Mathematical model of deoxidization with post stirring in a combined blowing converter

Dissolved oxygen in the steel at the terminal of the converter smelting process is the main cause for the formation of oxide inclusions, and the high terminal oxygen content worsens the steel cleanness. However, post stirring in a combined blowing converter can promote the carbon-oxygen reaction in the liquid steel and reduce the dissolved oxygen content at the terminal of the converter smelting process. Thus, the mathematical model of deoxidization in the post stirring process was obtained, and the rationa...     

Evolution of insoluble eutectic Si particles in anodic oxidation films during adipic-sulfuric acid anodizing processes of ZL114A aluminum alloys

The effects of insoluble eutectic Si particles on the growth of anodic oxide films on ZL114A aluminum alloy substrates were in-vestigated by optical microscopy (OM) and scanning electron microscopy (SE...     

SiCw/Al复合材料力学性能分散性

金属基复合材料力学性能分散性影响其性能评价和广泛应用.本文研究了测量方法和组织缺陷对SiCw/Al复合材料力学性能统计分布规律的影响.结果表明,复合材料的弹性模量、强度和延伸率等性能均存在分散性,高性能数据出现频率最高,向低的性能值分散.弹性模量分散性受测量方法影响很大,直线段扩大法可以减小性能分散范围.强度和延伸率性能高的试样中发现夹铝区或未浸渗区等缺陷的概率远低于低性能试样,微观缺陷是造成强度和延伸率向低值方向分散的主要原因.为此减少组织缺陷并合理选择实验方法对复合材料的性能评价和应用是必要的.     

Camouflage texture evaluation using a saliency map

Multimedia Systems - Camouflage effect evaluation and examination is an important procedure in digital camouflage pattern design as it is helpful in improving the objectivity and effectiveness...     

Stabilization of One‐Dimensional Schrödinger Equation with Variable Coefficient under Delayed Boundary Output Feedback

This article considers stabilization of a one‐dimensional Schrödinger equation with variable coefficient and boundary observation which suffers from an arbitrary given time delay. We design an observer and predictor to stabilize the system. The state is estimated in the time span where the observation is available, and also predicted in the time interval where the observation is not available. It is shown that the estimated state feedback stabilizes the system exponentially. A numerical simulation is presented to illustrate the effect of the stabilizing controller.     

Stereo-Based Stochastic Mapping for Robust Speech Recognition

We present a stochastic mapping technique for robust speech recognition that uses stereo data. The idea is based on constructing a Gaussian mixture model for the joint distribution of the clean and noisy features and using this distribution to predict the clean speech during testing. The proposed mapping is called stereo-based stochastic mapping (SSM). Two different estimators are considered. One is iterative and is based on the maximum a posteriori (MAP) criterion while the other uses the minimum mean square error (MMSE) criterion. The resulting estimators are effectively a mixture of linear transforms weighted by component posteriors, and the parameters of the linear transformations are derived from the joint distribution. Compared to the uncompensated result, the proposed method results in 45% relative improvement in word error rate (WER) for digit recognition in the car. In the same setting, SSM outperforms SPLICE and gives similar results to MMSE compensation of Huang A 66% relative improvement in word error rate (WER) is observed when applied in conjunction with multistyle training (MST) for large vocabulary English speech recognition in a real environment. Also, the combination of the proposed mapping with CMLLR leads to about 38% relative improvement in performance compared to CMLLR alone for real field data.     

An adaptive interference alignment scheme based on the dynamic selection of desired transmitters for unmanned ship network

Wireless Networks - In the unmanned ship networking scenario, the position of the unmanned ship changes continuously, leading to the result that the desired transmitters and the interference...     

Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks

International Journal of Computer Vision - Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional...