直线网格B样条混合滤波GPU光线投射

为了快速、高质量地绘制直线网格,提出B样条混合滤波方法,实现加速网格,并将其应用到直线网格GPU光线投射.证明了三次B样条基函数导数的符号性质,进而证明用快速三次滤波方法(S& H方法)计算非均匀B样条函数的导数会出现误差.据此,在光线积分计算中,如果条件允许,采用S & H方法;否则采用基于B样条基本公式的滤波方法.另外,证明三次B样条函数导数的范围,以实现梯度量调制和加速网格;在光线积分计算中,利用梯度量调制表现物质的分界面;利用加速网格,跳过无效积分步,加快绘制速度.实验结果表明,采用混合滤波的直线网格GPU光线投射方法能消除S&H方法导致的走样现象;与基于B样条基本公式的绘制方法相比,该方法更快;如果模拟或测量的物体本身是光滑的,该方法能快速反映其真实特征.     

How the Kano model contributes to Kansei engineering in services

Recent studies show that products and services hold great appeal if they are attractively designed to elicit emotional feelings from customers. Kansei engineering (KE) has good potential to provide a competitive advantage to those able to read and translate customer affect and emotion in actual product and services. This study introduces an integrative framework of the Kano model and KE, applied to services. The Kano model was used and inserted into KE to exhibit the relationship between service attribute performance and customer emotional response. Essentially, the Kano model categorises service attribute quality into three major groups (must-be [M], one-dimensional [O] and attractive [A]). The findings of a case study that involved 100 tourists who stayed in luxury 4- and 5-star hotels are presented. As a practical matter, this research provides insight on which service attributes deserve more attention with regard to their significant impact on customer emotional needs. STATEMENT OF RELEVANCE: Apart from cognitive evaluation, emotions and hedonism play a big role in service encounters. Through a focus on delighting qualities of service attributes, this research enables service providers and managers to establish the extent to which they prioritise their improvement efforts and to always satisfy their customer emotions beyond expectation.     

A novel multiset integrated canonical correlation analysis framework and its application in feature fusion

Multiset canonical correlation analysis (MCCA) is difficult to effectively express the integrated correlation among multiple feature vectors in feature fusion. Thus, this paper firstly presents a novel multiset integrated canonical correlation analysis (MICCA) framework. The MICCA establishes a discriminant correlation criterion function of multi-group variables based on generalized correlation coefficient. The criterion function can clearly depict the integrated correlation among multiple feature vectors. Then the paper presents a multiple feature fusion theory and algorithm using the MICCA method. The detailed process of the algorithm is as follows: firstly, extract multiple feature vectors from the same patterns by using different feature extraction methods; then extract multiset integrated canonical correlation features using MICCA; finally form effective discriminant feature vectors through two given feature fusion strategies for pattern classification. The multi-group feature fusion method based on MICCA not only achieves the aim of feature fusion, but also removes the redundancy between features. The experiment results on CENPARMI handwritten Arabic numerals and UCI multiple features database show that the MICCA method has better recognition rates and robustness than the fusion methods based on canonical correlation analysis (CCA) and MCCA.     

Online scheduling on unbounded parallel-batch machines to minimize maximum flow-time

We consider online scheduling on m unbounded parallel-batch machines to minimize maximum flow-time of the jobs. We show that no online algorithm can have a competitive ratio less than 1+αm, where αm is the positive root of α²+(m+1)α−1=0, and this lower bound is still valid even when all jobs have the same processing times. Then we provide an online algorithm of competitive ratio 1+1/m. When the jobs have the same processing times, we present a best possible online algorithm of competitive ratio 1+αm.     

Optimization for limited angle tomography in medical image processing

This paper aims to reduce the problems of incomplete data in computed tomography, which happens frequently in medical image process and analysis, e.g., when the high-density region of objects can only be penetrated by X-rays at a limited angular range. As the projection data are available only in an angular range, the incomplete data problem can be attributed to the limited angle problem, which is an ill-posed inverse problem. Image reconstruction based on total variation (TV) reduces the problem and gives better performance on edge-preserving reconstruction; however, the artificial parameter can only be determined through considerable experimentation. In this paper, an effective TV objective function is proposed to reduce the inverse problem in the limited angle tomography. This novel objective function provides a robust and effective reconstruction without any artificial parameter in the iterative processes, using the TV as a multiplicative constraint. The results demonstrate that this reconstruction strategy outperforms some previous ones.     

Parallelization methods for implementation of discharge simulation along resin insulator surfaces

In this paper, we will investigate the implementation of the parallelization approaches used in the program of discharge simulation along resin insulator surfaces in SF₆/N₂ gas mixture which initially consumes a great deal of computational time. In a general way, this simulation program spent 10 days of execution to achieve satisfactory research results. For this reason, the goal of our paper is to reduce the execution time by parallelizing this program. Three parallelization approaches were used in our simulation: (i) splitting by different types of the charged particles using a distributed-memory approach, (ii) splitting by physical domain using a distributed-memory approach, and (iii) splitting by both domain and charged particles using multi-level distributed and shared memory approach. At last, the three approaches are tested on a Linux cluster composed of six dual-core PCs, and the experimental results show that all the parallelization approaches achieve the goal of reducing the execution time to a certain extent. In addition, among these approaches, the multi-level approach offers the most effective parallelization method for implementing this simulation on symmetrical multi-processing (SMP) clusters.     

Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

Land surface and climate modelling requires continuous and consistent Leaf Area Index (LAI). High spatiotemporal resolution and long-time record data are more in demand nowadays and will continue to be in the future. MODIS LAI products meet these requirements to some degree. However, due to the presence of cloud and seasonal snow cover, the instrument problems and the uncertainties of retrieval algorithm, the current MODIS LAI products are spatially and temporally discontinuous and inconsistent, which limits their application in land surface and climate modelling. To improve the MODIS LAI products on a global scale, we considered the characteristics of the MODIS LAI data and made the best use of quality control (QC) information, and developed an integrated two-step method to derive the improved MODIS LAI products effectively and efficiently on a global scale. First, we used the modified temporal spatial filter (mTSF) method taking advantage of background values and QC information at each pixel to do a simple data assimilation for relatively low quality data. Then we applied the post processing-TIMESAT (A software package to analyze time-series of satellite sensor data) Savitzky-Golay (SG) filter to get the final result. We implemented the method to 10 years of the MODIS Collection 5 LAI data. In comparison with the LAI reference maps and the MODIS LAI data, our results showed that the improved MODIS LAI data are closer to the LAI reference maps in magnitude and also more continuous and consistent in both time-series and spatial domains. In addition, simple statistics were used to evaluate the differences between the MODIS LAI and the improved MODIS LAI.     

A novel linearly-weighted gradient smoothing method (LWGSM) in the simulation of fluid dynamics problem

This paper describes a novel linearly-weighted gradient smoothing method (LWGSM). The proposed method is based on irregular cells and thus can be used for problems with arbitrarily complex geometrical boundaries. Based on the analysis about the compactness and the positivity of coefficients of influence of their stencils for approximating a derivative, one favorable scheme (VIII) is selected among total eight proposed discretization schemes. This scheme VIII is successively verified and carefully examined in solving Poisson equations, subjected to changes in the number of nodes, the shapes of cells and the irregularity of triangular cells, respectively. Strong form of incompressible Navier–Stokes equations enhanced with artificial compressibility terms are tackled, in which the spatial derivatives are approximated by consistent and successive use of gradient smoothing operation over smoothing domains at various locations. All the test cases using LWGSM solver exhibits its robust, stable and accurate behaviors. The attained incompressible LWGSM solutions show good agreements with experimental and literature data. Therefore, the proposed LWGSM can be reliably used for accurate solutions to versatile fluid flow problems.     

一种基于ARM-Linux的分布式管道泄漏监测系统

针对中国输油管道现场具体情况,设计一套基于嵌入式ARM-Linux和LabVIEW的分布式管道泄漏监测系统,包括远程测控终端(RTU)、监测PC以及工业局域网.详细论述了系统架构、硬件组成、软件流程等.通过实际开发与应用,证明此设计完全适用于输油管道特殊现场,不仅降低了成本与功耗,还提高了效率,为长距离石油管道运输系统...