper. Three SMPU knitted fabrics series with different content of SMPU fibers: 100% SMPU, 50% SMPU and 50% cotton, 16% SMPU and 84% cotton are designed and manufactured in our lab. Their shape memory behaviors at different temperatures are characterized in terms of bagging. Our experimental results showed that shape memory effect can be improved with increasing content of SMPU fibers. A comparison between Lycra and SMPU knitted fabrics was also made to validate the shape memory effects of SMPU knitted fabrics.展开更多
Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many s...Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.展开更多
Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities...Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.展开更多
Chiral separation that is closely related to daily life is a meaningful research. Polysaccharide-(e.g., cellulose, amylose derivatives) based chiral packing materials afford powerful chiral stationary phases(CSPs) tow...Chiral separation that is closely related to daily life is a meaningful research. Polysaccharide-(e.g., cellulose, amylose derivatives) based chiral packing materials afford powerful chiral stationary phases(CSPs) toward a broad range of racemic compounds. However, considering the explosive growth of specific chiral drugs, the separation efficiencies of these CSPs need further improvement, which calls for new approaches and strategies. Smart polymers can change their physical or chemical properties dynamically and reversibly according to the external stimuli(e.g., thermo-, pH, solvent, ion, light, critical parameters for chromatographic separation) exerted on them, subsequently resulting in tunable changes in the macroscopic properties of materials. In addition to their excellent controllability, the introduction of chiral characteristics into the backbones or side-chains of smart polymers provides a promising route to realize reversibly conformational transition in response to the chiral analytes. This dramatic transition may significantly improve the performance of materials in chiral separation through modulating the enantioselective interactions between materials and analytes. With the help of chirality-responsive polymers, intelligent and switchable CSPs could be developed and applied in column-liquid chromatography. In these systems, the elution order or enantioselectivity of chiral drugs can be precisely modulated, which will help to solve many challenging problems that involve complicated enantiomers. In this paper we introduce some typical examples of smart polymers that serve as the basis for a discussion of emerging developments of CPSs, and then briefly outline the recent CSPs based on natural and certain synthetic polymers.展开更多
Solder bump technology has been widely used in electronic packaging. With the development of solder bumps towards higher density and finer pitch, it is more difficult to inspect the defects of solder bumps as they are...Solder bump technology has been widely used in electronic packaging. With the development of solder bumps towards higher density and finer pitch, it is more difficult to inspect the defects of solder bumps as they are hidden in the package. A nondestructive method using the transient active thermography has been proposed to inspect the defects of a solder bump, and we aim at developing an intelligent diagnosis system to eliminate the influence of emissivity unevenness and non-uniform heating on defects recognition in active infrared testing. An improved fuzzy c-means(FCM) algorithm based on the entropy weights is investigated in this paper. The captured thermograms are preprocessed to enhance the thermal contrast between the defective and good bumps. Hot spots corresponding to 16 solder bumps are segmented from the thermal images. The statistical features are calculated and selected appropriately to characterize the status of solder bumps in FCM clustering. The missing bump is identified in the FCM result, which is also validated by the principle component analysis. The intelligent diagnosis system using FCM algorithm with the entropy weights is effective for defects recognition in electronic packages.展开更多
In this review we have summarized some recent results mainly reported by our group that focused on the development of smart gating nanochannels based on polymer films. These nanochannels were prepared using a track-et...In this review we have summarized some recent results mainly reported by our group that focused on the development of smart gating nanochannels based on polymer films. These nanochannels were prepared using a track-etch process. The responsive materials/molecules and modification methods/techniques have also been demonstrated, from which we have obtained a series of smart gating nanochannels that can respond to single/dual external stimuli, e.g., pH, ion, temperature, light, and so on. These studies utilize responsive behaviors to regulate ionic transport properties inside a single nanochannel and demonstrate the fea-sibility of designing other smart nanodevices in the future.展开更多
Anchors are the key members of the geotechnical anchorage engineering, it is urgent to monitor their behavior in service for preventing the potential risks. This paper proposes a novel durable intelligent fiber reinfo...Anchors are the key members of the geotechnical anchorage engineering, it is urgent to monitor their behavior in service for preventing the potential risks. This paper proposes a novel durable intelligent fiber reinforced polymer （FRP） anchor to understand the behavior of the anchor in the field application. Series of optical fiber Bragg grating （FBG） sensors were embedded into the central axis along the anchor to sense itself axial strain variations. An evolution model of axial strain was also developed to evaluate the behavior along the anchor. A demonstration test was carried out to test its self-sensing properties in the laboratory. The experimental results indicated that the embedded FBG sensors monitored the behavior, e.g. axial strairl/interfacial damage evolutions along the anchor and displacement/applied load at the pulling end effectively, without influence from the interfacial compression and slip effects.展开更多
基金The National Natural Science Foundation of China(No.50378016).
文摘Because of the difficulty to obtain the traffic flow information of lanes at non-detector intersections in most metropolises of the world,based on the relationships between the lanes of signal-controlled intersections,cluster analysis and stepwise regression are integrated to predict the traffic volume of lanes at non-detector isolated controlled intersections.First cluster analysis is used to cluster the lanes of non-detector isolated signal-controlled intersections and the lanes of all signal-controlled intersections with detectors.Then, by the results of cluster analysis,the traffic volume samples are selected randomly and stepwise regression is used to predict the traffic volume of lanes at non-detector isolated signal-controlled intersections.The method is tested by the traffic volume data of lanes of the road network of Nanjing city.The problem of predicting the traffic volume of lanes at non-detector isolated signal-controlled intersections was resolved and can be widely used in urban traffic flow guidance and urban traffic control in cities without enough intersections equipped with detectors.
文摘In order to solve the problem of how to collaborate with foreign agents and ontologies, a restricted clustering integration approach is proposed. It differs from the traditional approaches in which web ontology language （OWL） is extended by adding some new collaborative interfaces （ i. e., agent-link and ontology-link） to it instead of owl： import. Syntaxes of the interface for foreign ontologies and foreign agents, respectively, and a meta-method of clustering integrated collaboration are discussed. The approach focuses on taking advantage of OWL itself to solve the collaborative problems, and it is feasible to track the contexts of newadded knowledge concerning ontological collaboration.
基金the National Natural Science Foundation of China (No.60472072)the Specialized Research Foundation for the Doctoral Program of Higher Educa-tion of China (No.20040699034).
文摘A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.
基金Sponsored by the Scientific Research Start-up Foundation of Qingdao University of Science and Technology.
文摘Traditional clustering algorithms generally have some problems, such as the sensitivity to initializing parameter, difficulty in finding out the optimization clustering result and the validity of clustering. In this paper, a FSM and a mathematic model of a new-style clustering algorithm based on the swarm intelligence are provided. In this algorithm, the clustering main body moves in a three-dimensional space and has the abilities of memory, communication, analysis, judgment and coordinating information. Experimental results conform that this algorithm has many merits such as insensitive to the order of the data, capable of dealing with exceptional, high-dimension or complicated data. The algorithm can be used in the fields of Web mining, incremental clustering. economic analysis, oattern recognition, document classification and so on.
文摘For a computer to perform intelligent information processing requires functions that can extract concepts from words, as humans do, and then associate those concepts with related concepts. In order to implement this association function, it is necessary to quantify the degree of association between two concepts. In the present paper, we propose a method for quantifying degree of association focusing on the viewpoint that uses a concept base （a knowledge base that expresses concepts as a collection of pairs, each pair consisting of an attribute word used to describe the concept and a weighting that expresses the word＇s importance）. Here, ＂Viewpoint＂ is the perspective from which a concept is viewed; for example, consider the degree of association between ＂airplane＂ and ＂automobile＂, and the degree of association between ＂airplane＂ and ＂bird＂. From the viewpoint of ＂vehicle＂, ＂airplane＂ and ＂automobile＂ are highly related, while from the viewpoint of ＂flight＂, ＂airplane＂ and ＂bird＂ are highly related. We present herein a comparison of two methods for calculating degree of association focusing on the viewpoint, and demonstrate that the method involving modulation of attribute weightings based on viewpoint results in degree of association calculations that are closer to human senses.
文摘Join-aggregate is an important and widely used operation in database system. However, it is time-consuming to process join-aggregate query in big data environment, especially on MapReduce framework. The main bottlenecks contain two aspects： lots of I/O caused by temporary data and heavy communication overhead between different data nodes during query processing. To overcome such disadvantages, we design a data structure called Reference Primary Key table （RPK-table） which stores the relationship of primary key and foreign key between tables. Based on this structure, we propose an improved algorithm on MapReduce framework for join-aggregate query. Experi-ments on TPC-H dataset demonstrate that our algorithm outperforms existing methods in terms of communication cost and query response time.
基金Project support by the Study of Temperature-Sensitive Shape-Memory Polymers for Smart Textile Applications, Shape MemoryCenter of Hong Kong Polytechnic University, HK, China
文摘A preliminary investigation of shape memory (SM) effects of SMPU (shape memory polyurethane) knitting fabric is presented in this paper. Three SMPU knitted fabrics series with different content of SMPU fibers: 100% SMPU, 50% SMPU and 50% cotton, 16% SMPU and 84% cotton are designed and manufactured in our lab. Their shape memory behaviors at different temperatures are characterized in terms of bagging. Our experimental results showed that shape memory effect can be improved with increasing content of SMPU fibers. A comparison between Lycra and SMPU knitted fabrics was also made to validate the shape memory effects of SMPU knitted fabrics.
文摘Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.
文摘Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.
基金supported by the National Natural Science Foundation of China(21104061,21275114,91127027,51173142)the China National Funds for Distinguished Young Scientists(51325302)+2 种基金the Major State Basic Research Development Program of China(2013CB933002)the Program of Introducing Talents of Discipline to Universities(B13035)Hubei Provincial Department of Education for financial assistance through the Chutian Scholar Program
文摘Chiral separation that is closely related to daily life is a meaningful research. Polysaccharide-(e.g., cellulose, amylose derivatives) based chiral packing materials afford powerful chiral stationary phases(CSPs) toward a broad range of racemic compounds. However, considering the explosive growth of specific chiral drugs, the separation efficiencies of these CSPs need further improvement, which calls for new approaches and strategies. Smart polymers can change their physical or chemical properties dynamically and reversibly according to the external stimuli(e.g., thermo-, pH, solvent, ion, light, critical parameters for chromatographic separation) exerted on them, subsequently resulting in tunable changes in the macroscopic properties of materials. In addition to their excellent controllability, the introduction of chiral characteristics into the backbones or side-chains of smart polymers provides a promising route to realize reversibly conformational transition in response to the chiral analytes. This dramatic transition may significantly improve the performance of materials in chiral separation through modulating the enantioselective interactions between materials and analytes. With the help of chirality-responsive polymers, intelligent and switchable CSPs could be developed and applied in column-liquid chromatography. In these systems, the elution order or enantioselectivity of chiral drugs can be precisely modulated, which will help to solve many challenging problems that involve complicated enantiomers. In this paper we introduce some typical examples of smart polymers that serve as the basis for a discussion of emerging developments of CPSs, and then briefly outline the recent CSPs based on natural and certain synthetic polymers.
基金supported by the National Natural Science Foundation of China(Grant Nos.51305179&51305177)the Natural Science Foundation of Jiangsu Higher Education Institutions(Grant No.13KJB510009)
文摘Solder bump technology has been widely used in electronic packaging. With the development of solder bumps towards higher density and finer pitch, it is more difficult to inspect the defects of solder bumps as they are hidden in the package. A nondestructive method using the transient active thermography has been proposed to inspect the defects of a solder bump, and we aim at developing an intelligent diagnosis system to eliminate the influence of emissivity unevenness and non-uniform heating on defects recognition in active infrared testing. An improved fuzzy c-means(FCM) algorithm based on the entropy weights is investigated in this paper. The captured thermograms are preprocessed to enhance the thermal contrast between the defective and good bumps. Hot spots corresponding to 16 solder bumps are segmented from the thermal images. The statistical features are calculated and selected appropriately to characterize the status of solder bumps in FCM clustering. The missing bump is identified in the FCM result, which is also validated by the principle component analysis. The intelligent diagnosis system using FCM algorithm with the entropy weights is effective for defects recognition in electronic packages.
基金supported by the National Basic Research Program of China(973 Program,2011CB935703,2010CB934700,2009CB930404 &2007CB936403)National Natural Science Foundation of China(20974113,20920102036)Center for Molecular Science,Chinese Academy of Sciences (CX-201014)
文摘In this review we have summarized some recent results mainly reported by our group that focused on the development of smart gating nanochannels based on polymer films. These nanochannels were prepared using a track-etch process. The responsive materials/molecules and modification methods/techniques have also been demonstrated, from which we have obtained a series of smart gating nanochannels that can respond to single/dual external stimuli, e.g., pH, ion, temperature, light, and so on. These studies utilize responsive behaviors to regulate ionic transport properties inside a single nanochannel and demonstrate the fea-sibility of designing other smart nanodevices in the future.
基金supported by the National Natural Science Foundation of China (Grant No. 50978079)the National Scientific Support Project (Grant No. 2011BAK02B01)
文摘Anchors are the key members of the geotechnical anchorage engineering, it is urgent to monitor their behavior in service for preventing the potential risks. This paper proposes a novel durable intelligent fiber reinforced polymer （FRP） anchor to understand the behavior of the anchor in the field application. Series of optical fiber Bragg grating （FBG） sensors were embedded into the central axis along the anchor to sense itself axial strain variations. An evolution model of axial strain was also developed to evaluate the behavior along the anchor. A demonstration test was carried out to test its self-sensing properties in the laboratory. The experimental results indicated that the embedded FBG sensors monitored the behavior, e.g. axial strairl/interfacial damage evolutions along the anchor and displacement/applied load at the pulling end effectively, without influence from the interfacial compression and slip effects.
