IJncertainty Modeling Based on Bayesian Metwork in Ontology Mapping

How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks （BN）. In our approach, ontology Web language （OWL） is extended to add probabilistie markups for attaching probability information, the source and target ontol ogies （expressed by patulous OWL） are translated into hayesian networks （BNs）, the mapping between the two ontologies can be digged out by constructing the conditional probability tables （CPTs） of the BN using a improved algorithm named I-IPFP based on iterative proportional fitting procedure （IPFP）. The basic idea of this framework and algorithm are validated by positive results from computer experiments.     

Biological networks to the analysis of microarray data

Microarray technology, which permits rapid and large-scale screening for patterns of gene expressions, usually generates a large amount of data. How to mine the biological meanings under these data is one of the main challenges in bioinformatics. Compared to the pure mathematical techniques, those methods incorporated with some prior biological knowledge generally bring better interpretations. Recently, a new analysis, in which the knowledge of biological networks such as metabolic network and protein interaction network is introduced, is widely applied to microarray data analysis. The microarray data analysis based on biological networks contains two main research aspects: identification of active components in biological networks and assessment of gene sets significance. In this paper, we briefly review the progress of these two categories of analyses, especially some representative methods.     

Method of Automatic Ontology Mapping through Machine Learning and Logic Mining

Ontology mapping is the bottleneck ot handhng confilicts among heterogeneous ontologies and of implementing reconfiguration or interoperability of legacy systems. We proposed an ontology mapping method by using machine learning, type constraints and logic mining techniques. This method is able to find concept correspondences through instances and the result is optimized by using an error function; it is able to find attribute correspondence between two equivalent concepts and the mapping accuracy is enhanced by combining together instances learning, type constraints and the logic relations that are imbedded in instances; moreover, it solves the most common kind of categorization conflicts. We thenproposed a merging algorithm to generate the shared ontology and proposed a reconfigurable architecture for interoperation based on multi agents. The legacy systems are encapsulated as information agents to participate in the integration system. Finally we give a simplified case study.     

Dynamic Analysis of Cable Roofs Under Transient Wind:A Comparison Between Time Domain and Frequency Domain Approaches

At present, high-speed computing capabilities and advanced nonlinear dynamic finite element procedures enable detailed dynamic analysis of cable structures. Although deterministic approaches require considerable analysis time and effort in relation to modeling, running, and data processing, they seem to be the only alternative to obtain high accuracy. Detailed dynamic analysis of cable roof networks is sophisticated and requires advanced modeling expertise. This paper presents a comparison between detailed nonlinear dynamic analysis and a simplified frequency domain approach to estimate the maximum probable response of weakly nonlinear cable roofs. The approach can be considered as alternative to detailed time-domain analysis in the preliminary design phase, or can be used to validate results obtained from more elaborated numerical models. The proposed method is illustrated with two examples of cable net roofs that were also analysed in the time domain.     

Multidisciplinary design optimization approach and its application to aerospace engineering

The multidisciplinary design optimization（MDO）approach is very crucial for the conceptual design of the aircraft,and this is due to the complex interaction between different disciplines, especially for the hypersonic vehicle. In the hypersonic vehicle, the airframe and the engine are highly integrated. In the current survey, the research progress in the MDO approach has been reviewed comprehensively from four primary techniques, namely the design of experiments, the surrogate modeling, the optimization method and the MDO framework, and its application to the aerospace engineering has been summarized from its category as well. The concurrent subspace optimization approach is the most popular method in the aerospace engineering for its easy implementation, and following is the collaborative optimization approach. The multi-level optimization approach has attracted a growing attention due to its high efficiency. In conclusion, some promising research recommendations have been put forward, namely the combination of the low- and high-fidelity optimization approaches, the consideration of the uncertainty analysis and the MDO approach coupled with the data mining techniques.     

A Computer-Aided FPS-Oriented Approach for Construction Briefing

Function performance specification (FPS) is one of the value management (VM) techniques developed for the explicit statement of optimum product definition. This technique is widely used in software engineering and manufacturing industry, and proved to be successful to perform product defining tasks. This paper describes an FPS-oriented approach for construction briefing, which is critical to the successful delivery of construction projects. Three techniques, i.e., function analysis system technique, shared space, and computer-aided toolkit, are incorporated into the proposed approach. A computer-aided toolkit is developed to facilitate the implementation of FPS in the briefing processes. This approach can facilitate systematic, efficient identification, clarification, and representation of client requirements in trail running. The limitations of the approach and future research work are also discussed at the end of the paper.     

Bridge Ontology： A Multi-Ontologies.Based Approach for Semantic Annotation

Representing the relationships between ontologies is the key problem of semantic annotations based on multi ontologies. Traditional approaches only had the ability of denoting the simple concept subsumption relations between ontologies. Through analyzing and classifying the relationships between ontologies, the idea of bridge ontology was proposed, which had the powerful capability of expressing the complex relationships between concepts and relationships between relations in muhi ontologies. Meanwhile, a new approach employing bridge ontology was proposed to deal with the multi ontologies-based semantic annotation problem. The bridge ontology is a peculiar ontology, which can be created and maintained conveniently, and is effective in the multi-ontologies based semantic annotation. The approach using bridge ontology has the advantages of low cost, scalablc, robust in the web circumstance, and avoiding the unnecessary ontology extending and integration.     

Pedigree transmission disequilibrium test for QTL mapping of threshold trait

With biological and economic importance,threshold traits are discrete in phenotype but have the same polygenic genetic basis as quantitative traits. The traditional linkage analysis for quantitative traits is invalid for threshold traits due to their special characters. The transmission disequilibrium test (TDT) has received great attention recently in localizing human disease genes due to its simplicity and powerfulness. But TDT only deals with data from independent nuclear families and it will lose information about extended pedigree that incorporates information not only from parents and siblings but also from other relatives. The pedigree disequilibrium test (PDT) proposed by Martin in 2001 can be used to analyze the extended pedigree in human.In this study, PDT was introduced into the QTL mapping of threshold traits for farm animals, and was modified in order to accommodate the pedigree structures of farm animals.The modified PDT was renamed pedigree transmission disequilibrium test (PTDT) and its power and type I error were investigated and compared with that of PDT by Monte Carlo simulation. It was shown that PTDT is a robust and valid approach to mapping QTL of threshold trait. When the parental information is complete, PTDT and PDT are almost the same in terms of power and type I error. However, if the parental information is missing to a certain extent, PTDT is higher in power and lower in type I error than PDT. These results imply that PTDT can be a novel approach to QTL fine mapping of threshold traits based on the existing coarse mapping information.     

Risk Assessment Mode of Sale-Leaseback——Based on Fuzzy Analytic Hierarchy Process

Sale-leaseback is paid special focus on for its great application in China in recent years.By giving a detailed analysis,an analysis of the features of sale-leaseback and a study of the risks of it are given in this paper.Then a quantitative analysis of commercial real estate sale-leaseback is carried out.The scientific theoretical analysis is provided in this paper for risk assessment in commercial real estate sale-leaseback mode in China.An effective approach is put forward for sale-leaseback project evaluation and prevention of risk.A useful means is given to supervise commerciai real estate sale-leaseback mode for relevant departments and a certain contribution is made to a further sound development of commercial real estate sale-leaseback mode.     

A Machine Learning Approach to Automated Negotiation

Automated negotiation between two competitive agents is analyzed, and a multi-issue negotiation model based on machine learning, time belief, offer belief and state-action pair expected Q value is developed. Unlike the widely used approaches such as game theory approach, heuristic approach and argumentation approach, This paper uses a machine learning method to compute agents‘ average Q values in each negotiation stage. The delayed reward is used to generate agents‘ offer and counteroffer of every issue. The effect of time and discount rate on negotiation outcome is analyzed. Theory analysis and experimental data show this negotiation model is practical.     

Multiscale coupling:challenges and opportunities

Multiscale coupling is ubiquitous in nature and attracts broad interests of scientists from mathematicians, physicists, machinists, chemists to biologists. However, much less attention has been paid to its intrinsic implication. In this paper, multiscale coupling is introduced by studying two typical examples in classic mechanics: fluid turbulence and solid failure. The nature of multiscale coupling in the two examples lies in their physical diversities and strong coupling over wide-range scales. The theories of dynamical system and statistical mechanics provide fundamental methods for the multiscale coupling problems. The diverse multiscale couplings call for unified approaches and might expedite new concepts, theories and disciplines.     

A GENERALLY APPLICABLE METHOD OF STATISTIC ANALYSIS FOR STUDYING AND CLASSIFYING NERVE TYPES OF CHILDREN,JUVENILES AND YOUTHS

In this paper, a method of statistic analysis is suggested for studying and classifying nerve types of children, juveniles and youths. First, a set of effective calculating formulae is established so as to describe nerve types quantitatively. Thus, our analysis of nerve types is based on the experimental data. Since these data can not be considered as normally distributed, even the sampling is sized greatly. This has caused certain difficulties in the data analysis. The Box-Cox Transformation is proposed for the sampling data in this paper. As a result, direr tronsformation all the sets of experimental data can successfully pass Kolmogorov‘s no rmallsation test. And then, the suitable representative points in normal distribution are sleeted as classifying standards. Once the dividing points for classification are set up, the problem of classifying nerve types is solved successfully.     

Study and Implementation of a New SQL-Based ETL Approach

This paper analyzes the main characteristics, benefits, and disadvantages of existing traditional ETL （extraction, transformation, loading） methods, and summaries some factors affecting the performance of ETL tools. Then, a new ETL approach, E-LT （extraction, loading and transformation）, is proposed. The E-LT approach applies database mapping technique to realize that loading stage and transformation stage in the ETL process are performed at the same time after the extraction stage. Thus, it can use SQL commands to complete loading and transformation processing, and eliminates the staging area before loading in traditional ETL process. The framework of an ETL engine based on E-LT method is presented. The ETL process including initial loading and incremental refreshment is discussed in detail, and the SQL-based algorithm for initial loading is presented. The performance of E-LT method on loading throughout outperforms some commercial ETL approaches by experimental proof and theoretical analysis. At last, a real case in marine data warehousing of the E-LT method is discussed for illustrating the validity of the proposed method.     

Missing Data Imputation for Traffic Flow Based on Improved Local Least Squares

Complete and reliable field traffic data is vital for the planning, design, and operation of urban traffic management systems. However, traffic data is often very incomplete in many traffic information systems, which hinders effective use of the data. Methods are needed for imputing missing traffic data to minimize the effect of incomplete data on the utilization. This paper presents an improved Local Least Squares (LLS) approach to impute the incomplete data. The LLS is an improved version of the K Nearest Neighbor (KNN) method. First, the missing traffic data is replaced by a row average of the known values. Then, the vector angle and Euclidean distance are used to select the nearest neighbors. Finally, a regression step is used to get weights of the nearest neighbors and the imputation results. Traffic flow volume collected in Beijing was analyzed to compare this approach with the Bayesian Principle Component Analysis (BPCA) imputation approach. Tests show that this approach provides slightly better performance than BPCA imputation to impute missing traffic data.     

Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China

Observations of atmospheric carbon dioxide (CO2 ) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO2 can be analyzed and modeled by geostatistical methods, and CO2 values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO2 , and does not consider temporal variation in the satellite-observed CO2 data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO2 in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO2 data sets in China. Prediction of monthly CO2 using the spatiotemporal variogram model and spacetime kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient (r2 ), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO2 than those from the spatial-only approach.     

Fourier analysis for discontinuous Galerkin and related methods

In this paper we review a series of recent work on using a Fourier analysis technique to study the sta- bility and error estimates for the discontinuous Galerkin method and other related schemes. The advantage of this approach is that it can reveal instability of certain ＂bad＂＇ schemes; it can verify stability for certain good schemes which are not easily amendable to standard finite element stability analysis techniques; it can provide quantitative error comparisons among different schemes; and it can be used to study superconvergence and time evolution of errors for the discontinuous Galerkin method. We will briefly describe this Fourier analysis technique, summarize its usage in stability and error estimates for various schemes, and indicate the advantages and disadvantages of this technique in comparison with other finite element techniques.     

Recognizing Hierarchically Related Biomedical Entities Using MeSH-Based Mapping

Identifying hierarchically related entities is a critical step towards constructing bio-networks in the field of biomedical text mining. To this end, we adopt a mapping-based approach by first mapping bio-entities to terms in an established ontology Medical Subject Headings (MeSH). We then utilize the hierarchical relationships available in MeSH to recognize hierarchically related entities. Specifically, we present two approaches to map biomedical entities identified using the Unified Medical Language System (UMLS) Metathesaurus to MeSH terms. The first approach utilizes a special feature provided by the MetaMap algorithm, whereas the other employs approximate phrase-based match to directly map entities to MeSH terms. These two approaches deliver comparable results with an accuracy of 72% and 75%, respectively, based on two evaluation datasets. A thorough error analysis demonstrates that these two approaches result in only around 10% mutual errors, indicating the complementary nature of these two approaches.     

Load Profiling and Its Application to Demand Response: A Review

The smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure(AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy,researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art,comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentivebased, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.     

Fusing PLSA model and Markov random fields for automatic image annotation

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis （PLSA） model and multiple Markov random fields （MRF）.A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image.The novelty of our method mainly lies in two aspects：exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation.To demonstrate the effectiveness of this approach,an experiment on the Corel5k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.     

Support vector machine ensemble using rough sets theory

A support vector machine （SVM） ensemble classifier is proposed. Performance of SVM trained in an input space eonsisting of all the information from many sources is not always good. The strategy that the original input space is partitioned into several input subspaces usually works for improving the performance. Different from conventional partition methods, the partition method used in this paper, rough sets theory based attribute reduction, allows the input subspaces partially overlapped. These input subspaces can offer complementary information about hidden data patterns. In every subspace, an SVM sub-classifier is learned. With the information fusion techniques, those SVM sub-classifiers with better performance are selected and combined to construct an SVM ensemble. The proposed method is applied to decision-making of medical diagnosis. Comparison of performance between our method and several other popular ensemble methods is done. Experimental results demonstrate that our proposed approach can make full use of the information contained in data and improve the decision-making performance.