模式识别催化剂生产调优

本文介绍如何将模式识别应用于生产调优,着重讨论了多指标(优化目标)因素的分级,提出了用模糊综合评价度量和依有序聚类最小损失函数准则划类,并相继进行变量与样本筛选、信息压缩、特征提取和模拟仿真获得优区操作条件,实施效果显著。     

Heavy arsenic doping of silicon grown by atmospheric pressure selective epitaxial chemical vapor deposition

Selective epitaxial Si with a high arsenic concentration of 2.2×10¹⁹ atoms/cm³ was deposited at a high growth rate of 3.3 nm/min under atmospheric pressure. It was confirmed that this method had excellent selectivity and produced films having good crystalline quality, abrupt dopant profiles at the interfaces, and smooth surfaces. The growth mechanism is discussed in terms of the relationship between the effects of arsenic surface segregation and etching by hydrogen chloride.     

Chemical vapor deposition of aluminum for ulsi applications

Aluminum has been used widely as a conducting material in the fabrication of integrated circuits, and chemical vapor deposition process for Al has been actively investigated for the application in ultra large scale integration. In this review, various precursors, mainly alkyl aluminum and alane compounds, and reaction mechanisms of these precursors in Al CVI) are described. Epitaxial growth and selectivity of the deposition are also discussed. In addition to thermal reactions, plasma and photochemical reactions are also briefly described.     

提高老采区资源回采率的若干途径

云应盐矿区通过新旧盐井更替、改造井身结构、完善分采工艺等技术措施，提高了老采区的资源回收率，实现了对老采区的挖潜改造。     

A new environmental friendly method for the preparation of sugar acids via catalytic oxidation on gold catalysts

A novel and efficient way of producing of aldonic acids is reported. The selective catalytic oxidation of monosaccharides (arabinose, ribose, xylose, lyxose, mannose, rhamnose, glucose, galactose, N-acetyl-glucosamine) and disaccharides (lactose, maltose, cellobiose, melibiose) on Au, Pd and Pt catalysts was investigated. By using the gold catalyst, a total selectivity with respect to aldonic acids and a high catalytic activity for all investigated sugars was found. The reaction conditions for the production of maltobionic and lactobionic acids via oxidation on gold catalysts were optimised. A high long-term stability of two different gold catalysts for the oxidation of maltose and lactose, respectively, was found.     

恶意软件鉴别技术及其应用

随着互联网技术的发展和安全形势的变化,恶意软件的数量呈指数级增长,恶意软件的变种更是层出不穷,传统的鉴别方法已经不能及时有效的处理这种海量数据,这使得以客户端为战场的传统查杀与防御模式不能适应新的安全需求,各大安全厂商开始构建各自的"云安全"计划。在这种大背景下,研究恶意软件检测关键技术是非常必要的。针对恶意软件数量大、变化快、维度高与干扰多的问题,我们研究云计算环境下的软件行为鉴别技术,探讨海量软件样本数据挖掘新方法、事件序列簇类模式挖掘新模型和算法及在恶意软件鉴别中的应用,并构建面向云安全的恶意软件智能鉴别系统原型以及中文钓鱼网站检测系统架构。     

基于ＥＥＭＤ域统计模型的话音激活检测算法

提出了一种基于EEMD域统计模型的话音激活检测算法。算法首先利用总体平均经验模态分解(Ensemble empirical mode decomposition,EEMD)对带噪语音进行分解,得到信号的本征模式函数(Intrinsicmode function,IMF)分量,选择与原信号的相关性最高的两个分量相加组成主分量;然后对主分量进行频域分解,引入统计模型,求出EEMD域特征参数;最后利用噪声与语音的EEMD域特征参数的不同来进行语音激活检测。实验结果表明,在不同信噪比情况下,本文算法性能优于目前常用的VAD算法,特别在噪声强度大时体现出明显的优势。     

旱情预报技术进展与展望

受全球气候变化影响和人类活动的加剧,干旱灾害发生频次增加且强度增大,严重威胁着我国的粮食安全和水安全。准确及时的旱情预报,对于制定科学有效的干旱应对策略、减少灾害造成的损失具有重大意义。从基于数理统计模型的预报技术和基于物理机制模型的预报技术两方面入手,梳理回顾了国内外研究进展,揭示了当前预报技术所存在的问题,并提出针对性的解决方案。未来研究应注重提高干旱监测数据的质量、突破核心关键技术、构建全国旱情预报业务化系统,为抗旱减灾事业提供强有力的科技支撑。     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.