舒曼钢琴音乐的双重性格特征

德国作曲家罗伯特·舒曼是十九世纪上半叶欧洲音乐文化史上最突出的人物之一。在他的钢琴音乐创作中,深刻地反映出浪漫主义音乐的特点。舒曼设计了一个想象中的“大卫同盟”,其中有两名核心盟员,一个叫弗洛列斯坦,另一个叫埃塞比乌斯。前者性格热情奔放,锋芒毕露;后者则文雅娴静,富于幻想,实际上这就是舒曼本人性格中的两个侧面。这种双重性格特征在舒曼的钢琴音乐中得到充分展示,并成为舒曼钢琴音乐的艺术特征之一。     

Analytical modeling of the periodic nonlinearity in heterodyne interferometry

The periodic nonlinearity that arises from nonideal laser sources and imperfections of optical components limits the accuracy of displacement measurements in heterodyne interferometry at the nanometer level. An analytical approach to investigating the nonlinearity is presented. Frequency mixing, polarization mixing, polarization-frequency mixing, and ghost reflections are all included in this investigation. A general form for the measurement signal, including that of the distortions, is given. The analytical approach is also applicable to homodyne interferometry.     

Highly efficient, widely tunable, 10-Hz parametric amplifier pumped by frequency-doubled femtosecond Ti:sapphire laser pulses

We report a 10-Hz, highly efficient, widely tunable (from the visible to the IR), broadband femtosecond optical parametric generator and optical parametric amplifier (OPA) in BBO, LBO, and CBO crystals pumped by the frequency-doubled output of a regeneratively amplified Ti:sapphire laser at 400 nm. The output of the system is continuously tunable from 440 nm to 2.5 mum with a maximum overall efficiency of ~25% at 670 nm and an optical conversion efficiency of more than 36% in the OPA stage. The effects of the seed beam energy, the type of the crystal and the crystal length, and the pumping energy of the output of the OPA, such as the optical efficiency, the bandwidth, the pulse duration, and the group velocity mismatch between the signal and the idler and between the seeder and the pump, are investigated. The results provide useful information for optimization of the design of the system.     

Research on the group decision-making about emergency event based on network technology

In order to improve decision-making efficiency about emergency event, this paper proposes a novel concept, i.e., Agile-Delphi Method, which is an integration of agile decision and Delphi Method implicating that the decision-makers instantly deliver, respond, treat, and utilize information via Delphi process while conducting group decision-making about emergency event. The paper details the mechanism of group decision-making about emergency event based on network technology and Agile-Delphi Method. Finally, the paper conducts an empiric analysis taking the “111 event”, i.e., the liquid ammonia spill event happened on November 1, 2006 in a phosphorus chemical company in China, as an example.     

Analysis and application of Bio-Inspired Multi-Net Security Model

With the rapid development of network technologies and deteriorating of network environment, traditional single-net security system cannot satisfy the security requirement. The excellent security performance of biological systems impels the bio-inspired network security theory to be a hot research area currently. Based on Bio-inspired Multidimensional Network Security Model we have put forward, we have advanced Bio-inspired Multi-Net Security system by implementing the functional distribution of different subnets of different transient states in Multi-Net Paralleling structure. Firstly, parameter estimation and modified algorithms of Hidden Markov Model are introduced to construct the mathematical mode of B-MNS; secondly, the integrated performance of our modified B-MNS has been tested and its simulation has been carried out. So the feasibility, validity and high efficiency of our model have been demonstrated theoretically, and practically.     

Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital

“Emergency medicine” is the front line of medical service a hospital provides; also it is the department people seek medical care from immediately after an emergency happens. The statistics by the Department of Health, Executive Yuan, indicate that over years, the number of people at the emergency department has been increasing. The US has introduced and practiced the triage system in the emergency medicine in 1960, whereby to aid the emergency department in allocating the patients, to give them appropriate medical care by the fast decision of the nurses and doctors in case of the patients’ seriousness through their judgment.This study takes on the knowledge contained in the massive data of unknown characteristics in the triage database at a Taiwanese regional hospital, using the cluster analysis and the rough set theory as tools for data mining to extract, with the analysis software ROSE2 (Rough Sets Data Explorer) and through rule induction technique, the imprecise, uncertain and vague information of rules from the massive database, and builds the model that is capable of simplifying massive data while maintaining the accuracy in classifying rules. After analyzing and evaluating the knowledge obtained from relevant mining in the hospitals past medical data for the consumption of emergency medical resources, this thesis proposes suggestions as reference for the hospitals in subsequent elevation of medical quality and decrease in operative costs.     

Strong solutions to the incompressible magnetohydrodynamic equations with vacuum

We consider the incompressible magnetohydrodynamic (MHD) equations with the coefficients depending on the density and temperature. We prove the existence of unique local strong solutions for all initial data satisfying a natural compatibility condition. The initial density need not be positive and may vanish in an open set.     

A rough set approach to feature selection based on power set tree

Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Traditional hill-climbing search approaches to feature selection have difficulties to find optimal reducts. And the current stochastic search strategies, such as GA, ACO and PSO, provide a more robust solution but at the expense of increased computational effort. It is necessary to investigate fast and effective search algorithms. Rough set theory provides a mathematical tool to discover data dependencies and reduce the number of features contained in a dataset by purely structural methods. In this paper, we define a structure called power set tree (PS-tree), which is an order tree representing the power set, and each possible reduct is mapped to a node of the tree. Then, we present a rough set approach to feature selection based on PS-tree. Two kinds of pruning rules for PS-tree are given. And two novel feature selection algorithms based on PS-tree are also given. Experiment results demonstrate that our algorithms are effective and efficient.     

Semi-supervised locally discriminant projection for classification and recognition

Semi-supervised dimensional reduction methods play an important role in pattern recognition, which are likely to be more suitable for plant leaf and palmprint classification, since labeling plant leaf and palmprint often requires expensive human labor, whereas unlabeled plant leaf and palmprint is far easier to obtain at very low cost. In this paper, we attempt to utilize the unlabeled data to aid plant leaf and palmprint classification task with the limited number of the labeled plant leaf or palmprint data, and propose a semi-supervised locally discriminant projection (SSLDP) algorithm for plant leaf and palmprint classification. By making use of both labeled and unlabeled data in learning a transformation for dimensionality reduction, the proposed method can overcome the small-sample-size (SSS) problem under the situation where labeled data are scant. In SSLDP, the labeled data points, combined with the unlabeled data ones, are used to construct the within-class and between-class weight matrices incorporating the neighborhood information of the data set. The experiments on plant leaf and palmprint databases demonstrate that SSLDP is effective and feasible for plant leaf and palmprint classification.