Fe（Ⅲ）-5-Br-PADAP络合物光化学还原反应的研究

研究了溶液酸度、光强度、有机酸及其浓度对Fe(Ⅲ)-5-Br-PADAP络合物光化学还原反应的影响,并对该光化学还原反应的机理作了初步探讨.结果表明,在pH为5.5的HAc-NaAc缓冲溶液中,Fe(Ⅲ)-5-Br-PADAP络合物的光化学还原反应速度较快;光强度增加,该光化学还原反应速度加快;甲酸等有机酸能加速该光化学还原反应;乙二酸、丙二酸能明显抑制该光化学还原反应;能促进反应的有机酸浓度增加,该光化学还原反应的速度加快.     

On the complexity of role updating feasibility problem in RBAC

In Role Based Access Control (RBAC) systems, it is necessary and important to update the role–permission assignments in order to reflect the evolutions of the system transactions. However, role updating is generally complex and challenging, especially for large-scale RBAC systems. This is because the resulting state is usually expected to meet various requirements and constraints. In this paper, we focus on a fundamental problem of role updating in RBAC, which determines whether there exists a valid role–permission assignment, i.e., whether it can satisfy all the requirements of the role updating and without violating any role–capacity or permission–capacity constraint. We formally define such a problem as the Role Updating Feasibility Problem (RUFP), and study the computational complexity of RUFP in different subcases. Our results show that although several subcases are solvable in linear time, this problem is NP-complete in the general case.     

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.     

Automatic 3D Indoor Scene Updating with RGBD Cameras

Since indoor scenes are frequently changed in daily life, such as re‐layout of furniture, the 3D reconstructions for them should be flexible and easy to update. We present an automatic 3D scene update algorithm to indoor scenes by capturing scene variation with RGBD cameras. We assume an initial scene has been reconstructed in advance in manual or other semi‐automatic way before the change, and automatically update the reconstruction according to the newly captured RGBD images of the real scene update. It starts with an automatic segmentation process without manual interaction, which benefits from accurate labeling training from the initial 3D scene. After the segmentation, objects captured by RGBD camera are extracted to form a local updated scene. We formulate an optimization problem to compare to the initial scene to locate moved objects. The moved objects are then integrated with static objects in the initial scene to generate a new 3D scene. We demonstrate the efficiency and robustness of our approach by updating the 3D scene of several real‐world scenes.     

基于STeC的高速列车运行模型与算法

为了使高速列车正点运行到达目的车站,提出了基于STeC(时空一致性)语言的高速列车运行模型与算法。该模型具有位置触发自动调整高速列车运行的特点,实现动态确定高速列车制动点并保证列车正点到达目的车站,从而满足了高速列车运行实时系统对时间和空间的一致性要求。通过Matlab/Simulink的仿真测试,证明了该模型的正确性和有效性。     

Effect of diamond-like carbon (DLC) on the properties of the NiTi alloys

NiTi alloy has found wide application in the biomedical field due to its unique shape memory effect, superelasticity and biocompatibility. However, the materials are vulnerable to surface corrosion and the most serious issue is out-diffusion of toxic Ni ions from the substrate into body tissues and fluids. In this paper, NiTi alloys were coated with diamond-like carbon (DLC) fabricated by plasma immersion ion implantation and deposition (PIIID) to improve their corrosion resistance and blood compatibility without sacrificing their shape memory effect and superelasticity. The structure of the films and the depth profiles between the films and substrate were studied using Raman spectroscopy and XPS, respectively. The phase transformation temperature, superelasticity, anticorrosion behavior and Ni ions release of the coated and uncoated sample were investigated by DSC, tensile tests, potentiodynamic polarization and AAS, respectively. The hemocompatibilty of the coated and uncoated samples was measured using clotting time and platelet adhesion. The results shows that the films is DLC accompanying with the formation of the mixing layer, and the DLC films can markedly improve the corrosion resistance and the hemocompatibility, obviously increase the ratio of albumin-to-fibrinogen and effectively block the Ni ions release of the NiTi alloys without sacrificing its superelasticity and changing its phase transformation temperature. The research results suggest DLC films prepared by PIIID could improve the in vivo performance of NiTi alloys implanted into the human body.     

汽车虚拟仪表盘的仿真设计

汽车仪表盘反映了汽车行驶过程的多种参数,设计虚拟仪表盘仿真系统在教学科研中具有重要意义。在分析汽车仪表盘功能的基础上,提出了汽车虚拟仪表盘总体设计方案。编写通信程序实现了上位 PC 机和下位单片机系统的数据采集发布功能。应用VB开发了数字化汽车虚拟仪表盘界面与性能仿真系统,可模拟发动机转速、车速、温度、时间及里程等信息实时显示在计算机界面上;操作控制按钮可实现汽车转向灯指示功能。经实际试验运行显示该仿真系统工作正常,实现了对汽车仪表盘的仿真效果。     

人工免疫优化的汽车散热器功能设计

应用公理设计原理设计车用散热器时,耦合功能的存在是一个普遍问题。国内外学者提出使用人工免疫方法求解耦合功能集。针对这些算法在度量抗体优异性时存在的不足,提出了新的度量指标：耦合功能集聚度,并给出相应公式,同时在抗体生成算法中引入基因优选的方法。经在银轮股份进行车用机油散热器设计仿真实验,表明新的算法具有较高的效率。