Segment-based adaptive hyper-Erlang model for long-tailed network traffic approximation

Modeling the long-tailedness property of network traffic with phase-type distributions is a powerful means to facilitate the consequent performance evaluation and queuing based analysis. This paper improves the recently proposed Fixed Hyper-Erlang model (FHE) by introducing an adaptive framework (Adaptive Hyper-Erlang model, AHE) to determine the crucially performance-sensitive model parameters. The adaptive model fits long-tailed traffic data set directly with a mixed Erlang distribution in a new divide-and-conquer manner. Compared with the well-known hyperexponential based models and the Fixed Hyper-Erlang model, the Adaptive Hyper-Erlang model is more flexible and practicable in addition to its accuracy in fitting the tail behavior.

Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks

This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable parameters of the WNFN model. The proposed WNFN-S learning scheme is based on dynamic symbiotic evolution (DSE). The proposed DSE uses the sequential-search-based dynamic evolutionary (SSDE) method. In some real-world applications, exact training data may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolutionary learning, called WNFN-R, is proposed. Computer simulations have been conducted to illustrate the performance and applicability of the proposed WNFN-S and WNFN-R learning algorithms.     

Knowledge discovery in corporate events by neural network rule extraction

Over the last two decades, artificial neural networks (ANN) have been applied to solve a variety of problems such as pattern classification and function approximation. In many applications, it is desirable to extract knowledge from trained neural networks for the users to gain a better understanding of the network’s solution. In this paper, we use a neural network rule extraction method to extract knowledge from 2222 dividend initiation and resumption events. We find that the positive relation between the short-term price reaction and the ratio of annualized dividend amount to stock price is primarily limited to 96 small firms with high dividend ratios. The results suggest that the degree of short-term stock price underreaction to dividend events may not be as dramatic as previously believed. The results also show that the relations between the stock price response and firm size is different across different types of firms. Thus, drawing the conclusions from the whole dividend event data may leave some important information unexamined. This study shows that neural network rule extraction method can reveal more knowledge from the data.     

Signal enhancement of surface plasmon resonance-based immunoassays for the allergen detection

A surface plasmon resonance (SPR) biosensor was used to determine the recombinant group 1 house dust mite allergen (rDer f1) in both HBS-EP buffer and fetal bovine serum (FBS). The monoclonal antibody was immobilized onto the CM5 sensor chip surface using an amine coupling method. The procedures of antibody immobilization and the subsequent primary and enhanced immunoassay were monitored in real time. The sensitivity for rDer f1 detection was remarkably improved by using intact polyclonal antibody as signal amplifying agent. Using this signal enhanced SPR immunosensor, rDer f1 in HBS-EP buffer and FBS was detected at a concentration of 15.4 and 32.1 ng/ml, respectively. The result demonstrates that SPR biosensor is a simple and reliable method for allergen detection.     

Characterization of surface deformation with the Edge of LightTM technique

The aircraft lap joints are inspected with an enhanced visual inspection technique named “Edge of Light”, which is patented by the NRC Institute for Aerospace Research. This technique is applicable for rapid detection of possible hidden corrosion in lap joints. The surface deformation due to hidden corrosion can be characterized by this optical-based inspection method. In this study, a calibration procedure is developed to quantify the lap joint surface deformation. The effect of surface reflectivity is investigated with the solid film highlighting technique (SolidHi^TM), which helps achieve a uniform reflectivity during the inspection. The efficiency of the technique is demonstrated with the experimental results. This work is supported by NRC-IAR New Initiative Research Funding.     

Perceptual image preview

Image preview is a convenient way to browse large or multiple images on small displays. However, current signal-level image resampling algorithms may remove many features of interest in the preview image. In this paper, we propose perceptual image preview which retains more perceptual features such that users can inspect features of interest by viewing the preview image only and without zooming in. This technology has two components, structure enhancement and perceptual feature visualization. Structure enhancement enhances the image structure while suppressing subtle details using a gradient modulation method, thus making the succedent perceptual features more apparent. For perceptual feature visualization, features of interest detected in the picture is visualized on the structure enhanced preview image. We demonstrate with two examples of most commonly used image quality features, image blur and noise. The effectiveness of the proposed method is validated by experimental results.     

Detection of the boundary-layer separation point based on the flexible sensor arrays

Microelectromechanical systems plays an important role for studying gaseous or liquid fluid flows experimentally due to the dimensions of the device on the μm-scale. For aerodynamic applications, the detection of the location of the separation point of the boundary layer is very important, which is used for active flow control and smart skin adaptive control system. This paper presents the design and integration methods of a flexible polymide substrate micro thermal sensor array, sensor array driver circuit, dynamic data acquisition circuit, and criterion algorithm of the boundary layer separation point. The above algorithms were used to analyze the NACA0012 wind tunnel test data at flow conditions of the velocity 40 m/s and AOA 16°. The result demonstrated the above system can fulfill the dynamic and real-time of the adaptive control system.     

Applications of thick Sacrificial-Layer of zinc in LIGA process

Sacrificial-Layer technology is widely used in the microfabrication field of SLIGA. In this paper, thick sacrificial layers of zinc techniques are studied. A movable structure with aspect ratio of 25 has been fabricated by using zinc sacrificial layer through LIGA technique. Compared with conventional metal sacrificial layers of titanium or copper, the thickness of zinc layer can grow up to more than 10 μm. The adhesion of different seed layers and the physiognomy of sacrificial layer are also focused in this paper.     

基于MATLAB的神经模糊聚合釜控制

针对模糊控制表达能力强,但学习能力、适应性差的特点,提出在聚合釜温度控制过程中使用模糊控制与神经网络相结合的控制方案.给出了神经模糊网络结构和学习算法,仿真研究表明,神经模糊网络可以直接从经验中获取知识,自动建立模糊规则和隶属函数,无需查表,具有较强的适应和联想能力,比单纯的模糊控制具有更大的优势.     

基于DSP2812的静止无功补偿控制器

针对电力系统中无功补偿装置发展的现状,介绍了一种基于DSP2812的静止无功补偿装置控制器的设计.该控制器充分利用了DSP2812内部资源丰富的特点,即响应速度快、动态性能好,系统结构简单、可靠性高、成本低.试验结果表明,该控制器具有优异的性能,可用于低压系统的动态无功补偿.