陶瓷坯体增强剂聚丙烯酸钠在艺术瓷成形过程的应用

本文合成了一系列不同相对分子质量的聚丙烯酸钠,应用于艺术瓷的坯体中,研究了聚丙烯酸钠对艺术瓷坯体强度、变形率的影响,并对聚丙烯酸钠增强机理进行了探讨。结果表明:聚丙烯酸钠可有效地提高艺术瓷坯体的成形强度,减少了坯体的变形率,当增强剂c加入的质量分数为0.6%时,坯体的干燥强度增加率达167.5%,坯体的变形率从50%降低到10%。     

某坝肩边坡开挖优化设计方案研究

为了研究前坪水库右坝肩边坡在设计方案和优化设计方案开挖下的边坡稳定性分析,采用数值分析和极限平衡法进行研究,首先针对边坡典型剖面进行了数值分析,研究边坡的位移和应力分布规律,然后运用极限平衡法得出边坡的安全系数,两种方法结合不仅可以反映边坡的稳定性和位移场之间的关系,而且可以用工程界所熟悉的安全系数来评价边坡的稳定性。结果表明边坡开挖优化设计方案满足边坡稳定性规范要求,且优化方案减少了开挖量,节省了工程投资,使得工程尽早发挥效益。     

State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements

This paper is concerned with the state estimation problem for the complex networked systems with randomly occurring nonlinearities and randomly missing measurements. The nonlinearities are included to describe the phenomena of nonlinear disturbances which exist in the network and may occur in a probabilistic way. Considering the fact that probabilistic data missing may occur in the process of information transmission, we introduce the randomly data missing into the sensor measurements. The aim of this paper is to design a state estimator to estimate the true states of the considered complex network through the available output measurements. By using a Lyapunov functional and some stochastic analysis techniques, sufficient criteria are obtained in the form of linear matrix inequalities under which the estimation error dynamics is globally asymptotically stable in the mean square. Furthermore, the state estimator gain is also obtained. Finally, a numerical example is employed to illustrate the effectiveness of the proposed state estimation conditions.     

Robust consensus algorithm for multi-agent systems with exogenous disturbances under convergence conditions

In this paper, a robust consensus algorithm is developed and sufficient conditions for convergence to consensus are proposed for a multi-agent system (MAS) with exogenous disturbances subject to partial information. By utilizing H_∞ robust control, differential game theory and a design-based approach, the consensus problem of the MAS with exogenous bounded interference is resolved and the disturbances are restrained, simultaneously. Attention is focused on designing an H_∞ robust controller (the robust consensus algorithm) based on minimisation of our proposed rational and individual cost functions according to goals of the MAS. Furthermore, sufficient conditions for convergence of the robust consensus algorithm are given. An example is employed to demonstrate that our results are effective and more capable to restrain exogenous disturbances than the existing literature.     

3D soft-tissue tracking using spatial-color joint probability distribution and thin-plate spline model

Visual tracking techniques based on stereo endoscope are developed to measure tissue motion in robot-assisted minimally invasive surgery. However, accurate 3D tracking of tissue surfaces remains challenging due to complicated deformation, poor imaging conditions, specular reflections and other dynamic effects during surgery. This study employs a robust and efficient 3D tracking scheme with two independent recursive processes, namely kernel-based inter-frame motion estimation and model-based intra-frame 3D matching. In the first process, target region is represented in joint spatial-color space for robust estimation. By defining a probabilistic similarity measure, a mean-shift-based iterative algorithm is derived for location of the target region in a new image. In the second process, the thin-plate spline model is used to fit the 3D shape of tissue surfaces around the target region. An iterative algorithm based on an efficient second-order minimization technique is derived to compute optimal model parameters. The two processes can be computed in parallel. Their outputs are combined to recover 3D information about the target region. The performance of the proposed method is validated using phantom heart videos and in vivo videos acquired by the daVinci^®${\mathrm{daVinci}}^{\circledR }$ surgical robotic platform and a synthesized data set with known ground truth.     

Fisher discrimination based low rank matrix recovery for face recognition

In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results.     

Estimation bias of ocean current measured by along-track interferometric synthetic aperture radar and its compensation methods

In previous works on current measured by along-track interferometric synthetic aperture radar (ATISAR), the decorrelation function of an ocean-surface backscattering signal was usually assumed to be a real Gaussian function, i.e. the phase term was omitted. In this study, it is proved that the omission of the phase term included in the decorrelation function results in a significant estimation bias that can be modelled by a higher-order function of time lag and that the coefficients of this higher-order function can be expressed as a series of higher-order Doppler spectral moments. This model is validated by the scatterometer data obtained from an experimental wind-wave tank. The estimation bias especially needs to be considered for the ATISAR system with a long time lag. Simulation results show that if the time lag is equal to the coherence time, the estimation bias of the current can reach about 0.2 m s^?1, which is not insignificant in high-precision current-retrieving applications. However, because most real-life ATISAR systems, including TerraSAR-X, operate with time lags significantly shorter than the expected coherence time, the estimation biases in these systems are relatively small or even negligible. Finally, four possible compensation methods for the estimation bias are proposed and discussed.     

Recent climate variability and its impact on precipitation,temperature, and vegetation dynamics in the Lancang River headwater area of China

The alpine ecosystem is one of the most fragile ecosystems threatened by global climate change. The impact of climate variability on the vegetation dynamics of alpine ecosystems has become important in global change studies. In this study, spatially explicit gridded data, including the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) product (MOD11A1/A2), the Tropical Rainfall Measuring Mission (TRMM) rainfall product (3B43), and MODIS net primary productivity (NPP) product (MOD17A3), together with meteorological observation data, were used to explore the spatio-temporal pattern of climate variability and its impact on vegetation dynamics from 2000 to 2012 in the Lancang River headwater area. We found that the variation patterns of LST, precipitation, and NPP in the study area showed remarkable spatial differences. From the northwest to the southeast the spatial variation of average annual LST exhibited a decreasing–increasing–decreasing–increasing pattern. At the same time, most of the study area exhibited an increasing LST during the growing season. The annual precipitation increased in the semi-arid northern part, whereas it decreased in the semi-humid southern part. The precipitation variability during the growing season has a pattern similar to the annual precipitation variability. Although the majority of the regions have seen an NPP increase from 2000 to 2012, the responses of the vegetation to the varied climate factors were spatially heterogeneous. The alpine–subalpine meadows in the high-altitude areas were more sensitive to climate variability in the growing season. It is argued that satellite remote-sensing products have great potential in investigating the impact of climate variability on vegetation dynamics at the finer scale, especially for the Lancang River headwater area with complex surface heterogeneity.     

3D Shape Segmentation and Labeling via Extreme Learning Machine

We propose a fast method for 3D shape segmentation and labeling via Extreme Learning Machine (ELM). Given a set of example shapes with labeled segmentation, we train an ELM classifier and use it to produce initial segmentation for test shapes. Based on the initial segmentation, we compute the final smooth segmentation through a graph‐cut optimization constrained by the super‐face boundaries obtained by over‐segmentation and the active contours computed from ELM segmentation. Experimental results show that our method achieves comparable results against the state‐of‐the‐arts, but reduces the training time by approximately two orders of magnitude, both for face‐level and super‐face‐level, making it scale well for large datasets. Based on such notable improvement, we demonstrate the application of our method for fast online sequential learning for 3D shape segmentation at face level, as well as realtime sequential learning at super‐face level.