煤气废水在不同生化环境中的处理效果分析及工艺优化

生物处理方法是煤气废水处理的核心工艺,生物处理的功效决定了煤气废水处理的经济可行性。通过研究煤气废水中主要污染物在厌氧和好氧生物处理系统中的去除效果,分析污染物在不同的生化环境中的降解特征,进而提出一种新型煤气废水生物处理工艺。     

宏观过程的方向与限度——可逆性判据(Ⅱ)

本文从平衡判据的普遍规律--克劳修斯不等式出发,按照不同的过程条件,分别讨论了熵判据、亥氏函数和吉氏函数判据以及相变化和化学变化的可逆性判据.同时指出了这些平衡判据所包含的物理意义及它们间的一些内在联系.     

水解-水热体系中同质异构TiO2纳米晶生成机理的分子模拟研究

在实验研究的基础上,采用分子模拟的方法,对在水解-水热体系中TiO2纳米颗粒晶相生成原理进行了系统的模拟计算和建模研究,文章介绍了分子模拟软件Hyperchem的计算方法,建立了钛离子水解过程和能量分布模型,利用模拟计算和模型,直观和定量分析了水解中间产物、水热晶化过程以及最终产物间的关系。研究了pH值对同质异构相成因的影响机理;静电场作用对同质异构相生成的影响。考察了水合络离子的连接方式,指出顶角连接与棱边连接保持到最终产物的结构中。从能量的角度揭示了同质异构TiO2晶型成因。     

催化裂化再生器中铂助燃剂催化活性的测定(Ⅰ)——在新助燃剂上CO氧化的反应动力学

研究了催化裂化再生条件下铂助燃剂上ＣＯ氧化反应动力学。在排除了反应管及稀释剂的催化氧化作用后，测定了铂含量为１０－６ｇ／ｇ的国产新助燃剂上的反应动力学数据，得出了在氧过量时一氧化碳和氧的反应级数分别为１和０．５。给出了６６０℃下的反应速度常数值及其置信区间。     

超重力技术在内循环生物反应器中的应用

超重力技术作为一种高新技术,应用于对相间传递过程进行强化的多相过程,它可以应用在内循环生物反应器中,使传质效率得到了很大的提高,是一种很有前途的技术,在工业上有重要的应用前景.     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

A general non-local denoising model using multi-kernel-induced measures

Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising. Due to their superior performance, the non-local methods have recently drawn more and more attention in the image denoising community. However, these methods generally do not work well in handling complicated noises with different levels and types. Inspired by the fact in machine learning field that multi-kernel methods are more robust and effective in tackling complex problems than single-kernel ones, we establish a general non-local denoising model based on multi-kernel-induced measures (GNLMKIM for short), which provides us a platform to analyze some existing and design new filters. With the help of GNLMKIM, we reinterpret two well-known non-local filters in the united view and extend them to their novel multi-kernel counterparts. The comprehensive experiments indicate that these novel filters achieve encouraging denoising results in both visual effect and PSNR index.     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition

Due to the noise disturbance and limited number of training samples, within-set and between-set sample covariance matrices in canonical correlation analysis (CCA) usually deviate from the true ones. In this paper, we re-estimate within-set and between-set covariance matrices to reduce the negative effect of this deviation. Specifically, we use the idea of fractional order to respectively correct the eigenvalues and singular values in the corresponding sample covariance matrices, and then construct fractional-order within-set and between-set scatter matrices which can obviously alleviate the problem of the deviation. On this basis, a new approach is proposed to reduce the dimensionality of multi-view data for classification tasks, called fractional-order embedding canonical correlation analysis (FECCA). The proposed method is evaluated on various handwritten numeral, face and object recognition problems. Extensive experimental results on the CENPARMI, UCI, AT&T, AR, and COIL-20 databases show that FECCA is very effective and obviously outperforms the existing joint dimensionality reduction or feature extraction methods in terms of classification accuracy. Moreover, its improvements for recognition rates are statistically significant on most cases below the significance level 0.05.