Robust and efficient ridge-based palmprint matching

During the past decade, many efforts have been made to use palmprints as a biometric modality. However, most of the existing palmprint recognition systems are based on encoding and matching creases, which are not as reliable as ridges. This affects the use of palmprints in large-scale person identification applications where the biometric modality needs to be distinctive as well as insensitive to changes in age and skin conditions. Recently, several ridge-based palmprint matching algorithms have been proposed to fill the gap. Major contributions of these systems include reliable orientation field estimation in the presence of creases and the use of multiple features in matching, while the matching algorithms adopted in these systems simply follow the matching algorithms for fingerprints. However, palmprints differ from fingerprints in several aspects: 1) Palmprints are much larger and thus contain a large number of minutiae, 2) palms are more deformable than fingertips, and 3) the quality and discrimination power of different regions in palmprints vary significantly. As a result, these matchers are unable to appropriately handle the distortion and noise, despite heavy computational cost. Motivated by the matching strategies of human palmprint experts, we developed a novel palmprint recognition system. The main contributions are as follows: 1) Statistics of major features in palmprints are quantitatively studied, 2) a segment-based matching and fusion algorithm is proposed to deal with the skin distortion and the varying discrimination power of different palmprint regions, and 3) to reduce the computational complexity, an orientation field-based registration algorithm is designed for registering the palmprints into the same coordinate system before matching and a cascade filter is built to reject the nonmated gallery palmprints in early stage. The proposed matcher is tested by matching 840 query palmprints against a gallery set of 13,736 palmprints. Experimental results show that the proposed matcher outperforms the existing matchers a lot both in matching accuracy and speed.     

Reference price effect and its implications for decision making in online auctions: An empirical study

Simultaneous supplies of an item by multiple online auction vendors greatly reduce the burden of arduous search that a consumer has to conduct in a brick-and-mortar market. The enriched information set endows all auction participants with added decision flexibility and complexity. One of such flexibilities or complications is the potential of information cross-referencing. In this study, we empirically tested if and how bidders interacted with each other through cross-referencing. Our results strongly supported the existence of cross-referencing. We also identified new strategies and tactical moves as best responses for sellers and bidders under the online simultaneous condition. This research provided another support for the path-dependent nature of online auctions as bidders were found to actively adjust their behavior in avoidance of winner's curse.     

Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Modeling NO_x emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NO_x combustion. This paper presents an efficient NO_x emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN) and generalized regression (GRNN) neural networks. A large number of NO_x emissions data from an actual power plant, was employed to train and validate the SVR model as well as two neural networks models. Moreover, an ant colony optimization (ACO) based technique was proposed to select the generalization parameter C and Gaussian kernel parameter γ. The focus is on the predictive accuracy and time response characteristics of the SVR model. Results show that ACO optimization algorithm can automatically obtain the optimal parameters, C and γ, of the SVR model with very high predictive accuracy. The predicted NO_x emissions from the SVR model, by comparing with the BPNN model, were in good agreement with those measured, and were comparable to those estimated from the GRNN model. Time response of establishing the optimum SVR model was in scale of minutes, which is suitable for on-line and real-time modeling NO_x emissions from coal-fired utility boilers.     

小型无人直升机地面控制站设计与实现

通过分析小型无人直升机对地面控制站的需求,介绍地面控制站的结构和功能,分析开发地面控制站涉及的关键技术和解决方案,控制站采用基于面向对象的VC++开发环境和嵌入式Matlab混合编程技术。通过在某小型无人直升机飞行实验中的应用,证明该系统具有实时性强、稳定性好、人机界面友好、可扩展能力强等优点。     

装甲车车身自动调平系统原理及算法设计

为了提高6点装甲车辆调平的快速性和稳定性,根据装甲车结构,考虑响应时间、速度和可靠性等综合因素,将车身系统由6点调平系统等效为4点调平系统。利用相应的角度和压力传感器检测车架的水平状态,以确定油气簧的动作量。采用双速调整的方法计算比例阀门的开启大小及时间,运用西门子控制器实现对调平系统的控制。试验结果表明,该系统具有较高的快速性和稳定性。     

Design of a modified repetitive-control system based on a continuous–discrete 2D model

This paper concerns the problem of designing a modified repetitive-control system for a class of strictly proper plants. Repetitive control involves two types of actions: control and learning; but the insertion of a low-pass filter in a modified repetitive controller, which is employed to guarantee the stability of the system, mixes the two actions together. In this paper, a continuous–discrete two-dimensional model is first constructed. Next, the continuity of repetitive control and Lyapunov stability theory are applied to the model to establish two linear-matrix-inequality (LMI) based sufficient stability conditions, one for the design of the cutoff angular frequency and one for the design of the feedback gains. The features of these conditions are exploited to develop an iterative algorithm that searches for the best combination of the maximum cutoff angular frequency of the low-pass filter and the feedback gains. A numerical example illustrates the design procedure and demonstrates the validity of the method.     

Performance of Sewage Sludge Treatment and Electricity Generation by Different Configuration Microbial Fuel Cells

This paper compared the degradation efficiency of sludge organic matters and electric-production by two typical microbial fuel cells——dual-chamber microbial fuel cell(DMFC)and single chamber air cathode microbial fuel cell(SAMFC),and the variations of sludge protein,polysaccharide and ammonia nitrogen within the systems were also investigated.The results showed that the concentration of sludge soluble chemical oxygen demand,protein and carbohydrate of DMFC are higher than these of SAMFC during the systems operation,while DMFC can achieve a better ammonia nitrogen removal than SAMFC.Under the same operation condition,the stable voltage output of DMFC and SAMFC is 0.61 V and 0.37 V;the maximum power density of DMFC and SAMFC is 2.79 W/m3and 1.25 W/m3;TCOD removal efficiency of DMFC and SAMFC is 34.14%and 28.63%for 12 d,respectively.Meanwhile,DMFC has a higher coulomb efficiency than SAMFC,but both are less than5%.The results showed that DMFC present a better performance on sludge degradation and electric-production.     

Application of BP Neural Network in Prediction of Cu-Pb Composite Plates Properties

Abstract： The artificial neural networks（ANN） , which have broad application, are proposed to develop Cu-Pb composite plates materials. Based on the back propagation（BP） algorithm of the forward muhilayer perceptron, the model to predict the shear stress under different ingredient of the third element and the hot dipping temperature for Cu-Pb composite plates are established. Then the relational model among the third element, hot dipping temperature and shear stress by using the limited data are studied, and the forecast average error is 4%. This model can satisfy the requirements of the precision of forecast in the project experiment process. The results show that the corresponding shear stress is greater when the third element in the element contains more Sn; the most appropriate temperature of hot-dip plating about is 340℃, ＇after predicted with lead/the third element/the best performance of copper composite material element of the third group is the one-element Sn, hot dip plating temperature is 335 ℃ ; two-element is 90% Sn 10% Bi, and hot dip plating temperature is 345 ℃. The prediction results can be used for a reference in instructing the further experimental design.     

Experimental Research on the Dynamic Energy Absorbing Capacity of the Honeycomb Paperboard Filled with Polyurethane

A kind of composite buffering material was made by filling the voids of honeycomb paperboard with polyurethane. Drop tests were performed to evaluate the dynamic energy absorption capacity of the material. Based on the tests results, the mechanical behaviors of the material under low velocity dynamic impact conditions were analyzed. It was shown that the absorbed energy of the composite material varies inversely with the void diameter. The absorbed energy of the composite material is 1 - 2 times than that of honeycomb paperboard and polyurethane. The energy absorption efficiency of the composite material is better than those of honeycomb paperboard and polyurethane.