Fast identification algorithms for Gaussian process model

A class identification algorithms is introduced for Gaussian process(GP)models.The fundamental approach is to propose a new kernel function which leads to a covariance matrix with low rank,a property that is consequently exploited for computational efficiency for both model parameter estimation and model predictions.The objective of either maximizing the marginal likelihood or the Kullback–Leibler (K–L) divergence between the estimated output probability density function(pdf)and the true pdf has been used as respective cost functions.For each cost function,an efficient coordinate descent algorithm is proposed to estimate the kernel parameters using a one dimensional derivative free search, and noise variance using a fast gradient descent algorithm. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

Construction of neurofuzzy models for imbalanced data classification

We propose a new class of neurofuzzy construction algorithms with the aim of maximizing generalization capability specifically for imbalanced data classification problems based on leave-one-out (LOO) cross validation. The algorithms are in two stages, first an initial rule base is constructed based on estimating the Gaussian mixture model with analysis of variance decomposition from input data; the second stage carries out the joint weighted least squares parameter estimation and rule selection using orthogonal forward subspace selection (OFSS)procedure. We show how different LOO based rule selection criteria can be incorporated with OFSS, and advocate either maximizing the leave-one-out area under curve of the receiver operating characteristics, or maximizing the leave-one-out Fmeasure if the data sets exhibit imbalanced class distribution. Extensive comparative simulations illustrate the effectiveness of the proposed algorithms.

PDFOS: PDF estimation based over-sampling for imbalanced two-class problems

This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (RBF) classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier’s structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.

Influence of UV radiation on chlorophyll, and antioxidant enzymes of wetland plants in different types of constructed wetland

A surface- and vertical subsurface-flow-constructed wetland were designed to study the response of chlorophyll and antioxidant enzymes to elevated UV radiation in three types of wetland plants (Canna indica, Phragmites austrail, and Typha augustifolia). Results showed that (1) chlorophyll content of C. indica, P. austrail, and T. augustifolia in the constructed wetland was significantly lower where UV radiation was increased by 10 and 20 % above ambient solar level than in treatment with ambient solar UV radiation (p < 0.05). (2) The malondialdehyde (MDA) content, guaiacol peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) activities of wetland plants increased with elevated UV radiation intensity. (3) The increased rate of MDA, SOD, POD, and CAT activities of C. indica, P. australis, and T. angustifolia by elevated UV radiation of 10 % was higher in vertical subsurface-flow-constructed wetland than in surface-flow-constructed wetland. The sensitivity of MDA, SOD, POD, and CAT activities of C. indica, P. austrail, and T. augustifolia to the elevated UV radiation was lower in surface-flow-constructed wetland than in the vertical subsurface-flow-constructed wetland, which was related to a reduction in UV radiation intensity through the dissolved organic carbon and suspended matter in the water. C. indica had the highest SOD and POD activities, which implied it is more sensitive to enhanced UV radiation. Therefore, different wetland plants had different antioxidant enzymes by elevated UV radiation, which were more sensitive in vertical subsurface-flow-constructed wetland than in surface-flow-constructed wetland.

Incorporating semiotics into fuzzy logic to enhance Clinical Decision Support Systems

In order to enhance the quality of care, healthcare organisations are increasingly resorting to clinical decision support systems (CDSSs), which provide physicians with appropriate health care decisions or recommendations. However, how to explicitly represent the diverse vague medical knowledge and effectively reason in the decision-making process are still problems we are confronted. In this paper, we incorporate semiotics into fuzzy logic to enhance CDSSs with the aim of providing both the abilities of describing medical domain concepts contextually and reasoning with vague knowledge. A semiotically inspired fuzzy CDSSs framework is presented, based on which the vague knowledge representation and reasoning process are demonstrated.

An ontological approach to personalized medical knowledge recommendation

Knowledge recommendation has become a promising method in supporting the clinicians decisions and improving the quality of medical services in the constantly changing clinical environment. However, current medical knowledge management systems cannot understand users requirements accurately and realize personalized recommendation. Therefore this paper proposes an ontological approach based on semiotic principles to personalized medical knowledge recommendations. In particular, healthcare domain knowledge is conceptualized and an ontology-based user profile is built. Furthermore, the personalized recommendation mechanism is illustrated.

An ontological approach to personalized medical knowledge matching

Knowledge management has become a promising method in supporting the clinicians′ decisions and improving the quality of medical services in the constantly changing clinical environment. However, current medical knowledge management systems cannot understand users′ requirements accurately and realize personalized matching. Therefore this paper proposes an ontological approach based on semiotic principles to personalized medical knowledge matching. In particular, healthcare domain knowledge is conceptualized and an ontology-based user profile is built. Furthmore, the personalized matching mechanism and algorithm are illustrated.

Hierarchical clustering based web service discovery

This paper presents a hierarchical clustering method for semantic Web service discovery. This method aims to improve the accuracy and efficiency of the traditional service discovery using vector space model. The Web service is converted into a standard vector format through the Web service description document. With the help of WordNet, a semantic analysis is conducted to reduce the dimension of the term vector and to make semantic expansion to meet the user’s service request. The process and algorithm of hierarchical clustering based semantic Web service discovery is discussed. Validation is carried out on the dataset.

Analysis of functional networks involved in motor execution and motor imagery using combined hierarchical clustering analysis and independent component analysis

Cognitive experiments involving motor execution (ME) and motor imagery (MI) have been intensively studied using functional magnetic resonance imaging (fMRI). However, the functional networks of a multitask paradigm which include ME and MI were not widely explored. In this article, we aimed to investigate the functional networks involved in MI and ME using a method combining the hierarchical clustering analysis (HCA) and the independent component analysis (ICA). Ten right-handed subjects were recruited to participate a multitask experiment with conditions such as visual cue, MI, ME and rest. The results showed that four activation clusters were found including parts of the visual network, ME network, the MI network and parts of the resting state network. Furthermore, the integration among these functional networks was also revealed. The findings further demonstrated that the combined HCA with ICA approach was an effective method to analyze the fMRI data of multitasks.

Urban microclimates and urban heat island in Chongqing, China

Chongqing is the largest directly-controlled municipality in China, which is now undergoing a rapid urbanization. The urbanization rate increased from 35.6% in 2000 to 48.3% in 2007, and it is estimated to reach at least 70% by 2020. The question remains open: What are the consequences of such rapid urbanization in Chongqing in terms of urban microclimate? Furthermore, Chongqing is located within the Three Gorges Reservoir (TGR) region and the upper Yangtze River, where the Three Gorges Reservoir (TGR) project started in 1993 and was completed in 2010. As one of the biggest construction projects in the world with a rising water level of 175m and water storage capacity of about 39.3 billion m3, it would be interesting to investigate how such a gigantic project impacts the surrounding micro-environment, especially in Chongqing. Different research approaches are adopted in the study. Our literature review indicates present studies on the urban climate in Chongqing are mainly confined within the historical trend analysis of several weather stations operated by the Chongqing government, little is known about the spatial distribution of urban air temperature and how the local land cover influences the air temperature, especially when there are rivers running through the Chongqing urban area. To contribute to the present knowledge, a series of field measurement campaigns and numerical simulations were carried out. Two complementary types of field measurements are included: fixed weather stations and mobile transverse measurement. Numerical simulations using a house-developed program are able to predict the urban air temperature in Chongqing.

Squeeze flow viscoelasticity of electrorheological fluids based on microcrystalline cellulose

Dynamic viscoelasticity of electrorheological fluids based on microcrystalline cellulose/castor oil suspensions was experimentally investigated in squeeze flow. The dependence of storage modulus G' and loss modulus G" parallel to external electric field on electric fields and strain amplitudes is presented. The experiments show that, when external electric field is higher than the critical field, the viscoelasticity of the ER fluids converts from linear to nonlinear, and the ER fluids transfer from solid-like state to fluid state with the growth of strain amplitude. The influences of strain amplitude and oscillatory frequency on the nonlinearity of viscoelasticity were also studied.

Nonlinear identification using orthogonal forward regression with nested optimal regularization

An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal forward regression (OFR) procedure. Thus, each OFR step consists of one model term selection based on the LOO mean square error (LOOMSE), followed by the optimization of the associated kernel width and regularization parameter, also based on the LOOMSE. Since like our previous state-of-the-art local regularization assisted orthogonal least squares (LROLS) algorithm, the same LOOMSE is adopted for model selection, our proposed new OFR algorithm is also capable of producing a very sparse RBF model with excellent generalization performance. Unlike our previous LROLS algorithm which requires an additional iterative loop to optimize the regularization parameters as well as an additional procedure to optimize the kernel width, the proposed new OFR algorithm optimizes both the kernel widths and regularization parameters within the single OFR procedure, and consequently the required computational complexity is dramatically reduced. Nonlinear system identification examples are included to demonstrate the effectiveness of this new approach in comparison to the well-known approaches of support vector machine and least absolute shrinkage and selection operator as well as the LROLS algorithm.

Sparse density estimation on the multinomial manifold

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators.

Estimation of Gaussian process regression model using probability distance measures

A new class of parameter estimation algorithms is introduced for Gaussian process regression (GPR) models. It is shown that the integration of the GPR model with probability distance measures of (i) the integrated square error and (ii) Kullback–Leibler (K–L) divergence are analytically tractable. An efficient coordinate descent algorithm is proposed to iteratively estimate the kernel width using golden section search which includes a fast gradient descent algorithm as an inner loop to estimate the noise variance. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

Dimensionality reduction assisted tensor clustering

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.