Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index predict...Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index prediction. The parameters (C, σ) of SVR models were selected by three different methods of grid search (GRID), particle swarm optimization (PSO) and genetic algorithm (GA).The optimized parameters were used to predict the opening price of the test samples. The predictive results shown that the SVR model with GRID (GRID-SVR), the SVR model with PSO (PSO-SVR) and the SVR model with GA (GA-SVR) were capable to fully demonstrate the time-dependent trend of stock index and had the significant prediction accuracy. The minimum root mean square error (RMSE) of the GA-SVR model was 15.630, the minimum mean absolute percentage error (MAPE) equaled to 0.39% and the correspondent optimal parameters (C, σ) were identified as (45.422, 0.012). The appreciated modeling results provided theoretical and technical reference for investors to make a better trading strategy.展开更多
Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed sea...Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed search schemes decreases as the index increases.For solving this problem,we build the two-level index.Simultaneously,for improving the semantic information,the central word expansion is combined.The purpose of privacy-preserving content-aware search by using the two-level index(CKESS)is that the first matching is performed by using the extended central words,then calculate the similarity between the trapdoor and the secondary index,finally return the results in turn.Through experiments and analysis,it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient.展开更多
Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (1D) transformation can break the curs...Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (1D) transformation can break the curse of dimensionality. Based on the two techniques above, a novel high-dimensional index is proposed, called Bit-code and Distance based index (BD). BD is based on a special partitioning strategy which is optimized for high-dimensional data. By the definitions of bit code and transformation function, a high-dimensional vector can be first approximately represented and then transformed into a 1D vector, the key managed by a B+-tree. A new KNN search algorithm is also proposed that exploits the bit code and distance to prune the search space more effectively. Results of extensive experiments using both synthetic and real data demonstrated that BD out- performs the existing index structures for KNN search in high-dimensional spaces.展开更多
In this paper, we propose a new method based on index to realize IR-style Chinese keyword search with ranking strategies in relational databases. This method creates an index by using the related information of tuple ...In this paper, we propose a new method based on index to realize IR-style Chinese keyword search with ranking strategies in relational databases. This method creates an index by using the related information of tuple words and presents a ranking strategy in terms of the nature of Chinese words. For a Chinese keyword query, the index is used to match query search words and the tuple words in index quickly, and to compute similarities between the query and tuples by the ranking strategy, and then the set of identifiers of candidate tuples is generated. Thus, we retrieve top-N results of the query using SQL selection statements and output the ranked answers according to the similarities. The experimental results show that our method is efficient and effective.展开更多
Most academic information has its creator, that is, a subject who has created the information. The subject can be an individual, a group, or an institution, and can be a nation depending on the nature of the relevant ...Most academic information has its creator, that is, a subject who has created the information. The subject can be an individual, a group, or an institution, and can be a nation depending on the nature of the relevant information. Most web data are composed of a title, an author, and contents. A paper which is under the academic information category has metadata including a title, an author, keyword, abstract, data about publication, place of publication, ISSN, and the like. A patent has metadata including the title, an applicant, an inventor, an attorney, IPC, number of application, and claims of the invention. Most web-based academic information services enable users to search the information by processing the meta-information. An important element is to search information by using the author field which corresponds to a personal name. This study suggests a method of efficient indexing and using the adjacent operation result ranking algorithm to which phrase search-based boosting elements are applied, and thus improving the accuracy of the search results of author name. This method can be effectively applied to providing accurate search results in the academic information services.展开更多
To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent s...To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent search can be supported. TCMSearch, a deployed intelligent search engine for traditional Chinese medicine （TCM）, is presented. The core of the system is an integrated knowledge base that uses a TCM domain ontology to represent the instances and relationships in TCM. Machine-learning techniques are used to generate semantic annotations for texts and semantic mappings for relational databases, and then a semantic index is constructed for these resources. The major benefit of representing the semantic index in RDF/OWL is to support some powerful reasoning functions, such as class hierarchies and relation inferences. By combining resource integration with reasoning, the knowledge base can support some intelligent search paradigms besides keyword search, such as correlated search, semantic graph navigation and concept recommendation.展开更多
Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal...Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices （SVIs） were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index （DVI） and LAI, with the adjusted R2 （0.82） and the RMSE （0.77）. The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value （30 m） for winter wheat of the study area increased from 1.29 （March 7, 2004） to 3.43 （April 8, 2004）, with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.展开更多
In propylene polymerization(PP) process, the melt index(MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of M...In propylene polymerization(PP) process, the melt index(MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of MI is essential for efficient and professional monitoring and control of practical PP processes. This paper presents a novel soft sensor based on extreme learning machine(ELM) and modified gravitational search algorithm(MGSA) to estimate MI from real PP process variables, where the MGSA algorithm is developed to find the best parameters of input weights and hidden biases for ELM. As the comparative basis, the models of ELM, APSO-ELM and GSAELM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. The research results show the accuracy and universality of the proposed model and it can be a powerful tool for online MI prediction.展开更多
文摘Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index prediction. The parameters (C, σ) of SVR models were selected by three different methods of grid search (GRID), particle swarm optimization (PSO) and genetic algorithm (GA).The optimized parameters were used to predict the opening price of the test samples. The predictive results shown that the SVR model with GRID (GRID-SVR), the SVR model with PSO (PSO-SVR) and the SVR model with GA (GA-SVR) were capable to fully demonstrate the time-dependent trend of stock index and had the significant prediction accuracy. The minimum root mean square error (RMSE) of the GA-SVR model was 15.630, the minimum mean absolute percentage error (MAPE) equaled to 0.39% and the correspondent optimal parameters (C, σ) were identified as (45.422, 0.012). The appreciated modeling results provided theoretical and technical reference for investors to make a better trading strategy.
基金This work is supported by the National Natural Science Foundation of China under grant U1836110,U1836208,U1536206,61602253,61672294by the National Key R&D Program of China under grant 2018YFB1003205+5 种基金by China Postdoctoral Science Foundation(2017M610574)by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20181407by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Major Program of the National Social Science Fund of China(17ZDA092)Qing Lan Projectby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed search schemes decreases as the index increases.For solving this problem,we build the two-level index.Simultaneously,for improving the semantic information,the central word expansion is combined.The purpose of privacy-preserving content-aware search by using the two-level index(CKESS)is that the first matching is performed by using the extended central words,then calculate the similarity between the trapdoor and the secondary index,finally return the results in turn.Through experiments and analysis,it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient.
基金Project (No. [2005]555) supported by the Hi-Tech Research and De-velopment Program (863) of China
文摘Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (1D) transformation can break the curse of dimensionality. Based on the two techniques above, a novel high-dimensional index is proposed, called Bit-code and Distance based index (BD). BD is based on a special partitioning strategy which is optimized for high-dimensional data. By the definitions of bit code and transformation function, a high-dimensional vector can be first approximately represented and then transformed into a 1D vector, the key managed by a B+-tree. A new KNN search algorithm is also proposed that exploits the bit code and distance to prune the search space more effectively. Results of extensive experiments using both synthetic and real data demonstrated that BD out- performs the existing index structures for KNN search in high-dimensional spaces.
文摘In this paper, we propose a new method based on index to realize IR-style Chinese keyword search with ranking strategies in relational databases. This method creates an index by using the related information of tuple words and presents a ranking strategy in terms of the nature of Chinese words. For a Chinese keyword query, the index is used to match query search words and the tuple words in index quickly, and to compute similarities between the query and tuples by the ranking strategy, and then the set of identifiers of candidate tuples is generated. Thus, we retrieve top-N results of the query using SQL selection statements and output the ranked answers according to the similarities. The experimental results show that our method is efficient and effective.
文摘Most academic information has its creator, that is, a subject who has created the information. The subject can be an individual, a group, or an institution, and can be a nation depending on the nature of the relevant information. Most web data are composed of a title, an author, and contents. A paper which is under the academic information category has metadata including a title, an author, keyword, abstract, data about publication, place of publication, ISSN, and the like. A patent has metadata including the title, an applicant, an inventor, an attorney, IPC, number of application, and claims of the invention. Most web-based academic information services enable users to search the information by processing the meta-information. An important element is to search information by using the author field which corresponds to a personal name. This study suggests a method of efficient indexing and using the adjacent operation result ranking algorithm to which phrase search-based boosting elements are applied, and thus improving the accuracy of the search results of author name. This method can be effectively applied to providing accurate search results in the academic information services.
基金Program for Changjiang Scholars and Innovative Research Team in University (NoIRT0652)the National High Technology Research and Development Program of China (863 Program) ( No2006AA01A123)
文摘To semantically integrate heterogeneous resources and provide a unified intelligent access interface, semantic web technology is exploited to publish and interlink machineunderstandable resources so that intelligent search can be supported. TCMSearch, a deployed intelligent search engine for traditional Chinese medicine （TCM）, is presented. The core of the system is an integrated knowledge base that uses a TCM domain ontology to represent the instances and relationships in TCM. Machine-learning techniques are used to generate semantic annotations for texts and semantic mappings for relational databases, and then a semantic index is constructed for these resources. The major benefit of representing the semantic index in RDF/OWL is to support some powerful reasoning functions, such as class hierarchies and relation inferences. By combining resource integration with reasoning, the knowledge base can support some intelligent search paradigms besides keyword search, such as correlated search, semantic graph navigation and concept recommendation.
文摘Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices （SVIs） were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index （DVI） and LAI, with the adjusted R2 （0.82） and the RMSE （0.77）. The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value （30 m） for winter wheat of the study area increased from 1.29 （March 7, 2004） to 3.43 （April 8, 2004）, with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.
基金Supported by the Major Program of National Natural Science Foundation of China(61590921)the Natural Science Foundation of Zhejiang Province(Y16B040003)+1 种基金Shanghai Aerospace Science and Technology Innovation Fund(E11501)Aerospace Science and Technology Innovation Fund of China,Aerospace Science and Technology Corporation(E11601)
文摘In propylene polymerization(PP) process, the melt index(MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of MI is essential for efficient and professional monitoring and control of practical PP processes. This paper presents a novel soft sensor based on extreme learning machine(ELM) and modified gravitational search algorithm(MGSA) to estimate MI from real PP process variables, where the MGSA algorithm is developed to find the best parameters of input weights and hidden biases for ELM. As the comparative basis, the models of ELM, APSO-ELM and GSAELM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. The research results show the accuracy and universality of the proposed model and it can be a powerful tool for online MI prediction.
