基于人脸重要特征和SVM的人脸识别方法

提出了一种基于人脸重要特征的人脸识别方法,首先选取人脸的重要特征并将其具体化,对得到的重要特征进行主成分分析,然后用支持向量机(Support Vector Machine,SVM)设计重要特征分类器来确定测试人脸图像中重要特征,同时设计支持向量机(SVM)人脸分类器,确定人脸图像的所属类别.对ORL人脸图像数据库进行仿真实验,结果表明,该方法要优于一般的基于整体特征的人脸识别方法并有较强的鲁棒性.     

Source supports in electrostatics

We investigate the inverse source problem of electrostatics in a bounded and convex domain with compactly supported source. We try to extract all information about the unknown source support from the given Cauchy data of the associated potential, adopting by this previous work of Kusiak and Sylvester to the case of electrostatics. We introduce, and for the unit disk we also compute numerically, what we call the discoidal source support, i.e., the smallest set made up by the intersection of disks within the domain, which carries a source compatible with the given data. AMS subject classification (2000)  35R30, 65N21     

A multicriteria DSS for stock evaluation using fundamental analysis

The paper describes a multicriteria decision support system which aims at presenting an evaluation of the Athens Stock Exchange (ASE) stocks, on the basis of fundamental analysis. The system evaluates the stocks based on the method of fundamental analysis ratios, which is the most appropriate evaluation approach regarding investment decisions within a long term horizon. In addition to quantitative data deriving from fundamental analysis, the system uses qualitative data as well, in order to improve the reliability of the evaluation. The system introduced in this paper, utilises multicriteria analysis methodologies in order to rank the stocks by placing the best stock first and the worst last. Stock evaluation considers the specific characteristics of the potential investor, as well as his attitude towards undertaken risk. The final output of the system is four stock rankings which respond to four different criteria groups, depending on the type of accounting plan each listed company belongs to. The system incorporates a large volume of relevant information and operates in ‘real world conditions’ since its data are constantly updated. Finally, the system is intended for both institutional and private investors.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

Reducing Cognitive Effort in Scoring Negotiation Space Using the Fuzzy Clustering Model

Negotiation scoring systems are fundamental tools used in negotiation support to facilitate parties searching for negotiation agreement and analyzing its efficiency and fairness. Such a scoring system is obtained in prenegotiation by implementing selected multiple criteria decision-aiding methods to elicit the negotiator’s preferences precisely and ensure that the support is reliable. However, the methods classically used in the preference elicitation require much cognitive effort from the negotiators, and hence, do not prevent them from using heuristics and making simple errors that result in inaccurate scoring systems. This paper aims to develop an alternative tool that allows scoring the negotiation offers by implementing a sorting approach and the reference set of limiting profiles defined individually by the negotiators in the form of complete packages. These limiting profiles are evaluated holistically and verbally by the negotiator. Then the fuzzy decision model is built that uses the notion of increasing the preference granularity by introducing a series of limiting sub-profiles for corresponding sub-categories of offers. This process is performed automatically by the support algorithm and does not require any additional preferential information from the negotiator. A new method of generating reference fuzzy scores to allow a detailed assignment of any negotiation offer from feasible negotiation space to clusters and sub-clusters is proposed. Finally, the efficient frontier and Nash’s fair division are used to identify the recommended packages for negotiation in the bargaining phase. This new approach allows negotiators to obtain economically efficient, fair, balanced, and reciprocated agreements while minimizing information needs and effort.     

Geographic Characterization of Leccinum rugosiceps by Ultraviolet and Infrared Spectral Fusion

Leccinum rugosiceps is an edible mushroom belonging to genus Leccinum of Boletaceae. Its fruiting bodies are richer in nutrients than many vegetables and fruit. The model of support vector machine was established for the discrimination of L. rugosiceps from regions based on rapid and low-cost ultraviolet and infrared spectroscopies. The mid-level data fusion was performed by support vector machine. Compared to a single spectroscopic technique, mid-level data fusion provided higher accuracy by selecting the most significant variance from data matrixes based on partial least squares discriminant analysis. The accuracy of the classification of samples in the calibration and test sets were 85.00 and 94.74%, higher than separate measurements by ultraviolet or infrared spectroscopy. This approach has applications for authentication and quality assessment of L. rugosiceps.     

基于支持向量回归机的陀螺漂移预测模型

为了预测某导弹陀螺漂移趋势,以该陀螺漂移角速度时间序列为对象,建立了基于支持向量回归机的预测模型。针对该预测模型的特点,提出了支持向量预选取的模型优化方法。基于ε不敏感损失函数的支持向量回归机具有稀疏性,其结构由支持向量决定。因此从训练样本集中预选出有可能成为支持向量的样本,精简样本规模是提高该类支持向量回归机训练和预测效率的有效方法。针对该类支持向量回归机从分类和回归两个角度分析了支持向量的几何特征,提出了核函数空间免疫聚类的支持向量预选取方法并用于某导弹陀螺漂移预测模型的数据预处理。仿真结果表明优化后的预测模型运算量小、建模速度快,精度高。     

点支撑预应力中厚矩形板的弯曲

基于Reissner-Mindlin一阶剪切变形板理论，讨论在预加面内机械荷载或温度场作用下，点支撑中厚矩形板的弯曲问题。温度场假定沿板表面为均布，沿板厚方向为线性分布的。利用考虑剪切变形影响的Timoshenko梁函数，采用Rayleigh-Ritz法给出不同边界条件下点支撑中厚板在横向荷载作用下的挠度和弯矩分布。结果表明，均匀温度场与预加面内压力将使板的挠度和弯矩增加。支撑点位置的变化、边界约束条件和横向剪切变形效应都对板的内力大小和分布有显著影响。