Orthogonal discriminant improved local tangent space alignment based feature fusion for face recognition

Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.     

Overlay Share Mesh for Interactive Group Communication with High Dynamic

An overlay share mesh infrastructure is presented for high dynamic group communication systems, such as distributed interactive simulation (DIS) and distributed virtual environments (DVE). Overlay share mesh infrastructure can own better adapting ability for high dynamic group than tradition multi-tree multicast infrastructure by sharing links among different groups. The mechanism of overlay share mesh based on area of interest (AOI) was discussed in detail in this paper. A large number of simulation experiments were done and the performance of mesh infrastructure was studied. Experiments results proved that overlay mesh infrastructure owns better adaptability than traditional multi-tree infrastructure for high dynamic group communication systems.     

An Adaptive Scheme for Neighbor Discovery in Mobile Ad Hoc Networks

The neighbor knowledge in mobile ad hoc networks is important information. However, the accuracy of neighbor knowledge is paid in terms of energy consumption. In traditional schemes for neighbor discovery, a mobile node uses fixed period to send HELLO messages to notify its existence. An adaptive scheme was proposed. The objective is that when mobile nodes are distributed sparsely or move slowly, fewer HELLO messages are needed to achieve reasonable accuracy, while in a mutable network where nodes are dense or move quickly, they can adaptively send more HELLO messages to ensure the accuracy. Simulation results show that the adaptive scheme achieves the objective and performs effectively.     

Improved Real-Coded Genetic Algorithm Solution for Unit Commitment Problem Considering Energy Saving and Emission Reduction Demands

Unit commitment(UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm(IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.     

Feature Selection for SVM Classifiers Based on Discretization

The rough sets and Boolean reasoning based discretization approach （RSBRA） is not suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A modified RSBRA for feature selection was proposed and evaluated with SVM classifiers. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of the RSBRA, which maintains the fidelity of the training set after discretization. The experimental results show the modified algorithm has better predictive accuracy and less training time than the original RSBRA.     

Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model

This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model(IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.     

Inverse Learning Control of Nonlinear Systems Using Support Vector Machines

An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs overcome the problems of local minimum and curse of dimensionality. Additionally, the good generalization performance of SVMs increases the robustness of control system. The method of designing SVM inverse learning controller was presented. The proposed method is demonstrated on tracking problems and the performance is satisfactory.     

Segmented dynamic optimization model for asset-liability management of commercial banks and its applications

Asset-liability management is the core business of commercial banks. Effective method of asset-liability management is a continuously exploring topic in the academic and practical fields. According to the operational characteristics of commercial banks, this paper addresses a segmented dynamic optimization model under the perspective of the regulatory environment for China commercial banks. The model can perform segmented sliding optimization and correct control variables to make optimal decision with the changes in situations for a certain future time.