2-氨基-5-甲基苯甲酸合成工艺的改进

对甲苯胺与水合氯醛、盐酸羟胺反应制得异亚硝基乙酰对甲苯胺,于浓硫酸中脱水成环得到5-甲基靛红,然后在碱性条件下以双氧水为氧化介质合成2-氨基-5-甲基苯甲酸。通过正交实验得到了一、二步反应的较佳工艺条件,三步反应总收率以对甲苯胺计算达61.7%。通过FTIR和1HNMR对最终产品进行了表征。     

陶瓷热障涂层在化工过程装备中的应用

针对化工生产中的易氧化、高温腐蚀和易磨损等因素,提出适用于化工过程装备的材料体系设计方案。在方案中将材料设计思路创造性地应用到陶瓷热障涂层,提出了对其进行结构设计的总体思路,并对主要设计参数的设计方法进行了初步研究。     

柑桔加工下脚料提取类胡萝卜素、橙皮苷、果胶和柠檬苦素

介绍了从柑桔加工下脚料提取类胡萝卜素、橙皮苷、果胶和柠檬苦素类产品的工艺方法 ,并简述了这些产品在医药、食品、日化等方面的应用     

Laplacian group sparse modeling of human actions

Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.     

Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes.     

A new generalised α scale spaces quadrature filters

The α   scale spaces is a recent theory that open new possibilities of phase-based image processing. It is a parameterised class (α∈]0,1])$(\alpha \in ]0,1\left]\right)$ of linear scale space representations, which allows a continuous connection beyond the well-known Gaussian scale space (α=1$\alpha =1$). In this paper, we make use of this unified representation to derive new families of band pass quadrature filters, built from derivatives and difference of the α scale space generating kernels. This construction leads to generalised α kernel filters including the commonly known families derived from the Gaussian and the Poisson kernels. The properties of each family are first studied and then experiments on one- and two-dimensional signals are shown to exemplify how the suggested filters can be used for edge detection. This work is complemented by an experimental evaluation, which demonstrates that the new proposed filters are a good alternative to the commonly used Log-Gabor filter.     

Comparing techniques for authorship attribution of source code

Attributing authorship of documents with unknown creators has been studied extensively for natural language text such as essays and literature, but less so for non‐natural languages such as computer source code. Previous attempts at attributing authorship of source code can be categorised by two attributes: the software features used for the classification, either strings of n tokens/bytes (n‐grams) or software metrics; and the classification technique that exploits those features, either information retrieval ranking or machine learning. The results of existing studies, however, are not directly comparable as all use different test beds and evaluation methodologies, making it difficult to assess which approach is superior. This paper summarises all previous techniques to source code authorship attribution, implements feature sets that are motivated by the literature, and applies information retrieval ranking methods or machine classifiers for each approach. Importantly, all approaches are tested on identical collections from varying programming languages and author types. Our conclusions are as follows: (i) ranking and machine classifier approaches are around 90% and 85% accurate, respectively, for a one‐in‐10 classification problem; (ii) the byte‐level n‐gram approach is best used with different parameters to those previously published; (iii) neural networks and support vector machines were found to be the most accurate machine classifiers of the eight evaluated; (iv) use of n‐gram features in combination with machine classifiers shows promise, but there are scalability problems that still must be overcome; and (v) approaches based on information retrieval techniques are currently more accurate than approaches based on machine learning. Copyright © 2012 John Wiley & Sons, Ltd.     

Coordinating efficiency and equity in disaster relief logistics via information updates

This paper addresses a multi-supplier, multi-affected area, multi-relief, and multi-vehicle relief allocation problem in disaster relief logistics. A multi-objective optimisation model based on disaster scenario information updates is proposed in an attempt to coordinate efficiency and equity through timely and appropriate decisions regarding issues such as vehicle routing and relief allocation. An optimal stopping rule is also proposed to determine the optimum period of delay before responding to disaster, because decision making requires accurate disaster information. The main contribution of this paper is solving relief allocation problem in a novel way by correlating operational research with statistical decision making and Bayesian sequential analysis. Finally, a case is presented based on the post-disaster rescue in Eastern China after supertyphoon Saomai to test the applicability and show the potential advantages of the proposed model.     

深海锰结核及其处理技术新探索

简述了深海锰结核的特征及冶金处理原则,综述了当今国内外对锰结核的处理技术的工艺过程、操作条件、金属回收率以及各自的优缺点。介绍了具有探索性意义的处理锰结核的新型还原剂BR法、三相氧化法。提出了使用FFC剑桥工艺处理锰结核得到锰铁合金或有价金属的新思路,并介绍了锰铁合金的用途。