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Online quantitative analysis of reaction gases or exhaust in industrial production is of great significance to improve the production capacity and process. A novel method is developed for the online quantitative analysis of reaction gases or exhaust using quantitative mathematical models combined with the linear regression algorithm of machine learning. After accurately estimating the component gases and their contents in the reaction gases or exhaust, a ratio matrix is constructed to separate the relevant overlapping peaks. The ratio and calibration standard gases are detected, filtered, normalized, and linearly regressed with an online process mass spectrometer to correct the ratio matrices and obtain the relative sensitivity matrices. A quantitative mathematical model can be established to obtain the content of each component of the reaction gases or exhaust in real time. The maximum quantification error and relative standard deviation of the method are within 0.3% and 1%, after online quantification of the representative yeast fermenter tail gas. [ABSTRACT FROM AUTHOR]

The availability of many heterogeneous but related views of data has arisen in numerous clustering problems. Different views encode distinct representations of the same data, which often admit the same underlying cluster structure. The goal of multi-view clustering is to properly combine information from multiple views so as to generate high quality clustering results that are consistent across different views. Based on max-product belief propagation, we propose a novel multi-view clustering algorithm termed multi-view affinity propagation (MVAP). The basic idea is to establish a multi-view clustering model consisting of two components, which measure the within-view clustering quality and the explicit clustering consistency across different views, respectively. Solving this model is NP-hard, and a multi-view affinity propagation is proposed, which works by passing messages both within individual views and across different views. However, the exemplar consistency constraint makes the optimization almost impossible. To this end, by using some previously designed mathematical techniques, the messages as well as the cluster assignment vector computations are simplified to get simple yet functionally equivalent computations. Experimental results on several real-world multi-view datasets show that MVAP outperforms existing multi-view clustering algorithms. It is especially suitable for clustering more than two views. [ABSTRACT FROM PUBLISHER]

Abstract: Image set matching attracted increasing attention in the field of pattern recognition. Recently, there are a number of effective image set-based matching methods under controlled environment. However in the more complex environment, like multi-view and illumination changed, it is still a challenging problem to develop unsupervised image set matching method to handle multi-local model data. To solve this problem, in this paper, we present a novel multi-local model image set matching method based on data description techniques. First, every image set is divided into multi-local models, and each local model corresponds to a data domain, that is, we innovatively train a support vector data domain to describe each local model by means of the excellent data description ability of support vector data domain, hence each image set can be expressed by a plurality of support vector data domain. Second, a new similarity measure based on domain–domain distance is proposed, and then the distance between two image sets is converted to integrate the distance between pair-wise domains. Finally, the proposed method is evaluated on both set-based face recognition and object classification tasks. Extensive experimental results show that the proposed method outperforms other state of the art set-based matching methods in three public video databases. [Copyright &y& Elsevier]

We present a stable finite difference scheme on a piecewise uniform mesh along with a penalty method for pricing American put options under Kou's jump-diffusion model. By adding a penalty term, the partial integrodifferential complementarity problem arising from pricing American put options under Kou's jump-diffusion model is transformed into a nonlinear parabolic integrodifferential equation. Then a finite difference scheme is proposed to solve the penalized integrodifferential equation, which combines a central difference scheme on a piecewise uniform mesh with respect to the spatial variable with an implicit-explicit time stepping technique. This leads to the solution of problems with a tridiagonal M-matrix. It is proved that the difference scheme satisfies the early exercise constraint. Furthermore, it is proved that the scheme is oscillation-free and is second-order convergent with respect to the spatial variable. The numerical results support the theoretical results. [ABSTRACT FROM AUTHOR]

The need to identify the physical properties of a structure given its force-response (input-output) relationship is driven primarily by the need to validate the approximate solution models, such as finite-element models. This paper proposes a method, which combines the structural system identification and model updating techniques, for the damage detection of a steel frame structure and a RC frame. The damage detection procedure consists of two steps: (1) identifying the system dynamic characteristics using the subspace identification (SI) technique from input/output measurements and (2) developing a damage assessment method for structural members (including joints) based on a progressive finite-element model updating and a large-scale optimization using a nonlinear least-square technique. The proposed method was verified through a shaking table experimental study using: (1) a 1/4-scale six-story steel frame structure by loosening the connection bolts for damage simulations and (2) a two-story RC frame subject to different levels of ground excitations back to back. As demonstrated by experimental results, the proposed damage detection method, based on the combination of SI technique and the model updating approach, is very effective for the damage assessment of frame structures. The method not only can detect the damage locations but also can quantify the damage severities. [ABSTRACT FROM AUTHOR]

A basic question in analyzing cDNA microarray data is normalization, the purpose of which is to remove systematic bias in the observed expression values by establishing a normalization curve across the whole dynamic range. A proper normalization procedure ensures that the normalized intensity ratios provide meaningful measures of relative expression levels. We propose a two-way semilinear model (TW-SLM) for normalization and analysis of microarray data. This method does not make the usual assumptions underlying some of the existing methods. For example, it does not assume that the percentage of differentially expressed genes is small or that there is symmetry in the expression levels of up-regulated and down-regulated genes, as required in the lowess normalization method. The TW-SLM also naturally incorporates uncertainty due to normalization into significance analysis of microarrays. We use a semiparametric approach based on polynomial splines in the TW-SLM to estimate the normalization curves and the normalized expression values. We study the theoretical properties of the proposed estimator in the TW-SLM, including the finite-sample distributional properties of the estimated gene effects and the rate of convergence of the estimated normalization curves when the number of genes under study is large. We also conduct simulation studies to evaluate the TW-SLM method and illustrate the proposed method using a published microarray dataset. [ABSTRACT FROM AUTHOR]

Presents the authors' comments on the paper "Semilinear High-Dimensional Model for Normalization of Microarray Data: A Theoretical Analysis and Partial Consistency," by Jianqing Fan, Heng Peng and Tao Huang, published in the September 2005 issue of the "Journal of the American Statistical Association." Appreciation of the authors for their significant contribution to the semiparametric estimation theory; Suggestions for a two-way semilinear model for normalization and analysis of cDNA microarray data; Comments on multiway semilinear models; Description of models for location normalization; Opinion on the relationship between the relationship between the multiway semilinear models and the semilinear in-slide model.

Background: Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative expression values. Methods: We propose a robust semiparametric method in a two-way semi-linear model (TWSLM) for normalization of cDNA microarray data. This method does not make the usual assumptions underlying some of the existing methods. For example, it does not assume that: (i) the percentage of differentially expressed genes is small; or (ii) the numbers of up- and down-regulated genes are about the same, as required in the LOWESS normalization method. We conduct simulation studies to evaluate the proposed method and use a real data set from a specially designed microarray experiment to compare the performance of the proposed method with that of the LOWESS normalization approach. Results: The simulation results show that the proposed method performs better than the LOWESS normalization method in terms of mean square errors for estimated gene effects. The results of analysis of the real data set also show that the proposed method yields more consistent results between the direct and the indirect comparisons and also can detect more differentially expressed genes than the LOWESS method. Conclusions: Our simulation studies and the real data example indicate that the proposed robust TW-SLM method works at least as well as the LOWESS method and works better when the underlying assumptions for the LOWESS method are not satisfied. Therefore, it is a powerful alternative to the existing normalization methods. [ABSTRACT FROM AUTHOR]

Estimation of the proportional-odds failure-time regression model with interval censoring is considered. Conditions that allow for positive information for the regression parameter are discussed. The efficient score is characterized by a Fredholm equation of the second kind. The sieve maximum likelihood estimator for the finite-dimensional regression parameter is shown to be asymptotically normal with n convergence rate and to achieve the information bound. Data analysis and simulations assist in clarifying our thoughts regarding the choice of sieve for finite-sample problems. [ABSTRACT FROM AUTHOR]

A mathematical model was established to simulate the weld pool development and dynamic process in stationary laser–metal inert gas (MIG) hybrid welding. Surface tension and buoyancy were considered to calculate liquid metal flow pattern; moreover, typical phenomena of MIG welding such as filler droplets impinging weld pool, electromagnetic force in the weld pool, and typical phenomena of laser beam welding such as recoil pressure, inverse Bremsstrahlung absorption, and Fresnel absorption were all considered in the model. The laser beam and arc couple effect was introduced into this model by the plasma width during hybrid welding. Transient weld pool shape and complicated liquid metal velocity distribution from two kinds of weld pool to a unified weld pool were calculated. Furthermore, the simulated weld bead geometries were in good agreement with experimental measurement. [ABSTRACT FROM AUTHOR]

Background: With increasing acceptance of premarital sex among young Chinese women, the rates of unintended pregnancies and induced abortions are becoming alarmingly high, suggesting the needs of educating women with adequate contraceptive knowledge and providing them with accessible contraceptive services. Previous studies have shown that knowledge and attitudes towards contraception could be modified through intervention strategies. This study aimed to evaluate the effects of two community intervention models on modifying contraceptive attitudes and behaviors among nulliparous women. Methods: In this clustered randomized controlled trial, nulliparous women aged 18-40 years from 18 communities were enrolled and randomized to either the traditional community intervention model (TC model) or the more comprehensive new community-based intervention model (NC model) with a ratio of 1:2. Contraceptive attitudes and behaviors were assessed before and after the interventions. Results: A total of 901 nulliparous women were followed. The most common contraceptive method in both groups was condom (approximately 80%) before or after interventions. The rates of using effective contraceptive methods were very low (<5%) even after the intervention. Comparing the NC with TC group, the adjusted ORs and 95% CIs regarding natural family planning, emergency contraceptive usage and short-acting OCPs were1.53 (95% CI: 1.11-2.13), 2.87 (95% CI: 2.05-4.02), and 2.71 (95% CI: 1.65-4.47), respectively; while the ORs and 95%CIs of gynecological examination and contraceptive use were 2.31 (95% CI: 1.63-3.27) and 2.89 (95% CI: 1.98-4.23), respectively. No statistical significant difference was found for the use of effective contraceptive methods at post-intervention among the two models. Conclusions: High proportions of nulliparous women held negative attitudes and behaviors towards effective contraceptive methods. The NC model, integrating existing health resources, had more positive influence than the TC model on the favorable contraceptive attitudes and behaviors towards the use of any contraceptive methods, but had limited impact on the use of effective contraceptive methods. Our study suggested the feasibility of applying the NC model in Shanghai. Interventions on contraceptive attitudes and behaviors should base on the existing health service system, synthesize resources and selectively apply to populations with distinct characteristics. [ABSTRACT FROM AUTHOR]

Each Chinese character is comprised of radicals, where a single character (compound character) contains one (or more than one) radicals. For human cognitive perspective, a Chinese character can be recognized by identifying its radicals and their spatial relationship. This human cognitive law may be followed in computer recognition. However, extracting Chinese character radicals automatically by computer is still an unsolved problem. In this paper, we propose using an improved sparse matrix factorization which integrates affine transformation, namely affine sparse matrix factorization (ASMF), for automatically extracting radicals from Chinese characters. Here the affine transformation is vitally important because it can address the poor-alignment problem of characters that may be caused by internal diversity of radicals and image segmentation. Consequently we develop a radical-based Chinese character recognition model. Because the number of radicals is much less than the number of Chinese characters, the radical-based recognition performs a far smaller category classification than the whole character-based recognition, resulting in a more robust recognition system. The experiments on standard Chinese character datasets show that the proposed method gets higher recognition rates than related Chinese character recognition methods. [ABSTRACT FROM AUTHOR]