煤矸石在高温材料中的研究进展

简要介绍了煤矸石的构成、来源及危害；论述了煤矸石在耐火材料中的可行性分析研究；详细阐述了煤矸石在耐火材料中的研究和应用现状，及其对未来的展望。     

Hydrothermal Synthesis and Characterization of KxNa(1−x)NbO3 Powders

NaNbO₃, KNbO₃, and K _x Na_{(1− x )}NbO₃ powders were successfully prepared by the hydrothermal method. The phase of the products was identified to be orthorhombic structure by X-ray diffraction (XRD) technique, and the XRD results revealed that the x value of the K _x Na_{(1− x )}NbO₃ gradually increased with the increase in the ratio of K⁺ to Na⁺ in alkaline solution. The morphology and the microstructure were investigated by scanning electron microscopy, energy-dispersive spectroscopy, and transmission electron microscopy, and the results indicated that the ratio of K⁺ to Na⁺ in the solution had a great effect on the morphology and the size of products. Na_0.5K_0.5NbO₃ with morphotropic phase boundary composition could be synthesized when the molar ratio of K⁺ to Na⁺ was between 4:1 and 6:1 in the solution. A possible formation mechanism of the K _x Na_{(1− x )}NbO₃ crystal was also proposed based on the experimental results.     

2—（2—氨基噻唑—4—基）—2—甲氧基亚氨基乙酸乙酯的合成

以乙酰乙酸乙酯为起始原料,经肟化、甲基化、溴化和缩合四步反应合成2-(2-氨基噻唑-4-基)-2-甲氧基亚氨基乙酸乙酯,总收率63.14％,高于文献报道。     

Dynamic deviation reduction‐based concurrent dual‐band digital predistortion

This article introduces the concurrent dual‐band digital predistortion (DPD) architecture with only one upconvertion unit, which is suitable for the linearization of wideband power amplifiers (PAs) excited by concurrent dual‐band signals. By extending the conventional dynamic deviation reduction (DDR) model to the concurrent dual‐band mode, we propose two DDR‐based concurrent dual‐band models, the dual‐band DDR (DB‐DDR) model and the simplified dual‐band DDR (SDB‐DDR) model. The performance of these two models is experimentally assessed with two types of wideband PAs (a GaN Class F PA and a GaN Doherty PA) driven by the concurrent dual‐band signal, and compared with the prior two‐dimensional digital predistortion (2D‐DPD) model and the two‐dimensional modified memory polynomial (2D‐MMP) model. The results prove the good DPD performance and low computational complexity of the proposed models. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:401–411, 2014.     

两段法处理高浓度含砷废水的工艺研究

通过正交试验研究了用石灰、有机捕集沉淀剂(DTCR)两段法处理高浓度含砷废水的工艺条件，并与常规法比较了处理效果和处理费用。结果表明：用两段法处理后的废水可达标排放；污泥量大大减少，易脱水，含水率低，降低了后处理的难度和费用；污泥稳定，不产生二次污染。该法具有显著的社会效益和生态效益。     

Block-mode discriminant analysis and its application to face and antenna signal recognition

In this paper we present a multi-linear algebra based approach for feature extraction and signal recognition. We call this method Block-Mode Discriminant Analysis (BMDA). Experimentations are carried out on the CMU-PIE benchmark database for the task of face recognition, and we further utilized our method for the application of the signal recognition of underground UWB dielectric resonator antennas. Experiments show that our proposed BMDA is more effective and fast than its predecessors.     

To accelerate shot boundary detection by reducing detection region and scope

Video Shot Boundary Detection (SBD) is the fundamental process towards video summarization and retrieval. A fast and efficient SBD algorithm is necessary for real-time video processing applications. Extensive work has focused on accurate shot boundary detection at the expense of demanding computational costs. In this paper, we propose a fast SBD approach that reduces the computation pixel-wise and frame-wise while still giving satisfactory accuracy. The proposed approach substantially speeds up the computation through reducing both detection region and scope. Color histogram and mutual information are used together to measure the difference between frames. Corner distribution of frames is utilized to exclude most of false boundaries. We conduct extensive experiments to evaluate the proposed approach, and the results show that our approach can not only speed up SBD, but also detect shot boundaries with high accuracy in both Cut (CUT) and Gradual Transition (GT) boundaries.     

Optimal entropy-constrained non-uniform scalar quantizer design for low bit-rate pixel domain DVC

In this paper, an optimal entropy-constrained non-uniform scalar quantizer is proposed for the pixel domain DVC. The uniform quantizer is efficient for the hybrid video coding since the residual signals conforming to a single-variance Laplacian distribution. However, the uniform quantizer is not optimal for pixel domain distributed video coding (DVC). This is because the uniform quantizer is not adaptive to the joint distribution of the source and the SI, especially for low level quantization. The signal distribution of pixel domain DVC conforms to the mixture model with multi-variance. The optimal non-uniform quantizer is designed according to the joint distribution, the error between the source and the SI can be decreased. As a result, the bit rate can be saved and the video quality won’t sacrifice too much. Accordingly, a better R-D trade-off can be achieved. First, the quantization level is fixed and the optimal RD trade-off is achieved by using a Lagrangian function J(Q). The rate and distortion components is designed based on P(Y|Q). The conditional probability density function of SI Y depend on quantization partitions Q, P(Y|Q), is approximated by a Guassian mixture model at encocder. Since the SI can not be accessed at encoder, an estimation of P(Y|Q) based on the distribution of the source is proposed. Next, J(Q) is optimized by an iterative Lloyd-Max algorithm with a novel quantization partition updating algorithm. To guarantee the convergence of J(Q), the monotonicity of the interval in which the endpoints of the quantizer lie must be satisfied. Then, a quantizer partition updating algorithm which considers the extreme points of the histogram of the source is proposed. Consequently, the entropy-constrained optimal non-uniform quantization partitions are derived and a better RD trade-off is achieved by applying them. Experiment results show that the proposed scheme can improve the performance by 0.5 dB averagely compared to the uniform scalar quantization.     

A comparative study of iterative and non-iterative feature selection techniques for software defect prediction

Two important problems which can affect the performance of classification models are high-dimensionality (an overabundance of independent features in the dataset) and imbalanced data (a skewed class distribution which creates at least one class with many fewer instances than other classes). To resolve these problems concurrently, we propose an iterative feature selection approach, which repeated applies data sampling (in order to address class imbalance) followed by feature selection (in order to address high-dimensionality), and finally we perform an aggregation step which combines the ranked feature lists from the separate iterations of sampling. This approach is designed to find a ranked feature list which is particularly effective on the more balanced dataset resulting from sampling while minimizing the risk of losing data through the sampling step and missing important features. To demonstrate this technique, we employ 18 different feature selection algorithms and Random Undersampling with two post-sampling class distributions. We also investigate the use of sampling and feature selection without the iterative step (e.g., using the ranked list from a single iteration, rather than combining the lists from multiple iterations), and compare these results from the version which uses iteration. Our study is carried out using three groups of datasets with different levels of class balance, all of which were collected from a real-world software system. All of our experiments use four different learners and one feature subset size. We find that our proposed iterative feature selection approach outperforms the non-iterative approach.