ASMEPTC4-2008锅炉性能试验效率计算软件开发与应用

ASME PTC 4-2008锅炉性能试验效率计算软件能够大大便利规程中的有关计算。本文以ASME PTC4-2008锅炉性能试验规程为基础,结合我国实际情况进行了该软件的开发。经过现场检验,在计算结果的准确性、易用性及适应性上都满足科研人员日常使用的要求。该软件涵盖新版标准要求的各项损失和外来热量的计算,并包括试验结果的不确定度计算。     

自升式海洋平台多桩腿同步控制的研究

该文针对多电机同步控制系统的参数时变、非线性以及容易受负载扰动等因素,提出了基于相邻交叉耦合结构的BP神经网络PID控制算法,在Mathb/Simulink环境下搭建了多电机同步控制系统仿真模型.为实现自升式海洋平台升降过程的同步控制,在自升式海洋平台升降控制系统中引入了基于相邻交叉耦合结构的BP神经网络PID控制算法,经试运行表明,海洋平台升降过程中,同步性能好、系统可靠性高、结构简单、完全能够满足海洋平台的平稳升降要求.     

基于排列熵和支持向量机的癫痫发作预测研究

针对癫痫发作给病人带来的巨大伤害,为临床治疗留下足够空余时间,提出一个可以预测癫痫发作的系统模型。对21名癫痫病人进行研究,提取具有较低算法复杂度的排列熵构成特征向量,将其输入支持向量机（support vector machine,SVM）训练出学习模型,用来识别发作期样本,利用投票机制充分考虑病人差异来判断所处状态,最终实现癫痫的实时预测。结果表明,其中81%的发作可以提前平均50多分钟预测到,且具有较低的误报率。为癫痫发作预测系统的理论研究打下坚实基础。     

Fabrication and amplification of Rhodamine B‐doped step‐index polymer optical fiber

A step‐index polymer optical fiber (SI POF) containing Rhodamine B in poly(methyl methacrylate) (PMMA) has been fabricated by a preform technique. Fluorescence of different fiber lengths were observed and discussed. A high gain (23 dB) for a SI POF with 60‐cm length, 400‐μm diameter was obtained. The Rhodamine B content of the doped SI POF is 5 ppm‐wt. The signal wavelength providing the highest gain for a 60 cm SI POF is around 630 nm, and the optimum fiber length is about 60 cm at 10 kW launched pump power. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 93: 681–685, 2004     

石灰石在煤气化过程中的催化和固硫作用

基于CaO在煤气化中的催化和固硫作用,以及石灰石加热分解的性质,石灰石有可能作为煤气化过程中的催化剂和固硫剂。本文根据国内外对石灰石在煤燃烧和煤气化中作用的研究,从石灰石结构、Ca/C,Ca/S,可能的反应机理等几个方面进行了介绍和分析,并指出需进一步研究的内容。     

循环式活性污泥法处理炼油污水

研究为在炼油污水的CAST工艺的主反应区中 ,通过恰当控制pH值和DO(污水中溶解氧含量 )值 ,可以使硝化与反硝化反应同步进行 ,在去除COD ,BOD的同时 ,也可提高其脱氮效果 ,从而得出DO的最佳值为 0 .8mg/L ,pH为 7.3～ 7.7之间。     

竹材丙酮法木质素高效分离技术研究

在一定的温度、压力条件下,利用丙酮提取竹材中的木质素,丙酮通过蒸馏回收后,可以反复利用;而通过蒸馏,得到的高纯度的木质素是重要的化工原料,整个过程形成一个封闭循环系统,基本上无三废排放;研究结果表明:当丙酮浓度为80%,液固比为10∶1,在最高反应温度160℃下保温3h,降解木质素的效果最佳;为解决制浆造纸行业废水对环境的污染问题提出了一条新的途径。     

Electrochemical performance of nanocrystalline Li3V2(PO4)3/carbon composite material synthesized by a novel sol–gel method

A novel sol–gel method based on V₂O₅·nH₂O hydro-gel was developed to synthesize nanocrystalline Li₃V₂(PO₄)₃/carbon composite material. In this route, V₂O₅·nH₂O hydro-gel, NH₄H₂PO₄, Li₂CO₃ and high-surface-area carbon were used as starting materials to prepare precursor, and the Li₃V₂(PO₄)₃/carbon was obtained by sintering precursor at 750 °C for 4 h in flowing argon. The sol–gel synthesis ensures homogeneity of the precursors and improved reactivity. The sample was characterized by XRD, SEM and TEM. X-ray diffraction results show Li₃V₂(PO₄)₃ sample is monoclinic structure with the space group of P2₁/n. The TEM image indicates that the Li₃V₂(PO₄)₃ particles modified by conductive carbon are about 70 nm in diameter. The Li₃V₂(PO₄)₃/carbon system showed that the discharge capacities in the first and 50th cycle are about 155.3 and 143.6 mAh/g, respectively, in the range of 3.0–4.8 V. The sol–gel method is fit for the preparation of Li₃V₂(PO₄)₃/carbon composite material which may offer some favorable properties for commercial application.     

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.     

Fisher discrimination based low rank matrix recovery for face recognition

In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results.